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Ever wondered why a battery that reads full voltage still struggles to power your device? The answer often lies in its internal resistance. This hidden factor affects how efficiently a battery delivers current, especially under load.

In this post, we will briefly examine the basics of internal resistance—and why it’s a critical factor in real-world performance, from handheld flashlights to high-power EV drivetrains.

What’s internal resistance and why it matters

Every battery has some resistance to the flow of current within itself—this is called internal resistance. It’s not a design flaw, but a natural consequence of the materials and construction. The electrolyte, electrodes, and even the connectors all contribute to it.

Internal resistance causes voltage to drop when the battery delivers current. The higher the current draw, the more noticeable the drop. That is why a battery might read 1.5 V at rest but dip below 1.2 V under load—and why devices sometimes shut off even when the battery seems “full.”

Here is what affects it:

• Battery type: Alkaline, lithium-ion, and NiMH cells all have different internal resistances.
• Age and usage: Resistance increases as the battery wears out.
• Temperature: Cold conditions raise resistance, reducing performance.
• State of charge: A nearly empty battery often shows higher resistance.

Building on that, internal resistance gradually increases as batteries age. This rise is driven by chemical wear, electrode degradation, and the buildup of reaction byproducts. As resistance climbs, the battery becomes less efficient, delivers less current, and shows more voltage drop under load—even when the resting voltage still looks healthy.

Digging a little deeper—focusing on functional behavior under load—internal resistance is not just a single value; it’s often split into two components. Ohmic resistance comes from the physical parts of the battery, like the electrodes and electrolyte, and tends to stay relatively stable.

Polarization resistance, on the other hand, reflects how the battery’s chemical reactions respond to current flow. It’s more dynamic, shifting with temperature, charge level, and discharge rate. Together, these resistances shape how a battery performs under load, which is why two batteries with identical voltage readings might behave very differently in real-world use.

Internal resistance in practice

Internal resistance is a key factor in determining how much current a battery can deliver. When internal resistance is low, the battery can supply a large current. But if the resistance is high, the current it can provide drops significantly. Also, higher the internal resistance, the greater the energy loss—this loss manifests as heat. That heat not only wastes energy but also accelerates the battery’s degradation over time.

The figure below illustrates a simplified electrical model of a battery. Ideally, internal resistance would be zero, enabling maximum current flow without energy loss. In practice, however, internal resistance is always present and affects performance.

Figure 1 Illustration of a battery’s internal configuration highlights the presence of internal resistance. Source: Author

Here is a quick side note regarding resistance breakdown. Focusing on material-level transport mechanisms, battery internal resistance comprises two primary contributors: electronic resistance, driven by electron flow through conductive paths, and ionic resistance, governed by ion transport within the electrolyte.

The total effective resistance reflects their combined influence, along with interfacial and contact resistances. Understanding this layered structure is key to diagnosing performance losses and carrying out design improvements.

As observed nowadays, elevated internal resistance in EV batteries hampers performance by increasing heat generation during acceleration and fast charging, ultimately reducing driving range and accelerating cell degradation.

Fortunately, several techniques are available for measuring a battery’s internal resistance, each suited to different use cases and levels of diagnostic depth. Common methods include direct current internal resistance (DCIR), alternating current internal resistance (ACIR), and electrochemical impedance spectroscopy (EIS).

And there is a two-tier variation of the standard DCIR technique, which applies two sequential discharge loads with distinct current levels and durations. The battery is first discharged at a low current for several seconds, followed by a higher current for a shorter interval. Resistance values are calculated using Ohm’s law, based on the voltage drops observed during each load phase.

Analyzing the voltage response under these conditions can reveal more nuanced resistive behavior, particularly under dynamic loads. However, the results remain strictly ohmic and do not provide direct information about the battery’s state of charge (SoC) or capacity.

Many branded battery testers, such as some product series from Hioki, apply a constant AC current at a measurement frequency of 1 kHz and determine the battery’s internal resistance by measuring the resulting voltage with an AC voltmeter (AC four-terminal method).

Figure 2 The Hioki BT3554-50 employs AC-IR method to achieve high-precision internal resistance measurement. Source: Hioki

The 1,000-hertz (1 kHz) ohm test is a widely used method for measuring internal resistance. In this approach, a small 1-kHz AC signal is applied to the battery, and resistance is calculated using Ohm’s law based on the resulting voltage-to-current ratio.

It’s important to note that AC and DC methods often yield different resistance values due to the battery’s reactive components. Both readings are valid—AC impedance primarily reflects the instantaneous ohmic resistance, while DC measurements capture additional effects such as charge transfer and diffusion.

Notably, the DC load method remains one of the most enduring—and nostalgically favored—approaches for measuring a battery’s internal resistance. Despite the rise of impedance spectroscopy and other advanced techniques, its simplicity and hands-on familiarity continue to resonate with seasoned engineers.

It involves briefly applying a load—typically for a second or longer—while measuring the voltage drop between the open-circuit voltage and the loaded voltage. The internal resistance is then calculated using Ohm’s law by dividing the voltage drop by the applied current.

A quick calculation: To estimate a battery’s internal resistance, you can use a simple voltage-drop method when the open-circuit voltage, loaded voltage, and current draw are known. For example, if a battery reads 9.6 V with no load and drops to 9.4 V under a 100-mA load:

Internal resistance = 9.6 V-9.4 V/0.1 A = 2 Ω

This method is especially useful in field diagnostics, where direct resistance measurements may not be practical, but voltage readings are easily obtained.

In simplified terms, internal resistance can be estimated using several proven techniques. However, the results are influenced by the test method, measurement parameters, and environmental conditions. Therefore, internal resistance should be viewed as a general diagnostic indicator—not a precise predictor of voltage drop in any specific application.

Bonus blueprint: A closing hardware pointer

For internal resistance testing, consider the adaptable e-load concept shown below. It forms a simple, reliable current sink for controlled battery discharge, offering a practical starting point for further refinement. As you know, the DC load test method allows an electronic load to estimate a battery’s internal resistance by observing the voltage drop during a controlled current draw.

Figure 3 The blueprint presents an electronic load concept tailored for internal resistance measurement, pairing a low-RDS(on) MOSFET with a precision load resistor to form a controlled current sink. Source: Author

Now it’s your turn to build, tweak, and test. If you have got refinements, field results, or alternate load strategies, share them in the comments. Let us keep the circuit conversation flowing.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

Related Content

• All About Batteries
• What Causes Batteries to Fail?
• Power Consumption and Battery Life Analysis
• Resistivity is the key to measuring electrical resistance
• Cell balancing maximizes the capacity of multi-cell batteries

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submitted by /u/freeflight_ua [link] [comments]

I can never keep this clean, its one thing after another. submitted by /u/lolslim [link] [comments]

Canadian mineral exploration company Quantum Critical Metals Corp and Nusano Inc of Valencia, CA, USA (a privately held physics-based technology company specializing in advanced mass-separation processes) have signed a memorandum of understanding (MoU) to collaborate on the development and refining of critical minerals in North America. The partnership aims to strengthen supply chains for US and Canadian industries by bringing high-purity mineral processing back to North American soil. Most refining of critical minerals currently occurs overseas. This dependence leaves North America vulnerable to geopolitical, economic and environmental disruptions...

Canadian mineral exploration company Quantum Critical Metals Corp and Nusano Inc of Valencia, CA, USA (a privately held physics-based technology company specializing in advanced mass-separation processes) have signed a memorandum of understanding (MoU) to collaborate on the development and refining of critical minerals in North America. The partnership aims to strengthen supply chains for US and Canadian industries by bringing high-purity mineral processing back to North American soil. Most refining of critical minerals currently occurs overseas. This dependence leaves North America vulnerable to geopolitical, economic and environmental disruptions...

It would've been a shame if it was domestic submitted by /u/Mmichex [link] [comments]

Silanna UV of Brisbane, Australia – which provides far-UVC light sources for water quality sensors, gas sensors, disinfection, and HPLC (high-performance liquid chromatography) applications – says that its ultraviolet LEDs effectively inactivate multiple H5N1 avian influenza virus strains within seconds, according to recent research by scientists at the University of Siena. The research showed strong viral reduction of up to 99.999% with Silanna’s 235nm far-ultraviolet C (UVC) LEDs, which support applications in public health protection, pandemic preparedness, and agricultural biosecurity...

Silanna UV of Brisbane, Australia – which provides far-UVC light sources for water quality sensors, gas sensors, disinfection, and HPLC (high-performance liquid chromatography) applications – says that its ultraviolet LEDs effectively inactivate multiple H5N1 avian influenza virus strains within seconds, according to recent research by scientists at the University of Siena. The research showed strong viral reduction of up to 99.999% with Silanna’s 235nm far-ultraviolet C (UVC) LEDs, which support applications in public health protection, pandemic preparedness, and agricultural biosecurity...

КПІ. Вулиця Михайла Брайчевського kpi ср, 11/26/2025 - 19:17

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Just cleaned up and reorganized my small bench setup yesterday and thought I could get some critiques on what might be missing. not shown is a HP 8592 Spectrum analyzer and HP 54615B 500 MHz OScope. submitted by /u/maydayM2 [link] [comments]

The ST87M01-1301 NB-IoT wireless module from ST provides narrowband cellular connectivity along with both GNSS and Wi-Fi–based positioning for outdoor and indoor geolocation. Its integrated GNSS receiver enables precise location tracking using GPS constellations, while the Wi-Fi positioning engine delivers fast, low-power indoor location services by scanning nearby 802.11b access points and leveraging third-party geocoding providers.

As the latest member of the ST87M01 series of NB-IoT (LTE Cat NB2) industrial modules, this variant supports multi-frequency bands with extended multi-regional coverage. Its compact, low-power design makes it well suited for smart IoT applications such as asset tracking, environmental monitoring, smart metering, and remote healthcare. A 10.6×12.8-mm, 51-pin LGA package further enables miniaturization in space-constrained designs.

ST provides an evaluation kit that includes a ready-to-use Conexa IoT SIM card and two SMA antennas, helping developers quickly prototype and validate NB-IoT connectivity in real-world conditions. This is supported by an expanding ecosystem featuring the Easy-Connect software library and design examples.

ST87M01 series product page

STMicroelectronics

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A 60-V boost controller from Diodes, the AL3069Q packs four 80-V current-sink channels for driving LED backlights in automotive displays. Its adaptive boost-voltage control allows operation from a 4.5-V to 60-V input range—covering common automotive power rails at 12 V, 24 V, and 48 V—and its switching frequency is adjustable from 100 kHz to 1 MHz.

The AL3069Q’s four current-sink channels are set using an external resistor, providing typical ±0.5% current matching between channels and devices to ensure uniform brightness across the display. Each channel delivers 250 mA continuous or up to 400 mA pulsed, enabling support for a range of display sizes and LED panels up to 32-inch diagonal, such as those used in infotainment systems, instrument clusters, and head-up displays. PWM-to-analog dimming, with a minimum duty cycle of 1/5000 at 100 Hz, improves brightness control while minimizing LED color shift.

Diode’s AL3069Q offers robust protection and fault diagnostics, including cycle-by-cycle current limit, soft-start, UVLO, programmable OVP, OTP, and LED-open/-short detection. Additional safeguards cover sense resistor, Schottky diode, inductor, and VOUT faults, with a dedicated pin to signal any fault condition.

The automotive-compliant controller costs $0.54 each in 1000-unit quantities.

AL3069Q product page 

Diodes

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TDK’s G series integrates a metal oxide varistor and a gas discharge tube into a single device to provide enhanced surge protection. The two elements are connected in series, combining the strengths of both technologies to deliver greater protection than either component can offer on its own. This hybrid configuration also reduces leakage current to virtually zero, helping extend the overall lifetime of the device.

The G series comprises two leaded variants—the G14 and G20—with disk diameters of 14 mm and 20 mm, respectively. G14 models support AC operating voltages from 50 V to 680 V, while G20 versions extend this range to 750 V. They can handle maximum surge currents of 6,000 A (G14) and 10,000 A (G20) for a single 8/20-µs pulse, and absorb up to 200 J (G14) or 490 J (G20) of energy.

Operating over a temperature range of –40 °C to +105 °C, the G series is suitable for use in power supplies, chargers, appliances, smart metering, communication systems, and surge protection devices. Integrating both protection elements into a single, epoxy-coated 2-pin package simplifies design and reduces board space compared to using discrete components.

To access the datasheets for the G14 series (ordering code B72214G) and the G20 series (B72220G), click here.

TDK Electronics 

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R&S has launched the NGT3600 series of DC power supplies, delivering up to 3.6 kW for a wide range of test and measurement applications. This versatile line provides clean, stable power with low voltage and current ripple and noise. With a resolution of 100 µA for current and 1 mV for voltage, as well as adjustable output voltages up to 80 V, the supplies offer both precision and flexibility.

The dual-channel NGT3622 combines two fully independent 1800-W outputs in a single compact instrument. Its channels can be connected in series or parallel, allowing either the voltage or the current to be doubled. For applications requiring even more power, up to three units can be linked to provide as much as 480 V or 300 A across six channels. The NGT3622 supports current and voltage testing under load, efficiency measurements, and thermal characterization of components such as DC/DC converters, power supplies, motors, and semiconductors.

Engineers can use the NGT3600 series to test high-current prototypes such as base stations, validate MPPT algorithms for solar inverters, and evaluate charging-station designs. In the automotive sector, the series supports the transition to 48-V on-board networks by simulating these networks and powering communication systems, sensors, and control units during testing.

All models in the NGT3600 series are directly rack-mountable with no adapter required. They will be available beginning January 13, 2026, from R&S and selected distribution partners. For more information, click here.

Rohde & Schwarz 

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Microchip’s two radiation-tolerant Ethernet PHY transceivers are the company’s first devices to earn QML Class P/ESCC 9000P qualification. The single-port VSC8541RT and quad-port VSC8574RT support data rates up to 1 Gbps, enabling dependable data links in mission-critical space applications.

Achieving QML Class P/ESCC 9000P certification involves rigorous testing—such as Total Ionizing Dose (TID) and Single Event Effects (SEE) assessments—to verify that devices tolerate the harsh radiation conditions of space. The certification also ensures long-term availability, traceability, and consistent performance.

The VSC8541RT and VSC8574RT withstand 100 krad(Si) TID and show no single-event latch-up at LET levels below 78 MeV·cm²/mg at 125 °C. The VSC8541RT integrates a single Ethernet copper port supporting MII, RMII, RGMII, and GMII MAC interfaces, while the VSC8574RT includes four dual-media copper/fiber ports with SGMII and QSGMII MAC interfaces. Their low power consumption and wide operating temperature ranges make them well-suited for missions where thermal constraints and power efficiency are key design considerations.

VSC8541RT product page  

VSC8574RT product page 

Microchip Technology 

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NcodiN of Palaiseau, Paris, France (which was founded in 2023 to pioneer optical interposer technology with integrated nanolasers for next-generation computing) has secured €16m in an oversubscribed seed financing round...

Current mirrors are a commonly useful circuit function, and sometimes high precision is essential. The challenge of getting current mirrors to be precise has created a long list of tricks and techniques. The list includes matched transistors, monolithic transistor multiples, emitter degeneration, fancy topologies with extra transistors, e.g., Wilson, cascode, etc.

But when all else fails and precision just can’t suffer any compromise, Figure 1 shows the nuclear option. Just add a rail-to-rail I/O (RRIO) op-amp!

Figure 1 An active current sink mirror. Assuming resistor equality and negligible A1 offset error, A1 feedback forces Q1 to maintain accurate current sink I/O equality I2 = I1.

Wow the engineering world with your unique design: Design Ideas Submission Guide

The theory of operation of the ACM couldn’t be more straightforward. Vr , which is equal to I1*R, is wired to A1’s noninverting input, forcing it to drive Q1 to conduct I2 such that I2R = I1R.

Therefore, if the resistors are equal, A1’s accuracy limiting parameters, like offset voltage, gain-bandwidth, bias and offset currents, etc., are adequate, and Q1 doesn’t saturate, I1 can be equal to I2 just as precisely as you like.

Obviously, Vr must be >> Voffset, and A1’s output span must be >> Q1’s threshold even after subtracting Vr.

Substitute a PFET for Figure 1’s NFET, and a current-sourcing mirror results, as shown in Figure 2.

Figure 2 An active current source mirror. This is identical to Figure 1, except this Q1 is a PFET and the polarities are swapped.

Active current mirror (ACM) precision can be better than that of easily available sense resistors. So, a bit of post-assembly trimming, as illustrated in Figure 3, might be useful.

Figure 3 If adequately accurate resistors aren’t handy, a trimmer pot might be useful for post-assembly trimming.

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

 Related Content

• A current mirror reduces Early effect
• A two-way mirror—current mirror that is
• A two-way Wilson current mirror
• Current mirror improves PWM regulator’s performance

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The world is witnessing an artificial intelligence (AI) tsunami. While the initial waves of this technological shift focused heavily on the cloud, a powerful new surge is now building at the edge. This rapid infusion of AI is set to redefine Internet of Things (IoT) devices and applications, from sophisticated smart homes to highly efficient industrial environments.

This evolution, however, has created significant fragmentation in the market. Many existing silicon providers have adopted a strategy of bolting on AI capabilities to legacy hardware originally designed for their primary end markets. This piecemeal approach has resulted in inconsistent performance, incompatible toolchains, and a confusing landscape for developers trying to deploy edge AI solutions.

To unlock the transformative potential of edge AI, industry must pivot. We must move beyond retrofitted solutions and embrace a purpose-built, AI-native approach that integrates hardware and software right from the foundational design.

 

The AI-native mandate

“AI-native” is more than a marketing term; it’s a fundamental architectural commitment where AI is the central consideration, not an afterthought. Here’s what it looks like.

• The hardware foundation: Purpose-built silicon

As IoT workloads evolve to handle data across multiple modalities, from vision and voice to audio and time series, the underlying silicon must present itself as a flexible, secure platform capable of efficient processing. Core to such design considerations include NPU architectures that can scale, and are supported by highly integrated vision, voice, video and display pipelines.

• The software ecosystem: Openness and portability

To accelerate innovation and combat fragmentation for IoT AI, the industry needs to embrace open standards. While the ‘language’ of model formats and frameworks is becoming more industry-standard, the ecosystem of edge AI compilers is largely being built from vendor-specific and proprietary offerings. Efficient execution of AI workloads is heavily dependent on optimized data movement and processing across scalar, vector, and matrix accelerator domains.

By open-sourcing compilers, companies encourage faster innovation through broader community adoption, providing flexibility to developers and ultimately facilitating more robust device-to-cloud developer journeys. Synaptics is encouraging broader adoption from the community by open-sourcing edge AI tooling and software, including Synaptics’ Torq edge AI platform, developed in partnership with Google Research.

• The dawn of a new device landscape

AI-native silicon will fuel the creation of entirely new device categories. We are currently seeing the emergence of a new class of devices truly geared around AI, such as wearables—smart glasses, smartwatches, and wristbands. Crucially, many of these devices are designed to operate without being constantly tethered to a smartphone.

Instead, they soon might connect to a small, dedicated computing element, perhaps carried in a pocket like a puck, providing intelligence and outcomes without requiring the user to look at a traditional phone display. This marks the beginning of a more distributed intelligence ecosystem.

The need for integrated solutions

This evolving landscape is complex, demanding a holistic approach. Intelligent processing capabilities must be tightly coupled with secure, reliable connectivity to deliver a seamless end-user experience. Connected IoT devices need to leverage a broad range of technologies from the latest Wi-Fi and Bluetooth standards to Thread and ZigBee.

Chip, device and system-level security are also vital, especially considering multi-tenant deployments of sensitive AI models. For intelligent IoT devices, particularly those that are battery-powered or wearable, security must be maintained consistently as the device transitions in and out of different power states. The combination of processing, security, and power must all work together effectively.

Navigating this new era of the AI edge requires a fundamental shift in mindset, a change from retrofitting existing technology to building products with a clear, AI-first mission. Take the case of Synaptics SL2610 processor, one of the industry’s first AI-native, transformer-capable processors designed specifically for the edge. It embodies the core hardware and software principles needed for the future of intelligent devices, running on a Linux platform.

By embracing purpose-built hardware, rallying around open software frameworks, and maintaining a strategy of self-reliance and strategic partnerships, the industry can move past the current market noise and begin building the next generation of truly intelligent, powerful, and secure devices.

Mehul Mehta is a Senior Director of Product Marketing at Synaptics Inc., where he is responsible for defining the Edge AI IoT SoC roadmap and collaborating with lead customers. Before joining Synaptics, Mehul held leadership roles at DSP Group spanning product marketing, software development, and worldwide customer support.

Related Content

• Edge AI: Bringing Intelligence Closer to the Source
• An edge AI processor’s pivot to the open-source world
• Edge AI powers the next wave of industrial intelligence
• Synaptics, Google partnership targets edge AI for the IoT
• How Advanced Packaging is Unleashing Possibilities for Edge AI

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