ELE Times

Elektrobit, a global expert in automotive software, and Microchip Technology Inc., a broadline supplier of semiconductors committed to making innovative design easier through total system solutions, today announced a collaboration to simplify and accelerate AUTOSAR-based software development for automotive manufacturers and suppliers using Microchip automotive platforms.

As the automotive industry moves toward software-defined vehicles, OEMs and Tier 1 suppliers are under increasing pressure to shorten development cycles while maintaining safety, quality and flexibility. By combining Microchip’s automotive hardware platforms with Elektrobit’s AUTOSAR software, tooling and automation capabilities, the two companies are enabling customers to establish a production-ready software foundation faster and more efficiently.

The collaboration provides customers with access to pre-integrated AUTOSAR Microcontroller Abstraction Layer (MCAL) software based on Elektrobit’s market-proven EB tresos portfolio, helping development teams get started more quickly while reducing software integration complexity and project risk. The combined solution allows developers to focus more quickly on application development and vehicle differentiation rather than foundational software integration.

“By integrating Microchip automotive platforms with Elektrobit’s AUTOSAR expertise and tooling, we are helping customers streamline development and accelerate adoption of next-generation automotive architectures,” said Mark Reiten, Senior Corporate Vice President of Microchip’s licensing, memory products and intelligent compute business units. “Together, we are making it easier for developers to move from platform evaluation to production deployment.”

As part of the unified automation-first approach, Elektrobit applies integration-as-code to turn repeatable Classic AUTOSAR integration work into reusable, executable logic so teams can deliver faster and scale capacity through automation. This supports advanced workflow automation and lays the groundwork for future AI-assisted software configuration and development processes.

“Automotive manufacturers are seeking ways to accelerate software innovation while reducing development costs and complexity,” said Jaganathan Rajagopalan, Vice President, Head of Strategy and Portfolio Elektrobit. “Our collaboration with Microchip combines proven automotive software foundations with ready-to-use hardware support, enabling customers to bring software-defined vehicle innovations to market faster and with lower integration risk.”

The collaboration also expands support across a growing range of Microchip automotive platforms. Elektrobit has successfully enabled EB tresos AutoCore OS and EB tresos AutoCore Generic modules on Microchip’s SAMA5 series of microprocessors, making the solution available for immediate customer adoption, with support for additional platforms expected to follow as the collaboration evolves.

The companies plan to focus initial market activities on high-growth automotive segments in Asia, including the rapidly evolving two-wheeler market, while also supporting broader automotive opportunities across Asia and North America.

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Nuvoton Technology Corporation, a leading global semiconductor provider, proudly introduces the Nu-Link3-Pro, a ground breaking programmer and debugger specifically designed for embedded system developers. Nu-Link3-Pro is not only the world’s first programming debug analyser that integrates logic analysis functionality, but its powerful multi-protocol support and automated programming capabilities also deliver unparalleled development efficiency for product developers.

The core technology architecture of the Nu-Link3-Pro is based on USB Type-C Interface, significantly enhancing connection stability. For the NuMicro series microcontrollers, its ETM real-time instruction trace frequency is boosted to 110 MHz, allowing developers to capture system behaviour more accurately in high-frequency applications, thereby greatly optimizing real-time system debugging efficiency. Additionally, the Nu-Link3-Pro supports multiple voltage ranges, including 1.8V, 2.5V, 3.3V, and 5.0V, catering to various Hardware requirements.

The standout feature of the Nu-Link3-Pro is its built-in logic analysis function, enabling developers to perform digital signal capture and multi-protocol decoding without additional Hardware. This innovative design allows debugging, programming, and signal verification to be completed on a single platform, significantly reducing Hardware verification and debugging time.

In terms of programming capabilities, the Nu-Link3-Pro supports MicroPython, allowing developers to achieve fully customized automated offline programming logic by simply storing a custom MAIN.PY script on a Micro SD card. This flexibility adapts to different automated testing platforms or mass production environments, making the Nu-Link3-Pro a powerful tool for USB to multi-protocol bridging, offering diverse application scenarios and significant advantages.

For peripheral Interfaces, the Nu-Link3-Pro supports traditional SPI, I²C, UART, and RS-485 Interfaces, with added support for I3C and CAN FD Interfaces, meeting the big data frame development needs of automotive and industrial applications. These rich peripheral options make the Nu-Link3-Pro an essential tool for developers tackling complex embedded systems.

Additionally, as product security awareness continues to grow, Nu-Link3-Pro also offers a value-added Secure Provisioning service. The private key is generated within Nu-Link3-Pro and securely stored in a hardware Key Store, with a hardware encryption/decryption engine handling certificate issuance and encrypted programming to ensure the private key never leaves the device. These capabilities can be customized according to the target chip and application requirements, and Nuvoton can provide project assessment and technical support.

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The rapid expansion of AI and its multifaceted applications have opened myriad avenues, while posing critical challenges that must be addressed in real time. Anwesh Koley of ELE Times, caught up with Sanjay Wadhwa, Chairman & Managing Director, NASH Group of Companies, to decode the latest technological advancements and India’s role in this growth story.

ELE Times: Share your thoughts on the increasing role of Smart Vision technologies in quality inspection, predictive maintenance, and operational efficiency.

Sanjay Wadhwa: Smart Vision is becoming central to the next phase of industrial transformation because it addresses four priorities that every manufacturer is focused on today: consistency, repeatability, speed, and traceability. As production environments become more complex and volumes rise, manual inspection alone cannot deliver the level of repeatability and real-time assurance that modern manufacturing demands.

AI-enabled vision systems bring intelligence directly into the production process. They can analyse visual data in real time, identify defects, recognise deviations, and support faster corrective action. More importantly, they provide a highly repeatable method of inspection, reducing the dependence on individual inspector judgement, fatigue, or differences in interpretation. This creates a much more consistent quality standard across shifts, operators, and production lines.

At NASH, we see Smart Vision not simply as an inspection tool, but as a broader capability across quality, security, monitoring and process intelligence. The ability to capture and analyse the same parameters consistently over time also creates a valuable digital record that can be used for traceability and continuous improvement.

Its value also extends beyond defect detection. When visual data is integrated with machine, process, and enterprise systems, it helps leadership identify weak points, understand recurring issues, and improve decision-making. In predictive maintenance, continuous monitoring can reveal early signs of deterioration, enabling teams to intervene before failures affect productivity.

The larger opportunity is to convert visual information into repeatable, reliable, and actionable intelligence. That is where Smart Vision will play a defining role – not only in improving operational efficiency but also in strengthening quality systems enabling predictive intervention, reducing process variation and ultimately building more efficient and resilient manufacturing operations.

ELE Times: How are Indian engineering and manufacturing companies building globally competitive Industry 4.0 solutions?

SW: Indian engineering and manufacturing companies are steadily moving from being execution-led partners to becoming creators of globally relevant industrial technology. This shift is being driven by strong engineering talent, deeper manufacturing experience, and growing collaboration with global technology ecosystems. The real transition is from “Make in India” to “Engineer in India, Innovate in India & Scale for the World.”

At NASH, our focus has been to build capability across the full product lifecycle — from design and engineering to embedded systems, Artificial Intelligence of Things (AIoT), Smart Vision, industrial computing and advanced manufacturing. This end-to-end capability allows us to develop solutions that are relevant to Indian operating conditions while meeting global expectations on quality, reliability, repeatability and scalability.

A strong example is the development of India’s first Industrial Motherboard, which reflects how Indian engineering strengths can translate into specialised platforms for industrial applications. Alongside this, our vertically integrated Design-to-Manufacturing approach enables us to customise solutions for specific customer needs without compromising robustness, repeatability, or scale.

For Indian companies, the opportunity is much larger than manufacturing for the world. India has the engineering depth, industrial base and entrepreneurial mindset to design, develop and manufacture competitive technology solutions for global markets. The ability to combine cost-effective engineering with global standards, interoperability, and long-term lifecycle support can become an important competitive advantage.

The next phase will depend on how effectively organisations combine engineering excellence, technology innovation, manufacturing discipline, and customer understanding. Equally important will be the ability to build solutions that are repeatable, scalable, secure, and capable of integrating with global industrial ecosystems. Companies that bring these together will be well positioned to shape Industry 4.0 solutions not only for India, but for global industry.

ELE Times: What are the existing opportunities and key challenges for manufacturers adopting AI-led industrial technologies at scale?

SW: The opportunity for AI-led industrial technologies is significant because manufacturers are under pressure to improve uptime, quality, productivity, and planning accuracy at the same time. AI can help organisations make sense of large volumes of operational data and convert that data into faster, more informed decisions. Predictive maintenance can shift teams from reacting to breakdowns to preventing them. AI-led vision systems can improve inspection consistency, and intelligent planning tools can support capacity decisions during new program ramp-ups.

However, scaling AI is not only a technology deployment exercise. The real challenge is moving from successful pilots to solutions that can be deployed repeatedly and economically across machines, lines, and plants. The challenge lies in the foundation. Data must be reliable, standardised and available at the right level of detail. Machines must be connected, processes must be consistent, and teams must have the ability to interpret insights and act on them quickly.

Cybersecurity is another critical consideration. As operational technology, enterprise systems and external ecosystems become increasingly connected, manufacturers must ensure that digital transformation does not compromise operational resilience or data security.

Therefore, the core challenge is not access to AI tools. It is building the digital, organisational, and engineering maturity required to move from pilot to scale and implement AI sustainably, securely and with measurable business value.

ELE Times: How do you foresee the future of intelligent factories? What are the technologies that will define the next decade of industrial innovation?

SW: The intelligent factory of the future will be connected, adaptive, and increasingly self-aware. We will see AIoT, Smart Vision, edge computing, advanced automation, and intelligent analytics working together as one integrated operating layer rather than isolated technology investments.

AIoT will provide real-time visibility across machines, utilities and production lines. Smart Vision will support autonomous quality inspection, safety monitoring, and process intelligence. Edge computing will enable faster decisions at the point where data is generated, while Digital Twins will enable manufacturers to simulate and optimise processes, capacity and changes before implementing them in the physical factory. AI will help manufacturers move from monitoring operations to predicting, optimising and continuously improving them.

The next evolution will be from connected factories to learning and increasingly autonomous factories. Digital twins and simulation will allow manufacturers to test changes before implementing them, while AI will increasingly move from providing insights to recommending and, within defined boundaries, executing decisions. The ability of different systems to communicate and learn from each other will be critical to achieving this at scale.

ELE Times: How is the “Make in India” initiative progressing and what role is NASH playing in this initiative?

SW: The Make in India initiative has evolved from a manufacturing-led movement into a larger national capability-building programme, and NASH is proud to be contributing to that transition. For us, Make in India is not only about assembling products locally; it is about creating end-to-end ownership across design, engineering, embedded electronics, software, testing, validation and manufacturing.

This is where NASH has become a preferred partner for customers who are looking for long-term value, reliability, and technology depth. We continue to evolve with industry requirements and have built strong capabilities to support sectors that demand high-quality, mission-critical hardware. A significant example is the movement of complete banking hardware programmes from China to India, where NASH has enabled local design-to-manufacture execution with the required scale, quality and responsiveness.

Our strength lies in combining product engineering, embedded systems, AIoT, industrial computing, smart vision, and advanced manufacturing under one integrated ecosystem. With a strong R&D foundation and over 180+ engineers, we are able to innovate faster, customise solutions for Indian and global customers, and add value at every stage of the product life cycle.

As the country builds deeper capabilities in electronics, industrial technology, EV, renewable energy and automation, NASH is focused on strengthening indigenous technology creation rather than depending on external ecosystems. Our work on industrial motherboards, LFP battery technology, and embedded platforms reflects this commitment. We believe the next phase of Make in India will be driven by companies that can design in India, manufacture in India and deliver globally competitive products. NASH is aligned to that vision, and we see our role as enabling customers to localise critical technologies while maintaining global standards of performance, quality and innovation.

ELE Times: Brief us about the current state of Lithium-ion technology for EVs in India and how NASH Energy is poised to deliver the required technology.

SW: Lithium-ion technology today is at an important inflection point in India’s EV journey. While the initial phase of adoption was largely driven by battery availability and cost, the market is now maturing towards a stronger focus on safety, reliability, lifecycle performance, thermal stability and localisation of critical materials & technologies. For Indian operating conditions, especially in mobility and energy storage applications, LFP chemistry is gaining strong relevance because of its inherent safety, durability and suitability for long-term use. At the same time, as applications evolve, there is a need to remain chemistry-agnostic and continuously evaluate other cell chemistries that can serve specific performance, energy density and use-case requirements.

At NASH Energy, our focus is to build deep technology and manufacturing capabilities across the battery value chain. We are working on LFP cell manufacturing along with advanced battery packs, battery management systems, power electronics and integrated energy storage solutions, while also exploring adjacent chemistries that may be relevant for future mobility and energy storage applications. This approach allows us to address not just the cell requirement, but the complete technology stack needed for reliable and scalable EV and industrial applications.

India has a significant opportunity to move beyond battery assembly and establish itself as a strong hub for cell technology, engineering and manufacturing. NASH Energy is well-positioned to contribute to this transition by combining technology expertise, manufacturing capability and application-level understanding to deliver safe, dependable and Made-in-India energy solutions for the evolving EV ecosystem.

ELE Times: How are AIoT, edge computing and intelligent automation reshaping modern manufacturing techniques?

SW: The convergence of the Artificial Intelligence of Things (AIoT), edge computing, and intelligent automation is transforming manufacturing from rigid, centrally scheduled assembly lines into adaptive, decentralised, and autonomous production ecosystems. This is possible by moving intelligence closer to machines, processes, and operators, instead of delayed action/analysis.

Primary decision drivers for the transformations on the Factory Floor:

• Ultra-Low Latency Quality Control
• Condition-Based & Prescriptive Maintenance
• Dynamic Shop-Floor Orchestration
• Live Digital Twins & Closed-Loop Tuning
• Adaptive Resource & Energy Optimisation

At NASH, we see these technologies as the foundation of smarter, more responsive industrial ecosystems. By integrating embedded electronics, industrial computing, AI and software, our solutions support applications such as predictive maintenance, process monitoring, machine vision, OEE analysis and industrial automation. These capabilities also help similar manufacturers reduce downtime, improve quality consistency, enhance traceability, and optimise resource utilisation.

For us, the value of AIoT and automation lies in solving real operational challenges, not in adopting technology for its own sake. When implemented with the right hardware, software and manufacturing expertise, these technologies enable faster decision-making, higher reliability, and more flexible production. This is how modern manufacturing is evolving from reactive operations to intelligent, data-driven and future-ready factories.

The post Interview | Sanjay Wadhwa, Chairman & Managing Director, NASH Group of Companies appeared first on ELE Times.

DigiKey, the global distribution leader of electronic components and automation products, today announced a $100 million investment over the next 18 months to expand its global product distribution centre in Thief River Falls, Minn., and its order management system. This escalation of capacity and automation is designed to support sustained growth in demand for its industry-leading same-day fulfilment model.

Expansion at record-growth pace

The investment funds the next phases of DigiKey’s product distribution centre (PDC) expansion, adding high-volume picking capacity and advanced automated packing operations. The first new automated packing system is already on site and will be in use this year, with additional multi-line packing automation phased in as part of the investment program.

The announcement follows a year of extraordinary growth for the company. DigiKey is on pace to grow more than 50 percent in 2026, and projects continued double-digit growth in the year ahead.

“Our customers choose DigiKey because they know we’re fast and they know we’ll be fast tomorrow,” said DigiKey CEO Dave Doherty. “A $100 million commitment says exactly that: we’re expanding capacity and automating where it counts to keep same-day fulfillment the standard, not the exception.”

Building for future scale

The expansion continues the design philosophy behind the company’s purpose-built product distribution centre: building in capacity ahead of demand. DigiKey’s state-of-the-art, 2.2 million-square-foot product distribution centre expansion project opened in Aug. 2022 and was designed for scalable growth, with many open spaces and much of the fourth floor left empty for future expansion.

New picking bays, order retrieval automation and expanded packing operations will process a growing share of order volume across multiple product form factors with higher speed and efficiency. Every investment in DigiKey is focused on the ultimate goal of sustainable growth.

“Simplification creates capacity, and capacity creates growth,” said Doherty. “We’re redirecting resources from legacy work to future capabilities and investing in where we’re going, not just where we’ve been.”

The investment reinforces DigiKey’s position as a global leader in electronic component distribution, serving one million customers annually with more than 1.9 million products in stock and ready to ship the same day from the product distribution centre.

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Two complementary market footprints have teamed up to make battery and charging infrastructure more intelligent for EV fleets across India. Datakrew and Gaadin Technologies have signed a Memorandum of Understanding (MoU) to collaborate on integrated EV fleet, battery and charging solutions for customers across their respective markets. The collaboration will combine Datakrew’s EV battery and vehicle intelligence with GaadinAI’s EV charging infrastructure management platform.

The strategy intends to create a more connected operating view for fleet operators, charge point operators and mobility businesses across Asia Pacific, the Middle East, Africa and other markets. Under the collaboration, each company will be able to bring the other’s capabilities into relevant customer opportunities, while also working together where customers require a more integrated view across the vehicle, battery and charging infrastructure.

Connecting the Vehicle and the Charger

As electric fleets scale, vehicle operations and charging operations are increasingly becoming part of the same operating system. Yet in many fleets today, vehicle and battery data sits in one platform while charger, session and energy data sits in another. Operators can see what happened on either side, but often lack the context needed to understand how the two are affecting each other.

That separation matters because charging behaviour influences battery ageing, while battery condition influences vehicle availability, safety, performance and long-term asset economics. Looking at both sides together creates a stronger basis for operational decisions than treating the vehicle and the charging network as independent systems.

The MoU brings together Datakrew’s capabilities in EV battery intelligence, IoT-enabled vehicle data acquisition and AI-driven fleet analytics with GaadinAI’s EV charging management and energy management capabilities. The shared objective is to give customers a more complete operating picture: how a vehicle is being used, how its battery is behaving at cell and pack level, how it is being charged, and how those factors interact over time.

Commenting on the collaboration, Dr. Sumanta Bose, Founder and CEO of Datakrew Pte. Ltd., said, “The vehicle and the charger are often managed as separate systems, but for an EV fleet they are part of the same operating reality. Battery condition is shaped by how the vehicle is driven and how it is charged, while charging decisions are more useful when they are informed by what is happening inside the battery. GaadinAI brings strong EV charging management and energy optimisation with additional market reach, while Datakrew brings the vehicle and battery intelligence layer. Bringing those two views together creates something much more useful for operators than either view in isolation.”

Understanding the Scope of the Collaboration

Under the MoU, Datakrew and Gaadin intend to combine their respective capabilities into integrated offerings spanning EV fleet operations, battery intelligence and EV charging and energy management.

Datakrew will contribute its EV battery and vehicle intelligence capabilities, including state of charge and state of health monitoring, cell-level voltage, current and temperature behaviour, thermal and pack anomaly detection, analysis of the effect of driving patterns and duty cycles on battery condition, and broader vehicle and fleet analytics. These capabilities are delivered through Datakrew’s OXRED platform, with vehicle data acquired through its ITUS Max device family and related integrations.

GaadinAI will contribute its EV charging management and energy management capabilities, including charge point operations across multiple charger vendors, session and energy management, charging network monitoring, and the operational and deployment experience it has built across its markets.

The two companies also intend to make relevant platform interfaces available for integration. This ensures that vehicle-side and charge-side information can be presented to customers as a connected operating view rather than as separate datasets that need to be reconciled manually. Where appropriate, both sides will also support customer engagements with technical and commercial resources from their respective teams.

Commenting on the collaboration, Sanjeev Tripathi, Founder and CEO, Gaadin, said, “As EV fleets scale, the constraint is no longer only charger availability. It is how intelligently those chargers are operated, how energy is managed, and how charging behaviour affects battery life. This collaboration is designed to address that full stack.”

Datakrew and Gaadin: Moving From Deployment to Operations

The EV industry is entering a different phase of maturity. For much of the past decade, the immediate challenge was deployment: procuring vehicles, installing chargers and putting the necessary infrastructure in place. As fleets become larger and more established, the focus is shifting toward operating those assets efficiently over many years.

That shift changes the role of data. During deployment, vehicle systems and charging systems could be managed separately because they were often solving different immediate problems. During long-term operation, however, many of the questions converge. Fleet uptime, charging strategy, battery life, energy cost, maintenance planning and residual value are closely connected, and decisions in one area increasingly influence outcomes in another.

This creates a natural role for collaboration between specialised technology providers. Battery intelligence and charging infrastructure management are both technically deep disciplines, and customers benefit when strong platforms in each area can work together without requiring one provider to replicate the other’s technology. Open interfaces and practical interoperability allow operators to retain flexibility while gaining a more complete view of their electric assets.

The Datakrew–Gaadin collaboration is built around that principle. Each company will continue to focus on its own area of expertise, while making it easier for customers to connect the vehicle, battery and charging layers where there is a clear operational benefit.

What it Means for the Customers

For customers, the value of the collaboration is expected to be operational rather than simply technical. Instead of managing fleet and charging information as separate exports or dashboards, operators can work toward a connected view in which charging sessions, vehicles, batteries and duty cycles can be interpreted together.

That creates the potential for charging decisions to be informed by battery condition rather than by energy demand alone. Over time, operators may be able to make more deliberate decisions about which vehicles are charged, how frequently they are charged, how deeply they are charged and how charging behaviour should vary based on battery condition, route requirements and fleet availability.

The combined view can also help with fault isolation. An unusual charging event can originate from the charger, the vehicle, the battery or the interaction between them. When information from both sides is available together, engineering and operations teams have more context to identify the likely source of a problem and respond appropriately.

The partnership intends to reduce the integration burden on customers. Operators already working with either company may be able to access complementary capability through an existing technology relationship rather than independently sourcing systems and then commissioning a separate integration between them.

Importantly, the collaboration does not require either company to become a full-stack provider. Both platforms remain distinct, giving customers the ability to combine specialised capabilities while retaining flexibility across mixed vehicle fleets and multi-vendor charging environments.

From Complementary Capabilities to a Joint Offering

Datakrew has focused on what happens in the vehicle and inside the battery, while GaadinAI has focused on what happens across the charging network. The areas are closely connected in day-to-day EV operations, but they require different technical capabilities and operating experience. By working together, Datakrew can bring battery and vehicle intelligence into charging-led customer conversations and into markets where Gaadin already has operational presence and relationships.

Gaadin, in turn, can extend the depth of its charging proposition by helping customers understand the condition and behaviour of the vehicle and battery at the other end of the charging session. The commercial model is intended to remain practical. Each company can introduce the other into relevant customer opportunities, and both can pursue joint opportunities where a customer has a clear need spanning the vehicle, battery and charging layers.

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The working principle of an electric vehicle (EV) relies on electrical energy stored in a large battery pack. An electric motor converts this electrical energy into mechanical energy, delivering power to the drivetrain to rotate the wheels and create motion. The exterior of an electric vehicle looks very similar to that of a car running on fuel but the powertrain installed inside an electric vehicle works very differently compared to the combustion engine in a fuel-powered car.

A major difference between a fuel-car and an electric vehicle is that an EV can generate power while applying brakes or slowing down, converting kinetic energy into electrical energy; a fuel-powered car does not support this method.

The complete working mechanism of an EV involves coordination between several systems: the battery pack, battery management system (BMS), power electronics, inverter, electric motor, transmission, thermal-management system and vehicle control system. These systems communicate with each other to control the flow of electricity from the battery to the wheels and recover energy when the vehicle slows down.

Battery Pack Stores Electrical Energy

It is the primary energy storage device containing thousands of individual battery cells arranged into modules and assembled into a large battery pack. It uses lithium-ion battery technology because of its small size, lightweight, high energy density, and low self-discharge rate. The battery stores energy in chemical form and releases it as electricity when the vehicle requires it.

The Battery-Management System Controls the Battery

The Battery Management System is responsible for monitoring and controlling the battery pack. Its monitoring parameters include state of charge (SoC) and State of Health (SoH), which helps the BMS monitor, protect, and optimize the battery pack. To perform its functionalities, it uses a combination of hardware and software components such as cell monitoring ICs, cell balancing circuits, stack communication, and CAN bus.

Power Electronics Control the Flow of Electricity

Power electronics perform switching operations by rapidly switching high-power solid-state semiconductor devices (MOSFETs, IGBTs, and thyristors) to control the flow of electric current. This method can reduce energy losses in the form of heat compared with traditional approaches that rely on resistors before advanced semiconductor switching.

Other core functionalities of power electronics include pulse width modulation to control the voltage or power delivered to the load, energy storage and filtering to manage high-power flow between the battery, electric motor, and accessories, and feedback control loops that adjust the switching frequency or duty cycle in real time to maintain stable power delivery and prevent fluctuations.

The Inverter Converts Battery Power for the Motor

The energy transferred from battery to electric motor is via inverter. It is an important link between a battery and motor that converts DC current from the battery into AC current for running the traction motor.

When the driver presses the accelerator, the vehicle’s control system determines the required power for the demanding acceleration. The inverter then converts DC current from the battery into a controlled AC waveform for the motor. When the vehicle is accelerating, the flow of energy from the battery to the wheels is shown in the given flow chart.

Battery ➔ Inverter ➔ Electric Motor➔ Transmission ➔ Wheels

The Electric Motor Converts Electricity into Motion

The conversion of chemical energy stored in a battery to mechanical energy via the inverter takes place in three phases and it also controls the frequency and voltage of the produced output alternating current (AC). The inverter uses high speed electronic switches (such as MOSFETs or IGBTs) to perform pulse width modulation (PWM). The technique shapes the electricity into AC cycles.

The produced AC cycles are then divided into three separate AC currents sent to the motor’s windings in a carefully timed sequence via the inverter. By adjusting the frequency of the AC power, the inverter dynamically controls the motor’s speed. By adjusting the voltage/amplitude, it changes the motor’s power and torque.

Transmission Transfers Torque to the Wheels

Once the rotational power is produced by an inverter, the drivetrain transfers that power to wheels by converting electrical energy into mechanical rotation. Electric motors spin at a very high RPM (revolutions per minute). Most EVs use a single-speed gearbox to drop this RPM to a lower, safer speed while multiplying the torque force. The resultant force is passed to a physical part, known as a differential, which allows the wheels on the same axle to rotate at different speeds while turning and the same speed to both wheels when driving in a straight line.

The Wheels Convert Motor Torque into Vehicle Motion

The last step is to convert the motor drive torque into work. The reduction gear and differential send the torque to the wheels. Force is created from the relationship between the rotating tires and the ground surface, which causes the vehicle to move. The vehicle’s control system constantly provides data such as wheel speed, accelerator position, steering angle and vehicle speed. This data can be used to control motor torque to achieve the best movement while driving an electric vehicle. These features make modern electric vehicles more advanced not only in terms of battery size but also in controlling torque because of integrated software, sensors, power electronics, and mechanical components that work together.

Regenerative Braking Recovers Energy

As already discussed at the beginning of this article, electric vehicles generate power while applying brakes or slowing down the vehicle. This feature is not available in a fuel-powered car. The amount of energy generated while applying brakes depends on the vehicle’s kinetic energy, which is then converted into electrical energy and stored as chemical energy in the battery.

Given below is the flow chart describing the flow of energy generated from applying brakes to storing energy in the battery:

Wheels ➔ Electric ➔ Motor ➔ Inverter ➔ Battery

The inverter transforms the created power into a form compatible with the battery, and the BMS decides whether the converted energy is accepted and how much is accepted.

Thermal Management Keeps the System Within Safe Temperatures

Temperature of vehicle components plays a crucial role in the proper operation of overall vehicle performance. The temperature of the electric motor should be maintained between 90°C and 95°C. This is the ideal temperature range for an electric vehicle to perform operations. Temperature above 100°C represents the risk state, which can cause battery damage or catch fire.

To avoid these major problems, vehicles come with installed thermal management systems which use liquid cooling and heating circuits to maintain an optimum temperature. Not only high temperatures, but also extremely low temperatures can affect battery and efficiency of an electric vehicle. The vehicle depends on onboard thermal management systems, active battery heaters, and specialised cabin-warming tech to deal with extremely cold weather conditions.

Conclusion

The operation of the electric vehicle depends on a precise sequence of energy conversions. Electrical energy stored inside the battery is regulated by the BMS; conversion and transfer of energy to the traction motor is through power electronics. An electric motor produces torque and transfers it to wheels through a reduction gearbox and differential. The primary purpose of the gearbox and differential is to turn high-speed motor power into smooth and controlled motion at the wheels.

At the same time, many operations are performed by an electric vehicle along with the working state of an engine such as thermal management for maintaining the suitable temperature, software platforms to control powertrain by coordinating with electronic control units (ECUs), power electronics, and BMS. An electric vehicle replaces a combustion engine with tightly integrated electronics, electrochemical, and propulsion systems.

The post How an Electric Vehicle Works: From Battery Packs to Wheels appeared first on ELE Times.

From rule-based workflows to AI-generated content and autonomous decision-making, businesses are entering a new era of intelligent automation.

The Next Stage of Enterprise Automation

Artificial Intelligence Companies are reinventing how they use products, data, and the enterprise. Automation has been a real workload saver for businesses for years and has played a role by automating rule-based manual and rule-based workflows. Then Generative AI arrived with its game-changing capability of content generation- text, images, code, technical documentation. Now we have Agentic AI, which, in addition to generating content, can plan actions, leverage digital assistants, and complete multi-step workflows with very little human guidance.

These three types of technology, Agentic AI, Generative AI, and Traditional Automation, are important for any enterprise running pilots or already investing in digital transformation. They may seem similar but will likely solve different pain points and require different deployment strategies.

Computer scientist and Coursera founder Andrew Ng has said that it’s ‘the new electricity’ because, as widely touted as the technology is, AI is going to change everything, ‘not one thing at a time’. From helping (creating content) to doing (acting), AI and automation are leading a fourth industrial revolution in manufacturing, medicine, finance, security, and IT.

What Is Traditional Automation?

Traditional automation automates instructions, rules, scripts, and flows, automating work that repeats with little human intervention. Examples include: Robotic Process Automation (RPA), batch system jobs, manufacturing controllers, enterprise business rules-based software. These systems work effectively if you know the input conditions, the environment, and the desired output, and you know the relationship between all three. For example, a manufacturing system that controls the plant to follow a control algorithm; a monitoring and alarm system where the alarm sounds when the temperature rises above a specified level; or an ERP system that automatically creates an invoice when the order fulfils certain conditions.

The primary advantages of traditional automation include reliability, stability, speed, and cost-efficiency for repetitive predictable operations. However, conventional and rule-based automation can lack robustness when data is partial, when instructions need to change, or when an unanticipated situation isn’t covered by previous rules. This doesn’t render traditional automation inaccessible, though. There are still uses for conventional automation in situations requiring deterministic behaviour, accurate timing, and consistent reactions—safety-sensitive industrial machinery still needs designed controller hardware. There are still uses for conventional automation in situations where you need deterministic behaviour, accurate timing, and consistent reactions- safety-sensitive industrial machinery still needs designed controller hardware.

What Is Generative AI?

The outputs of generative AI models are created based on the data and format you input into the model. Copywriting, summarisation, coding, translation, and visual design are all use cases for Large Language Models (LLMs), image generation models, and multimodal AI. Unlike classic automation, which relies on structured data, generative AI understands natural language input and can be used by marketers for writing campaigns, software developers for code snippets and summarising technical documentation, and product teams to create ideas for new product designs.

Enterprises are applying Generative AI in their automation use cases. These use cases include customer service, managing the knowledge base, research, and software engineering. Generative AI is useful for providing employees easier ways to search extensive document repositories and to translate complex technical content into understandable content.

However, the technology is imperfect. Models may generate incorrect statements, invented references, or misinterpret instructions. Their results can require review by a human subject matter expert- particularly in technical, financial, legal, and safety- critical contexts. Moreover, generative AI doesn’t just generate content; it lacks the power or capability to actually perform the action. A model might prepare a maintenance report, but it can’t change an asset-management system or make an engineering assignment on its own.

What Is Agentic AI?

An agentic AI can goal-seek (when given instructions), reason out (by planning and executing steps to accomplish a goal), use tooling (e.g., APIs and tools), get feedback, and alter behaviour if the context changes. We see agentic AI as being powered by an LLM (or, in our focus case, a Generative AI) for reasoning and language tasks, while utilising APIs, enterprise data, memory, rules, etc. Agentic AI systems are taught by enterprise chat to explore how they can use generative models for input interpretation, problem solving, and action planning.

Think of a particular IT support function. A classic automation would trigger an alert whenever a server fails. A Generative AI assistant may review the alert and suggest a short list of the most probable root causes. An Agentic AI system that has been configured correctly would gather diagnostic information, review peer-approved troubleshooting instructions, recommend actions, and carry out peer-approved remediation.

Agentic capabilities also enable coordination of multi-agent AI. A retrieval agent might surface results, while one or more subsequent specialised agents carry out processing, and yet other sketches out an execution plan. Multi-agent support might facilitate a variety of advanced workflows, but supporting such collaboration introduces additional problems-especially in coordination, monitoring, permission, and accountability. Agentic skills do not mean that humans lose control.

Agentic AI vs Generative AI vs Traditional Automation: Understanding the Differences

The biggest contrast between Agentic AI, Generative AI, and Traditional Automation is how each approaches a given task. The traditional Automation approach is to follow a set process. This is most effective for repetitive tasks where the data inputs and outputs are known—examples include AP automation, predefined reporting, and assembly line control.

Generative AI generates text or other output from a prompt. It is helpful for writing documents, summarising, generating code, and knowledge work. Agentic AI combines reasoning, planning, and use of tools to achieve a long-term goal via a series of steps. It can gather information, decide between alternatives, connect to multiple interacting applications, and determine when a goal has been reached.

For instance, with traditional automation, the ticket would be assigned based on parameters, and with Generative AI, it could be summarised and potentially even responded to. Generative AI could even be used by an agent to create a draft resolution. An Agentic AI could retrieve the customer order history, compare that to the refund policy, generate a suggested outcome, and activate a supported process step. And it’s not always a case of choosing just one: all three can be used together in the same automation.

Real-World Applications Across Industries Manufacturing and Industrial Operations

At the manufacturing stage, Traditional Automation sets the pace for manufacturing, conveyor planning, and repetitive assembly. Generative AI may help engineers when they read logs, write manuals, or guide troubleshooting of failed equipment. Agentic AI can support authorised workflows: Collect sensor data, generate a service ticket, check availability of spare parts, and coordinate dispatching a technician. In the industrial use case, keep the AI agents as close as possible to the traditional control system: Request safety interlocks and high-impact actions only after engineering control approval has been obtained.

Healthcare and Financial Services

In healthcare, Generative AI in the healthcare could generate summarise of clinical documentation, and help with administrative correspondence. Agentic AI might lead scheduling, obtaining approved records, and similar tasks. Clinical decision-making would require appropriate professional governance.

In Financial Services, traditional automation handled transactional processing, whereas Generative AI can create summaries of financial documents and even assist with customer engagement. Agent AI can help with fraud investigation by gathering evidence and escalating concerns. Payments and changes to accounts should have controls and audit trails.

Cybersecurity and IT Operations

Typical security automation generates alerts by configured indicators. Agentic AI may help security analytics by summarising incidents, investigation reports and producing plain language explanations of technical results. Agentic AI for cyber security may gather approved data sources, prioritise alerts, collect evidence, and carry out approved actions.

However, with greater access comes greater risk. A badly trained agent could alter data, disclose sensitive details, or make a mistake and take the wrong action. This is why organisations need least-privilege access, observability into actions and decision points, and the ability to pause and revert those decisions.

Challenges and the Road Ahead

Digital automation processes are different. You are switching from high-volume test automation to a completely new process, and there are a few factors to consider before you decide. Data quality, integration cost, human resilience, security, and performance. Conventional automation is reliable but less flexible. Generative AI is flexible but unpredictable. Agentic AI is flexible but unpredictable. Agentic AI is more powerful operationally but comes with new, more complex risks, as the system can query applications.

Microsoft CEO Satya Nadella has been talking about an emerging agentic world- where AI agents operate in the jobs of humans, teams, and enterprises. This vision’s next step is to utilise enterprise systems to orchestrate work between teams and individuals and not simply in response to the work they submit. Pragmatic advice: automate the routine work that is deterministic; Generative AI for work that involves language and content generation; and Agentic AI for case orchestration that is multi-step, multi-function, multi-stakeholder, multi-outcome where the complexity merits more nuance. Minimise human oversight as the scale of potential consequences of an action increases.

Conclusion

Looking at Agentic AI vs Generative AI vs Traditional Automation, we see three flexible dimensions for enterprise tech: Traditional automation provides predictable execution, Generative AI allows machines to produce and consume new data, and Agentic AI gives machines the ability to orchestrate a series of actions to achieve an explicit goal.

Hence, for businesses, the goal shouldn’t be to replace each process with the latest AI, but rather to choose the right capability for each use case, artfully connect the systems, and retain the right level of human oversight. An enterprise that leverages both deterministic automation and Generative AI with well-governed AI agents will be more agile, productive, and responsible.

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STMicroelectronics has highlighted its latest miniature 6-axis motion sensors designed to deliver accurate motion tracking and context-aware sensing for smart devices, drones and other electronic applications. Developed under the company’s Sustainable Technology framework, these sensors combine enhanced precision with compliance with stringent sustainable-materials requirements.

The new sensors combine six-axis motion sensing technology to allow electronic devices to identify and measure motion A 6-axis motion sensor generally includes both a three-axis accelerometer to sense linear acceleration and a three-axis gyroscope that measures the rate of rotation. These measurements combine to help devices recognize changes in position, orientation and movement.

“By maximizing the signal strength and resilience in an extremely compact form factor, the architecture of our latest 6-axis motion sensors will drive powerful new features in smart applications,” said Simone Ferri, Executive Vice President, MEMS Sub-Group, STMicroelectronics. “Moreover, meeting today’s leading sustainable-materials codes such as BPA-free, Pb-free and others helps smart-tech innovators address environmental and regulatory expectations as these become progressively tougher, encompassing additional classes of materials.”

In smart devices, this sensing ability can provide motion sensing, activity tracking, tilt and gesture-based control. In drones, the clear motion data can enable flight control systems to detect movement, track tilt and respond to tilt changes. The sensors can also enable other applications that require accurate motion sensing and event reconstruction.

“ST’s new LSM6DSK320X sensor delivers unprecedented vibration immunity that we just had to integrate and take advantage of immediately in our ongoing reference platform development,” said Jay Blackman, Leader, Betaflight.

“The LSM6DSK320X IMU is the new performance leader for smart and context-aware consumer technology, including UAVs,” said Dr. Zebo Zhou, Chief Technology Officer, Tianhe (Chengdu). “Drone flight controllers place extremely demanding requirements on an IMU: the sensor must not saturate during aggressive maneuvers, must maintain low noise under severe vibrations, and must be compact and power efficient. Of all the available devices we have evaluated, ST’s LSM6DSK320X was the best match.”

Technical Specifications

• The super-symmetric architecture comprises two mirrored electromechanical structures, acting in counter phase to each other, thereby cancelling the effects of external vibrations and shocks. Combined with a fully differential signal chain, this structure maximizes detected-signal strength and resilience.
• The internal structure is also extremely compact, enabling each IMU to contain one gyroscope with two separate accelerometers in a single package. The dual accelerometers allow simultaneous measurement of low-range disturbances up to ±16 g and high-g events up to ±320 g, ensuring these IMUs never miss an event. This contrasts with single-accelerometer IMUs that cannot perform measurements while switching between low-range and high-range modes.
• The consumer-oriented LSM6DSK320X and industrial-grade ISM6HGK256X also contain ST’s machine-learning core (MLC) that embeds AI processing directly in the sensor to reduce the application’s energy consumption and save system resources.
• Developers can leverage extra libraries including ST’s Motion XLF, and sensor-fusion and context awareness software, in addition to basic libraries, that enhance power and flexibility to address diverse applications.
• The new IMUs are supported within the ST MEMS Studio development environment. The STEVAL-MKI257A adapter board for the LSM6DSK320X and STEVAL-MKI258KA adapter board for the ISM6HGK256X will be available in the eSTore and from distributors by November 2026.
• The LSM6DSK320X for consumer applications operates over a broad temperature range, from -40°C to +85°C. The industrial-grade ISM6HGK256X handles temperatures from -40°C to +105°C and is included in ST’s 10-year longevity program that assures long-term component availability.
• The IMUs come in 2.5 mm x 3 mm x 0.83 mm packages and available from $4.6 for the consumer-oriented LSM6DSK320X and $5.1 for the industrial-grade ISM6HGK256X for orders of 1000 pieces.

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Nuvoton Technology introduces the NSP2340BA24E Smart Buzzer, integrating voice capabilities and buzzer driving functions into a single IC. Designed to extend the capabilities of conventional piezo buzzers beyond simple beep sounds, the NSP2340BA24E enables direct playback of voice and sound effects, bringing more intuitive and distinctive audio interaction to home appliances, security systems, healthcare devices, industrial equipment, and other smart applications.

Conventional buzzers have long been widely used in electronic products for audible alerts and operation feedback due to their simple architecture and proven reliability. However, as smart devices become increasingly sophisticated, simple beep patterns alone may not provide the intuitive and informative user experience required by modern applications.

Built around the concept of “Beyond the Beep,” the NSP2340BA24E expands the role of the conventional buzzer by enabling richer and more expressive audio feedback.

From Simple Beeps to Voice & Melody

The NSP2340BA24E Smart Buzzer integrates embedded Flash and a buzzer driver, together with a voltage booster and Class-AB amplifier, enabling direct drive of a piezo buzzer for voice and sound-effect playback. This transforms the buzzer from a simple audible alert component into a more informative audio interface.

With voice and diverse audio content, products can communicate operating status, notifications, and warning messages more clearly. Instead of relying solely on different beep patterns to indicate different conditions, the NSP2340BA24E enables more recognizable voice or sound feedback, helping users understand device status more intuitively. This approach allows developers to build upon familiar piezo buzzer designs while creating greater product differentiation and a more engaging audio experience.

Highly Integrated Design for Easier Implementation

The NSP2340BA24E integrates audio content storage, voltage boosting, and buzzer driving capabilities into a highly integrated solution. The NSP2340BA24E supports booster output of up to 24V, along with UART, I²C, and Two-Wire interfaces for host MCU control. It also supports In-System Programming (ISP), allowing audio content to be updated through the host MCU.

With this integrated architecture, developers can more easily introduce Voice & Melody capabilities into products traditionally designed around buzzers, while maintaining flexibility to customize audio content for different products and application requirements.

Expanding the Value of Buzzers Across Smart Applications

The NSP2340BA24E can be applied across a broad range of products requiring audible feedback and status notification. From operation and completion notifications in home appliances to warning alerts in security systems and status feedback in industrial equipment, the NSP2340BA24E allows sound to do more than simply attract attention—it can help users better understand what a device is communicating.

With the NSP2340BA24E Smart Buzzer , Nuvoton extends the conventional piezo buzzer beyond simple beep sounds to richer Voice & Melody experiences, providing developers with greater flexibility in audio design and enabling more intuitive audio interaction for the next generation of smart devices.

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Infineon Technologies AG introduces the XDP8HD40S-1, a high-voltage hot-swap controller designed for digital power path protection and monitoring in next-generation AI server power architectures operating at ±400 Volt and 800 Volt direct current (VDC). As AI clusters scale from 48 V toward higher rack power levels, reliable hot-swap functionality becomes increasingly important for maintaining uninterrupted system operation. The XDP8HD40S-1 enables power modules, converters, GPUs, and server subsystems to be connected and disconnected safely while preserving system integrity, extending Infineon’s power semiconductor portfolio, spanning silicon, silicon carbide (SiC), and gallium nitride (GaN), further into the protection and monitoring layer required for scalable AI infrastructure.

“Future AI data centres require power delivery systems that combine high efficiency, maximum reliability, and comprehensive protection capabilities,” said Magdalene Boebel, Senior Vice President and Business Line Head of Power System ICs at Infineon. “The XDP8HD40S-1 enables system designers to implement reliable hot-swap solutions for ±400 V and 800 V power architectures while providing the monitoring and protection features needed to maximize server uptime and operational efficiency.”

The XDP8HD40S-1 is designed for a range of high-voltage applications, including two-wire 800 V or ±400 V intermediate bus converters and GPUs, 1.500 V intermediate bus converter systems, high-voltage rack and server power delivery, battery backup systems, peak shaving, and 400 V to 800 V single- or dual-side hot-swap implementations. The device is compatible with Infineon’s power semiconductor portfolio, including OptiMOS MOSFETs and CoolSiC JFETs, allowing designers to build efficient, rugged protection stages using the same underlying technologies Infineon has already applied in its 800 VDC AI data center reference designs.

Operating at higher system voltages increases the complexity of controlling inrush current and protecting infrastructure from transient events. The XDP8HD40S-1 integrates digital Safe Operating Area (SOA) control with programmable and preconfigured active protections. Together with a variable pulse SOA control mode (Boost Mode), the device supports optimized startup behavior while maintaining safe operation of the power switch. An integrated gate driver supports external SiC cascode implementations based on a MOSFET and JFET combination, enabling efficient control of high voltage power stages. To further enhance system robustness, the controller incorporates surge immunity protection and supports fast power-switch shutdown with a configurable 1.5 A pull-down current, helping protect AI compute infrastructure during abnormal operating conditions.

The XDP8HD40S-1 supports a broad operating voltage range, with VDDB from 4.5 V to 24 V and VDDA from 4.5 V to 13.2 V, allowing integration into a variety of server and rack-level power systems. For system management and remote diagnostics, the controller includes a PMBus 1.5 interface with enhanced security features for integration into data centre management infrastructure. A built-in black-box recording feature captures critical event information to help accelerate fault analysis and system debugging. Additional protection features include programmable input and output overvoltage and undervoltage protection, external temperature sensor support, and overtemperature protection.

Modern AI server platforms increasingly require real-time visibility into power consumption and system health. The XDP8HD40S-1 addresses this through 12-bit current and voltage analog-to-digital converters, providing measurement accuracies of approximately 1 percent for voltage, 1 percent for current, and 3 percent for power, with analog power monitoring reporting accuracy of 1.5 percent and integrated energy metering for continuous system monitoring. These capabilities support system optimization, predictive maintenance, and power management strategies in high-density AI computing platforms, complementing the reference designs Infineon has already demonstrated for 800 V-to-48 V and direct-conversion architectures now emerging as AI data center rack power continues to rise.

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HARTING Technology Group, a leading global provider of industrial connectivity, today announced it has joined the NVIDIA MGX ecosystem as a power system component provider for the NVIDIA Vera Rubin NVL72 platform.

HARTING delivers power whip solutions – cable assemblies that distribute power from the building to each rack – as growing AI compute demands reshape how power is distributed within data centres. The solutions span the transition across two generations of NVIDIA MGX power architecture, from 480 VAC to the NVIDIA 800 VDC architecture, designed to support the rapidly increasing power demands of AI factories.

As AI workloads grow and compute density increases, data centres must manage significantly more power within increasingly dense environments without compromising reliability, efficiency or scalability. That shift is placing new demands on the connectivity systems that deliver power to AI compute infrastructure.

“AI is fundamentally changing data centre infrastructure, and HARTING has the engineering expertise to help enable that transformation,” said Philip Harting, Chairman of the Board and CEO, HARTING Technology Group. “Our inclusion in the NVIDIA MGX ecosystem brings our decades of connectivity experience in high power environments, from rolling stock to automotive, to the infrastructure supporting the next generation of AI.” For the NVIDIA MGX platform, HARTING provides a 100A/480VAC power whip for the 3RU 110kW power shelf for NVIDIA Vera Rubin NVL72, supporting the transition to 800 VDC with a next-generation 157A/800 VDC DC-DC power whip connecting the power infrastructure to the compute rack.

“As AI compute becomes more powerful and rack densities increase, the industry has to rethink power distribution from the ground up,” said Christian Gysin, Chief Technology Officer and Member of the Executive Board, HARTING Technology Group. “The transition from 480VAC toward 800 VDC is a significant step in that evolution. Connectivity must support higher power levels while delivering the reliability, safety and scalability these environments demand. That is precisely where HARTING’s engineering expertise comes into play.”

HARTING solutions transmit power, data and signal across mission-critical systems worldwide. The company is drawing on decades of experience in demanding industrial environments and its global engineering and vertically integrated manufacturing capabilities to address the evolving connectivity requirements of AI infrastructure and data centres.

As part of the NVIDIA MGX ecosystem, HARTING will be featured on the NVIDIA AI Factory MGX Ecosystem wall at the OCP Global Summit 2026. The executives from HARTING will honourably attend the NVIDIA MGX ceremony. Besides, two HARTING power whip solutions supporting the NVIDIA Vera Rubin NVL72 platform will also be on display in the HARTING booth (Booth No. C79) demonstrating the power connectivity behind current and next-generation AI infrastructure.

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As AI workloads drive demand for more scalable and flexible data centre architectures, organizations are looking for faster, more efficient ways to move, store and protect data. Microchip Technology is helping to address these needs by expanding its portfolio of data centre solutions with new innovations in PCIe connectivity, NVMe storage and acceleration that support high-performance computing infrastructure.

“Customers are looking for technology partners that can address multiple challenges across the infrastructure stack while helping simplify design and accelerate deployment,” said Brian McCarson, Corporate Vice President and GM of Microchip’s data centre solutions business unit. “By continuing to expand our portfolio, we’re providing the building blocks needed to create more efficient, secure and adaptable platforms for today’s increasingly distributed and data intensive computing environments.”

Designed to bring PCIe Gen 6 connectivity to space- and power-constrained platforms, the Switchtec PCIe 6.0 Low Lane-Count (LLC) switch provides up to 64 PCIe Gen 6 lanes, 32 ports and flexible x2, x4, x8 and x16 bifurcation options. Integrated Direct Memory Access (DMA) supports efficient peer-to-peer data movement between connected devices while reducing host CPU involvement. Like Microchip’s larger Switchtec Gen 6 devices, the LLC switch features PCIe Multicast and partitioning capabilities along with robust security through hardware root of trust and secure boot technologies. The new device delivers PCIe Gen 6 capabilities with a smaller, lower power footprint to extend support to a broader range of data centre and intelligent edge applications.

To address the industry’s requirements for secure, scalable and AI-optimized storage infrastructure, Microchip is updating its Flashtec SSD controller portfolio with an enhanced version of the PCIe Gen 5 NVMe 5016 controller. Building on the controller’s high-performance, power-optimized architecture, the updated device adds support for Commercial National Security Algorithm Suite (CNSA) 2.0 to help customers prepare for emerging post-quantum security requirements. Dual-port capability enables highly available storage architectures through redundant data paths and load balancing, while advanced virtualization, Quality of Service (QoS) management and flexible programmability help optimize performance. Together, these enhancements extend the Flashtec platform’s ability to address evolving NAND technologies, high-capacity SSD designs and next-generation enterprise, hyperscale and AI deployments.

Microchip is also expanding the capabilities of its Adaptec SmartRAID 4300 family of NVMe RAID storage accelerators with a series of enhancements designed to address practical operational needs. The updates are focused on strengthening storage security, minimizing downtime, expanding data protection and simplifying deployment across a wider range of environments. Self-encrypting drives (SEDs) with both local and remote key management, and RAID 6 and RAID 60 options have been added for increased resiliency and security. Broader platform compatibility is enabled through support for Linux, Debian, Ubuntu and Microsoft Windows 11 environments. Additional updates such as dynamic NVMe configuration for in-service management, persistent event logging and peer-to-peer rebuild support for greater CPU offload help reduce management overhead while minimizing service disruption. StorageCore API support further expands integration flexibility, enabling customers to incorporate SmartRAID management into third-party tools through in-band host applications as well as customized out-of-band BMC management frameworks.

The latest additions reflect the company’s ongoing commitment to advancing the performance, availability and security of next-generation computing platforms. Visit the website to learn more about Microchip’s full portfolio of data centre solutions.

Development Tools

Microchip’s data centre portfolio is supported by a suite of development and management tools, including ChipLink software for configuration, diagnostics, telemetry and debug of PCIe switches. Additional support includes software for device monitoring, configuration and firmware management, Flashtec NVMe controller development resources and Adaptec storage management software.

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At the Open Compute Project (OCP) Global Summit, Texas Instruments (TI) debuted the first analog-to-digital converter (ADC) to enable 24/7, low-power, event-based remote monitoring at nanosecond resolution.

As AI workloads rapidly increase, data centres operators face significant challenges around power instability, inadequate real-time monitoring and signal degradation, all of which can lead to costly downtime. TI is addressing these challenges through its new ADC device and other analog solutions designed to tackle the challenges of scaling artificial intelligence (AI) infrastructures. Demonstrations on display in TI’s booth, No. E45, at OCP include:

• Real-time nanosecond-level telemetry: TI’s ADC34DSO75 is the industry’s highest performance 3 gigasamples per second (GSPS) ADC, enabling 24/7, low-power, event-based remote monitoring at nanosecond resolution – thousands of times faster than what current solutions offer. Existing monitoring solutions often fail to detect the nanosecond-level power-rail glitches that cause costly, unplanned downtime in data centres. By combining ultra-high speed sampling with advanced on-chip digital signal processing in a small form factor, the ADC34DSO75 detects and captures transient fault events in real time.

As the industry’s first solution to offer fully remote, event-based telemetry, the device eliminates the need for on-site debugging and oscilloscopes. By providing continuous, high-resolution monitoring of low-voltage, high-current power rails, the ADC34DSO75 helps data centres minimize downtime – which can cost operators hundreds of thousands of dollars an hour – while optimizing grid power efficiency. This compact, efficient solution offers hyperscalers unmatched remote diagnostic capabilities at a fraction of the size and cost of conventional monitoring methods.

• Reliable high-voltage power delivery: The LMI800PS is the industry’s first 800V isolated hot-swap controller, replacing a complex discrete solution requiring five integrated circuits with a single high-density package that delivers in-rush current and power path protection. The integration of an isolated bias supply and PMBus telemetry reduces board area by more than 60% and simplifies high-voltage designs. The LMI800PS enables safe power insertion and fast fault isolation, meeting safety standards with 5kVrms reinforced isolation and more than 8mm creepage. This prevents a single load failure from disrupting the shared power bus and maximizes data centres stability. Supporting both ±400V and 800V distributions, the device provides a scalable solution for diverse rack architectures, accelerating the deployment of high-efficiency power systems required for next-generation AI infrastructures.
• Ultra-low jitter signal integrity: The LMK6B is a bulk acoustic wave (BAW)-based oscillator that delivers best-in-class jitter performance and power-supply rejection ratio (PSRR). As data center transmission rates approach 3.2Tbps, the margin for timing error shrinks significantly, and even minute jitter can increase bit error rates and cause system instability. The LMK6B provides industry-leading jitter performance of 9.6fs at 625MHz, ensuring reliable performance and preserving signal integrity for next-generation, AI-driven networking infrastructures.

“As AI workloads drive massive scale, the need for real-time visibility and architectural stability has never been greater,” said Karthik Vasanth, Vice President and General Manager for TI’s Data, Converters and Clocks business unit. “By delivering industry-leading solutions for high-speed telemetry, advanced power protection and precision timing, TI is enabling hyperscalers and operators to design the next generation of AI data centres with optimized performance and minimal downtime.”

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​Semiconductor manufacturer onsemi, specialised in silicon carbide (SiC) power solutions, power management, and CMOS image sensors has introduced its Embedded Power Platform—a wafer-level power architecture that integrates multiple dies (silicon, SiC, and GaN) directly into a single system. This new semiconductor packaging architecture enables higher power density, lower electrical losses and easier power-system integration. This platform has the potential to develop traction inverters and power systems that are smaller and more efficient for EVs.

​onsemi announced the Embedded Power Platform on September 16, 2026, at its headquarters in Scottsdale, Arizona, USA. The architecture delivers up to 3-5 times higher power density compared to current traction inverters. It supports the integration of silicon, silicon carbide, and gallium nitride (GaN) technologies, along with field-effect transistors, drivers, and controllers.

Advance Packaging Reduces EV Power Losses

Traction Inverter design developed by onsemi replaces high-in​ductive wire bonds with precision wafer-level redistribution layers (RDLs). This method reduces inductance and performs higher switching frequency operations. It reduces heat loss and increases EV traction inverter efficiency by addressing both thermal and performance challenges simultaneously.

Subaru Corporation, a Japanese multinational automotive manufacturing company has shown interest in implementing this embedded power platform developed by onsemi in its future electrified vehicles. This technology engagements between industries allows them to explore methods that can enhance energy efficiency, reduce development complexity, and facilitate next-generation vehicle architecture.

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​Multilayer electrodes combine different electrode layers with varying material compositions. This approach provides multilayer coating, enabling better ion transport and mechanical stability compared to a conventional single coating layer in battery technology. While traditional electric vehicle (EV) batteries rely on single-layer coatings, implementing a multilayer coating architecture could enhance battery performance and extend its service life.

Enhancing EV Battery Performance

Researchers will examine different coating materials, layer thicknesses, and electrode structures for both anodes and cathodes. The team will then investigate coating formulation and drying conditions to understand their impact on electrode quality and manufacturing process consistency.

The project will evaluate two coating techniques, which include two-layer slot-die coating and multilayer curtain coating under Aachen’s Chair of Production Engineering of E-Mobility (PEM) to develop scalable production process. On the basis of outcome results of these two methods, the projects aim to broader the thickness of electrode from 200 micrometres to 800 micrometres. This increase in thickness of electrode coating will allow battery manufacturers to reduce the number of inactive layers (separators and current collectors) within a cell and lower the cost of production while expanding energy density.

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​Battery technology plays a central role in describing the performance and driving range of an electric vehicle. The more advanced and efficient the battery technology, the better the overall performance of an electric vehicle will be. With advancements in battery technology over time, ProLogium, a battery developer company has officially announced its Dynamic Coordination Network (DCN) electrolyte architecture for its next-generation GEN4 battery technology.

The purpose of introducing this technology is to showcase advancement in electrolyte design, combining characteristics of liquid and solid electrolytes. The company aims to address key challenges in next-generation batteries such as charging speed, ion transport and battery performance.

DCN Electrolyte Architecture Targets Faster Charging

Electrolyte is a key factor in battery technology, determining how well the battery works, how long it lasts, and how safe it is. It enables lithium ions to move between positive and negative electrodes during charging and discharging. The physical and chemical properties of an electrolyte such ionic conductivity, viscosity, transference number, electrochemical stability window, and solid electrolyte Interphase (SEI) influence charging performance, energy efficiency and battery operation.

The role of ProLogium’s DCN architecture is the coordination of lithium ions within the electrolyte, aiming to improve ion transport and support fast charging in its next-generation battery technology.

As per the company’s report on internal test results, the battery can charge from 5% to 80% in 6.4 minutes. This figure is from the company’s end and should not be considered as independent verification of performance on a production electric vehicle. Charging time in real-life scenarios depends on many factors like battery temperature, charger capacity, cell design, and the battery management system.

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Keysight Technologies, today announced the Keysight Velocity 4900 Series, a new line of cost-effective Layer 1 automation switches designed to transform and automate physical connectivity in network test labs. Through software controlled testbed configuration and management, the Velocity 4900 Series enables engineering teams to dynamically connect and automate the management of test equipment using an intuitive graphical user interface. Teams can securely access and share valuable lab resources remotely, improve equipment utilization, and create repeatable testing workflows for increasingly complex networks and infrastructure supporting AI, 6G, and next-generation services.

As the speed, scale, and complexity of next-generation network technologies and services increase, test teams face growing pressure to perform accurate, comprehensive validation and keep pace with rising traffic volumes. Yet many test labs still depend on manual cabling and time-consuming physical topology changes, leaving valuable equipment underutilized or inaccessible to remote teams. These limitations slow testing, increase the risk of configuration errors, and prevent physical lab infrastructure from integrating fully with automated development and continuous integration and continuous delivery (CI/CD) workflows. Without greater lab automation, organizations risk delays to product releases and critical network updates, with significant business and operational consequences.

The Velocity 4900 Series addresses this physical-connectivity bottleneck by replacing manual testbed configuration with software-controlled Layer 1 connectivity. Engineering teams can quickly create, save, modify, and reproduce physical test topologies while securely sharing expensive or scarce test resources with authorized users across teams and locations.

The first two models in the series are:

• Keysight Velocity 4902: A high-speed Layer 1 automation switch supporting Ethernet speeds from 100 megabits per second (Mbps) to 100 gigabits per second (Gbps). The platform includes 72 QSFP28 ports, supporting 288 connections with breakouts, providing flexible connectivity for high-speed functional and performance testing.
• Keysight Velocity 4906: A high-density Layer 1 automation switch with 288 SFP28 ports supporting Ethernet speeds from 100 Mbps to 25 Gbps. Its density enables organizations to connect and automate a larger portion of their lab inventory within a compact, fixed-form-factor platform.

Both switches provide true Layer 1 forwarding through an ultra-low-latency, fully transparent switching matrix. They support single-mode fiber, multimode fiber, direct-attach copper, and active optical cables, with port speed and media configuration available on a per-port basis. Additional capabilities include port tapping for diagnostics and monitoring and one-to-many traffic replication for broader test coverage and more efficient reuse of traffic-generation resources.

Key benefits of the Velocity 4900 Series include:

• Faster, repeatable testing: Automates physical testbed setup and teardown, allowing teams to provision complex topologies in seconds or minutes rather than relying on hours or days of manual cabling.
• Secure global resource sharing: Helps authorized engineers remotely access and share test equipment across labs, teams, and geographic locations without requiring physical cable changes or equipment movement.
• Improved equipment utilization: Enables organizations to pool scarce or expensive instruments, make underused assets available to more teams, and reduce unnecessary equipment duplication.
• Greater test coverage: Allows engineers using Velocity software to configure physical test environments, run tests, and automatically tear down and recreate those configurations in seconds, expanding test coverage while reducing manual effort and connection errors.
• Incremental lab modernization: Provides a cost-effective starting point for Layer 1 automation that can expand into resource reservation, lab orchestration, test automation, and CI/CD integration through the broader Keysight Velocity portfolio.

Anil Kollipara, Vice President and General Manager of Automated Testing and Assurance, Keysight, said: “Engineering teams developing services and infrastructure to support AI, 6G, and next-generation systems need test environments that can change as rapidly as the technologies they are creating. The Velocity 4900 Series gives organizations a practical and cost-effective way to begin modernizing the physical lab. By automating Layer 1 connectivity and making test resources easier to share, Keysight is helping customers improve utilization, expand test coverage, and accelerate development without requiring a complete lab transformation.”

The Velocity 4900 Series integrates with the broader Keysight Velocity portfolio, enabling organizations to extend software controlled physical connectivity into resource reservation, lab orchestration, test automation, and CI/CD workflows. The Keysight Velocity 4902 and 4906 are available for order immediately with initial deliveries planned for January 2027.

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L&T Technology Services, a global leader in Engineering Intelligence Solutions and ER&D services, today inaugurated its AI-Native EI Lab for Digital Manufacturing in Bengaluru, an experiential environment where customers and partners can see AI-Native Manufacturing, digital twins and autonomous systems working together across the lifecycles.

The EI Lab extends LTTS’ Engineering Intelligence charter to the manufacturing shop floor. Through live and scenario-based demonstrations, customers can move from digital engineering and virtual validation to connected production, intelligent operations and autonomous systems, and see the integration pathways that make each of them scale.

The EI Lab is built around multiple capability pillars:

• Manufacturing Intelligence: An AI-powered Manufacturing Command Center with executive decision-intelligence, manufacturing data fabric with knowledge graph, a supply chain control tower supported by AI-powered production planning and multi-plant benchmarking.
• AI-Native Factory: Here intelligent machines, robots, cobots, drones, autonomous systems and digital twins work together to execute manufacturing tasks with minimal human intervention. This layer has an NVIDIA accelerated environment combining NVIDIA Omniverse, NVIDIA Isaac Sim, NVIDIA PhysX, NVIDIA Cosmos, NVIDIA NeMo, and OpenUSD-based digital twin workflows, accelerated visualization and technologies for Industrial and Physical AI.
• Operations AI & Autonomy: A verification gate that checks actions generated by agent swarms before they reach a machine, enabling verifiable, governed execution and safer autonomous decision-making across connected manufacturing environments. This layer encompasses factory design & simulation, production, process & quality control, maintenance and sustainability.
• LTTS’ Unified Intelligence platform: EIOne, the Engineering Intelligence platform that brings together 100+ EI solutions and agentic AI swarms into a single, governed ecosystem. It empowers enterprises to rapidly build, deploy, orchestrate, and scale AI-driven engineering & manufacturing workflows while ensuring enterprise-grade security, governance, and operational control.

“AI is transforming how manufacturers design, simulate and operate the factories of the future,” said Vishal Dhupar, Managing Director for South Asia at NVIDIA. “The EI Lab, combining NVIDIA technologies spanning hardware, software and world models and LTTS’ deep engineering expertise, gives manufacturers a practical environment to explore and validate AI-driven, simulation-first approaches to industrial transformation.”

“The EI Lab for Manufacturing brings Engineering Intelligence into a tangible manufacturing environment, allowing customers to experience how engineering, AI and physical systems can come together to address real-world challenges. Our collaboration with NVIDIA adds advanced simulation and accelerated computing capabilities to this environment, while our engineering & manufacturing expertise helps translate these technologies into practical manufacturing solutions for our global customers,” said Amit Chadha, CEO & Managing Director, L&T Technology Services and NASSCOM Executive Council Member.

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Five-year agreement aims to expand production and sustainment of a key naval and land-based ballistic missile defence system.

Raytheon, an RTX business, has been awarded a contract valued at up to $6.3 billion to manufacture and sustain Standard Missile-3 Block IB (SM-3 IB) interceptors. RTX and the US Department of War announced on 8 th October that the contract is for five years with two option years. The SM-3 IB missile is a short- to intermediate-range ballistic missile defence interceptors, and is deployed as part of the US and allied missile-defence network to shoot down mid-course flight phase targets during the mid-course phase of flight. The SM-3 IB missile can be launched from Ships or land-based missile launchers to provide integrated air and missile defence coverage.

The deal is designed to accelerate the replenishment of interceptor inventories and offer more stability for production and sustainment. Raytheon said most work will be performed at its sites in Tucson, Arizona, and Huntsville, Alabama. The company also announced investments to increase production capacity of the broader Standard Missile family. The contract comes amid a rising demand for missile defence systems among governments reassessing stockpiles, manufacturing capacity and protection for armed forces and strategic sites. A maximum contract ceiling of $6.3 billion, however, does not equate to an expectation that the entire sum will be spent straight away.

Increased production of complex interceptors demands specialised components, testing facilities and supply chain. To increase throughput, it will not be enough simply to speed up assembly; suppliers and manufacturers will need to meet demanding quality and performance standards. The deal underscores the need to maintain military industrial capacity as missile technology developers, as armed forces search for credible ballistic missile defences.

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Nexus software connects radar, sensors and drone systems to improve detection, information sharing and defensive decision-making.

Dutch software firm Intelic is working on AI-enabled coordination technology to help Ukraine counter an increasing onslaught of hostile drones and missiles, a Reuters report published on 8 th October said. According to the news agency’s report, the company’s Nexus software was tested at a converted airbase and was being rolled out in Ukraine’s battle environment. The software is meant to correlate information from different radar systems, sensors, and image-recognition systems so operators can piece together a clearer picture of aerial threats. The efforts target one of the key problems in modern air defences- where equipment made by different manufacturers does not always easily work with other systems.

Reuters reports, Intelic has been awarded a 30m contract by the Dutch Defence Ministry. It is designed to integrate new systems as the nature of battlespace needs shift- a key feature in a war where drone specifications, countermeasures and combat strategies are constantly changing. More generally, it illustrates how these sorts of technologies are replacing traditional hardware as the fundamental component of defence systems, with the thrust increasingly on integrating different sensors, information and weaponry, and sharing it so the system can act as a cohesive whole.

AI can identify information processing and threat detection; however, the reliability and accuracy of this is based on the sensors and the quality of training data, communications, and operational testing. Human supervision and engagement rules still need to exist, especially for automated systems that support key and high-consequence decisions. Ukraine’s application is being observed by the world as militaries research more flexible, software-based approaches to counter-drone efforts.

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