Новини світу мікро- та наноелектроніки
Navitas showcasing GaN and SiC-based solutions for AI data-center, energy and grid infrastructure, and industrial electrification at PCIM
Navitas showcasing GaN and SiC-based solutions for AI data-center, energy and grid infrastructure, and industrial electrification at PCIM
EPC showcasing GaN power solutions at PCIM
EPC showcasing GaN power solutions at PCIM
Infineon adds devices to CoolGaN BDS 40V G3 family
Infineon adds devices to CoolGaN BDS 40V G3 family
Texas Tech receives $4.5m TSIF grant for wide/ultrawide-bandgap R&D
Texas Tech receives $4.5m TSIF grant for wide/ultrawide-bandgap R&D
Wolfspeed introduces 3.3kV SiC power modules in two industry-standard footprints
Wolfspeed introduces 3.3kV SiC power modules in two industry-standard footprints
Toshiba starts test-sample shipments of 1200V trench-gate SiC MOSFET for AI data centers
Toshiba starts test-sample shipments of 1200V trench-gate SiC MOSFET for AI data centers
Infineon-led European project Moore4Power launches
BluGlass achieves record 1.9W peak output for single-mode GaN laser
heres my 4 bit calculator progress
 | submitted by /u/Maestro_gaylover [link] [comments] |
EEVblog 1749 - Werewolf VFLEX USB-C Power Supply Adapters - Very Cool!
AI/ML integration in medical systems
Artificial intelligence and machine learning (AI/ML) are increasingly being integrated into medical systems. This delivers smarter and faster care by bringing intelligence closer to where the data is created and used.
The most recent advances in computer vision, large language models, edge computing, and real-time signal processing are improving medical diagnosis and reducing the latency between data acquisition and actionable clinical results. This is enhancing medical imaging use cases and enabling advances in robotic surgery and remote monitoring while delivering more integrated systems to improve patient care.
We introduce some real-world products and solutions that demonstrate how the combination of AI/ML and healthcare has become a reality.
Medical imagingIn medical imaging, AI/ML techniques, including convolutional neural networks, are being deployed directly within magnetic resonance imaging (MRI) and computed tomography (CT) systems. Platforms such as Royal Philips’s AI-enhanced MRI and CT suites and Aidoc’s radiology triage are examples of how models trained using large datasets, helping to detect critical conditions with very low latency. Butterfly Network has further expanded this concept by embedding AI inference into portable, handheld ultrasound hardware.
Philips’s AI-enhanced MRI and CTAt the 2025 European Congress of Radiology, Royal Philips announced a generation of AI-integrated MRI solutions. The main innovation is SmartSpeed Precise, a system powered by dual AI engines built around the company’s existing Compressed Sense and SmartSpeed platforms.
SmartSpeed Precise improves image sharpness by 80%, enabling a better visualization of anatomical structures. Applied to standard Sense imaging, the technology allows scans to be completed in one-third of the time without affecting the quality of the diagnostic images.
A faster scan time means a more comfortable experience for the patient, with less time spent without moving inside the scanner. Moreover, patients can access a diagnosis more quickly, as wait times are reduced. One clinical site reduced exam times to under 60 minutes per slot for whole-body multiparametric exams, enabling it to scan two additional patients per day.
Recently, Royal Philips also received FDA 510(k) clearance for its Verida system, a next-generation spectral CT scanner that integrates AI-driven reconstruction with a specialized dual-layer detector architecture. At the core of the Verida system is a third-generation Nano-panel Precise dual-layer detector.
Unlike photon-counting detectors that utilize direct conversion, this scintillator-based stack employs two layers (the Nano-panel Precise detector) to capture low- and high-energy photon spectra from a single X-ray source, providing spectral results 100% of the time. This “always-on” technology enables spectral imaging capabilities to be active for every patient, on every scan, without requiring special procedures or separate, time-consuming scans.
Inside the signal-processing chain, the system uses a deep-learning reconstruction engine, a properly trained neural network that provides an estimated 80% reduction of the image noise, maintaining the spatial resolution. The computational back end can handle high-throughput processing, up to 145 images per second, enabling full-volume spectral data processing in under 30 seconds, 2× faster than previously available technology.
Figure 1: Royal Philips’s spectral CT Verida system (Source: Royal Philips)
Aidoc’s AI-powered clinical platform
Aidoc’s AI-powered platform, adopted by over 1,600 hospitals worldwide, is built around the idea that connected teams deliver better outcomes. At the heart of the solution is aiOS, the company’s proprietary enterprise platform, which operates as an always-on intelligence layer across a health system. The platform covers 75% of the patient population, according to the company, enabling physicians to make accurate decisions using real-time data and allowing care teams in multiple departments to collaborate on a unified patient journey.
The clinical solutions have five core specialties. Radiology solutions include image-based triage and quantification, powered by 18 FDA-cleared algorithms and eight FDA-cleared partner algorithms. Beyond imaging, Aidoc extends into cardiology, neurovascular care (including stroke and brain aneurysm detection), vascular conditions such as pulmonary embolism and aortic disease, and spine solutions.
A mobile care coordination app delivers real-time notifications for time-sensitive cases, built-in risk stratification, and a mobile imaging viewer, with electronic health-record data automatically fed in to facilitate communication between divisions. Adopted by several leading health systems, Aidoc has delivered significant improvements, including turnaround-time reductions of up to 55% for intracranial hemorrhage cases and length-of-stay reductions of up to 26% for pulmonary embolism cases.
Aidoc’s AI-powered triage platform for large vessel occlusions and medium vessel occlusions proved effective in a study presented at the International Stroke Conference 2026.
In a comparative study of 1,557 CT angiography exams by the University of Texas Medical Branch, Aidoc also showed 92.6% sensitivity for large vessel occlusions, a rate significantly higher than the 70.4% offered by traditional solutions.
Butterfly Network’s ultrasound technologyButterfly Network, a company specializing in ultrasound technology, has received FDA 510(k) clearance for its Gestational Age (GA) Tool, the first “blind-sweep” AI-powered ultrasound application for pregnancy dating. The technical innovation consists of replacing traditional piezoelectric transducer arrays with Ultrasound-on-Chip technology, which integrates a complete ultrasound system onto a single CMOS chip.
The GA Tool employs a deep-learning inference engine trained with a dataset of >21 million ultrasound images. In contrast to conventional fetal biometry that requires precise manual alignment and measurement of the biparietal diameter or femur length by an experienced sonographer, the “blind-sweep” method employs a simplified acquisition protocol. The operator performs guided probe sweeps over the maternal abdomen without the need to interpret images in real time or optimize for targets.
The AI algorithm then examines the resulting volumetric data to estimate the gestational age between 16 and 37 weeks. The system learns the mapping of anatomical landmarks to gestational maturity to ensure high fidelity of diagnosis and provides results similar to traditional biometric assessments in less than two minutes.
Robotic surgery and remote monitoringIn the field of surgery, robotic platforms such as Intuitive Surgical Operations Inc.’s da Vinci system include real-time haptic feedback loops and computer-assisted motion scaling to minimize the distance between the surgeon’s input and the end-effector’s output. Edge ML models on devices such as DexCom Inc.’s G7 and Eko Health Inc.’s cardiac sensors analyze continuous streams of biosignals locally, sending only clinically relevant anomalies, as in remote monitoring.
Intuitive’s da Vinci 5It is one of the most advanced robotic systems for minimally invasive surgery. The name is no coincidence. Leonardo da Vinci was the first to study the movements of the human body systematically and, in 1495, to design a prototype of a humanoid robot, the mechanical knight (called automa cavaliere in Italian).
With a processing capacity 10,000× greater than the previous Xi generation, the da Vinci 5 supports a suite of digital and tactile features intended to improve surgical precision through real-time data analysis and haptic integration.
A major technical upgrade is the introduction of Force Feedback technology. It uses force-sensing instruments and new algorithms to measure the physical resistance that the robotic arms encounter. This information is then fed back to the surgeon’s console so that the surgeon can “feel” tissue tension and resistance. To complement this haptic information, the system also incorporates a Force Gauge, a real-time visual display of pressure information.
The Intuitive Hub and its ML models provide the AI capabilities of the system. These models produce automated case insights by algorithmically dividing surgical video into discrete phases such as dissection, retraction, and suturing. The system provides objective performance metrics based on the instrument kinematics and phase durations. These metrics can be used to compare individual surgical techniques against standardized clinical benchmarks, with a focus on motion efficiency and instrument economy.
During active procedures, the da Vinci 5 utilizes predictive analytics and vision-based AI to enhance the surgical field. The in-console video replay feature allows surgeons to access recorded segments of the ongoing procedure directly through the 3D viewer. This function is supported by AI overlays that can highlight specific anatomical landmarks or track instrument paths.
Figure 2: Intuitive’s complete da Vinci 5 system, consisting of the tower, generator, console, and patient cart (Source: Intuitive Surgical Operations Inc.)
Dexcom G7
The Dexcom G7, a continuous glucose monitor (CGM), has evolved into an AI-driven health device. According to recent updates from DexCom, the G7 (Figure 3) now integrates sophisticated AI to simplify daily diabetes management and deliver deeper metabolic insights.
A relevant AI feature is Smart Food Logging. This tool uses computer vision and ML to analyze photos of meals taken within the app. The AI automatically identifies ingredients and generates meal descriptions, significantly reducing the manual effort required for carb counting and data entry.
Furthermore, DexCom has launched a proprietary generative AI platform powered by Google Cloud’s Vertex AI and Gemini models. This platform analyzes individual health data patterns to offer personalized “Weekly Insights”—that is, recommendations based on the user’s glucose trends, activity levels, and sleep patterns.
Figure 3: The Dexcom G7 15-Day CGM has recently received FDA clearance for people aged 18 years and older with diabetes. (Source: DexCom Inc.)
Eko Health Sensora platform
Eko Health has achieved a major milestone with the FDA clearance of its Eko Foundation Analysis Software with Transformers (EFAST) algorithm, the first-ever cardiac “foundation model” designed for clinical use. This AI has been integrated into the Sensora platform, Eko’s digital stethoscope, which helps clinicians detect signs of potential cardiac diseases quickly and accurately (Figure 4).
The EFAST algorithm integrates several advanced AI features that transform the diagnostic process. Instead of being trained for a single purpose, the system utilizes a cardiac foundation-model architecture trained on over 4 million heart-sound and electrocardiogram recordings. This large dataset allows the AI to learn a general representation of cardiac health, which is then fine-tuned to detect specific conditions, such as structural heart murmurs and atrial fibrillation, with high specificity.
Figure 4: Using Eko Health’s Sensora platform, clinicians receive automated alerts for structural heart murmurs and low ejection fraction, a sign of a weakened heart pump. (Source: Eko Health Inc.)
The post AI/ML integration in medical systems appeared first on EDN.
Google I/O 2026: Agentic AI gets serious
This week’s latest iteration of Google’s yearly developer event reiterated the company’s significant AI commitment. What’s different from messaging and examples past? Maturity.
One of the technologies showcased in the most recent edition of my previous-year retrospective series, published on New Year’s Day, was agentic AI. An overview excerpt from that earlier coverage follows:
Here’s what Wikipedia says about AI agents in its topic intro:
“In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation and do not require human prompts or continuous oversight.”
And what about the aforementioned broader category of intelligent agents, of which AI agents are a subset? Glad you asked:
“In artificial intelligence, an intelligent agent is an entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through machine learning or by acquiring knowledge. AI textbooks define artificial intelligence as the “study and design of intelligent agents,” emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods.”
A recent post on Google’s Cloud Blog included, I thought, I concise summary of the aspiration:
“Agentic workflows represent the next logical step in AI, where models don’t just respond to a single prompt but execute complex, multi-step tasks. An AI agent might be asked to “plan a trip to Paris,” requiring it to perform dozens of interconnected operations: browsing for flights, checking hotel availability, comparing reviews, and mapping locations. Each of these steps is an inference operation, creating a cascade of requests that must be orchestrated across different systems.”
I suggested there that last year’s rapid evolution of agentic AI technology and products based on it wasn’t a one-off; that the maturation and proliferation trends would undoubtedly continue in the coming year and beyond. We’re nearing the 2026 mid-point and, judging from what Google showcased at yesterday’s keynote, I wasn’t offbase with my earlier perpetuation prediction:
But I’m getting ahead of myself…
Android Show: I/O Edition 2026Google extended the trend it initiated last year by delivering a separate Android-specific showcase one week ahead of the main event:
Company representatives covered a lot of ground in only a bit more than a half hour, including pending enhancements to Android Auto and “Continue On”, an in-beta conceptual clone of Apple’s Handoff. But two other topics particularly caught my eye. Generally speaking, Google is fundamentally integrating Gemini Intelligence even more than previously into the core of both Android and its Chrome browser, including both anticipatory awareness of what you might need next and the agentic “chops” to independently (potentially) tackle such tasks on your behalf.
The central reason why I find this trend interesting is contextual in nature. Both Amazon (again) and OpenAI are reportedly working on smartphones based on brand new AI-based—specifically agentic, generative and personalized—operating systems. Going “clean slate” from a software standpoint does have at least some advantages, conceptually speaking at least, but it also tends to result in a “heavy lift” with respect to application development, internally and especially from a third-party standpoint. Conversely, Google’s building on a longstanding Android foundation.
Consider that contrast, too, in the context of the other key Android Show tidbit that I want to pass along today. Confirming longstanding rumor, Google announced that it is seriously re-engaging in the tablet market with Android (where, to clarify, it remains a “player” today, primarily courtesy of its Samsung partnership, albeit on a limited basis versus Apple iPad alternatives), as well as expanding Android into computing form factors that were traditionally serviced by Chrome OS, all with a new operating system version code-named “Aluminum”.
The coexistence of the two operating systems had always been awkward at best. They’re both built on a Linux foundation, but that’s kind of like saying that a Trabant and a Ferrari both hail from a Ford Model T heritage. I’m not trying here to infer a vehicle-analogous comparison between the two operating systems with respect to “sleekness”, price or anything like that, only generally proffering that they’re notably dissimilar. Different code bases, different development teams and schedules…over time, Android and Chrome OS had increasingly diverged, to their shared detriment.
And what does “Aluminum” mean for Chrome OS fortunes long-term? Unclear; the latter’s only notable success has been in the education market, but it’s been a notable success there, so Google needs to be careful about how it hand-holds these key customers during the transition (which I’d suggest is a matter not of if, but when). Event-delivered reassurances included that support-timeframe schedules for existing Chrome OS-based products would continue to be honored in full, that new Chrome OS-based products were still in the development pipeline from partners, and that at least some existing Chrome OS-based hardware would be upgradeable to whatever the marketing moniker for “Aluminum” ends up being. That said, if new Chrome OS hardware is still being announced when the decade turns in a few years, I’ll be shocked.
Foundation AI evolutionsNow for the main event. AI has been front and center in Google I/O messaging for a while now, as The Verge and I joked about two years back:
@vergePretty sure Google is focusing on AI at this year’s I/O. #google #googleio #ai #tech #technews #techtok
And it was more of the same this year. For those of you who’ve been wondering what the term “foundation model” (or variants of that name) means, I’ll start out with a Wikipedia-sourced definition:
In artificial intelligence, a foundation model (FM), also known as large x model (LxM, where “x” is a variable representing any text, image, sound, etc.), is a machine learning or deep learning model trained on vast datasets so that it can be applied across a wide range of use cases. Generative AI applications like large language models (LLM) are common examples of foundation models.
Building foundation models is often highly resource-intensive, with the most advanced models costing hundreds of millions of dollars to cover the expenses of acquiring, curating, and processing massive datasets, as well as the compute power required for training. These costs stem from the need for sophisticated infrastructure, extended training times, and advanced hardware, such as GPUs.
This all in contrast to dataset- and application-specific models. Wikipedia again:
Adapting an existing foundation model for a specific task or using it directly is far less costly, as it leverages pre-trained capabilities and typically requires only fine-tuning on smaller, task-specific datasets.
Last year at I/O, Google shared updates on v2.5 of its Gemini model family (standard, Flash and Pro), which had been introduced a few months earlier. Gemini v3 subsequently arrived last November. And now we’re up to Gemini family v3.5. Commensurate with the update, another term is in circulation for us to sort out: “Frontier model”. NVIDIA with the definition this time:
Frontier models are the most advanced AI models available at a given moment, trained on massive datasets to deliver state-of-the-art performance across many tasks, representing the leading edge of AI capability. They typically power advanced reasoning, image and text generation, and agentic workflows.
Translation: a fancy way of saying “next generation”. Gotta love those marketeers.
More generally, snark aside, I admittedly was particularly gob smacked by this subset of the event-opening keynote remarks by CEO Sundar Pichai:
These stories of how people are using AI are the best measure of progress. To understand the scale at which people are adopting AI, there is another great proxy — tokens, the fundamental units of data our models process, many representing a problem being solved.
Two years ago, we were processing 9.7 trillion tokens a month across our surfaces — a huge number. Last year at I/O, that grew to roughly 480 trillion tokens. Fast forward to today, that number jumped 7x to over 3.2 quadrillion per month. [Editor note: token maxxing? Likely, to a degree. Still…]
It tells an important story about our products and how others are building as well — especially developers and enterprises:
- Over 8.5 million developers are now building new apps and experiences with our models monthly.
- Our model APIs are now processing roughly 19 billion tokens per minute.
- Over the past 12 months, over 375 Google Cloud customers each processed more than one trillion tokens, representing incredible demand for AI from across industries.
Today we have 13 products with over a billion users each. Five of those have more than 3 billion users. [Editor note: and they’re all AI-enhanced, if not AI-centric]
Multimodal and agentic enhancementsBack in December 2024, within a broader attempt to forecast the year to come, I opined:
Large language models (LLMs), which I rightly showcased at the very top of my 2023 retrospective list, are increasingly impressive in their capabilities. But they’re also, admittedly somewhat simplistically speaking, “one-trick ponies”. As their name implies, they’re language-based from both input (typed) and output (displayed) standpoints. If you want to speak to one, you need to first run the audio through a separate speech-to-text model (or standalone algorithm); the same goes for spitting a response back at you through a set of speakers. Analogies to images and video clips, and other sensory and output data, are apt.
Granted, this approach is at least somewhat analogous to human beings’ cerebral cortexes, which are roughly subdivided into areas optimized for language, vision and other processing functions. Still, given that humans are fundamentally multisensory in both input and output schema, any AI model that undershoots this reality will be inherently limited. That’s where newer multimodal models come in. Vision language models (VLMs), for example, augment language with equally innate still and video image perception and generation capabilities. And large multimodal models (LMMs) are even more input- and output-diverse. Think of them as the deep learning analogies to the legacy sensor fusion techniques applied to traditional processing algorithms, which I ironically alluded to in my 2022 retrospective.
Enter the new Gemini Omni multimodal model:
Last year, Nano Banana brought Gemini’s intelligence to image generation and editing. Since then, it’s helped millions of people restore old photos, design from sketches and visualize ideas in ways that weren’t possible before. From the start we built Gemini to be natively multimodal from the ground up, and now we’re taking the next step.
We’re introducing Gemini Omni, where Gemini’s ability to reason meets the ability to create. Omni is our new model that can create anything from any input — starting with video. With Omni, you can combine images, audio, video and text as input and generate high-quality videos grounded in Gemini’s real-world knowledge. You can also easily edit your videos through conversation.
Today, we’re rolling out the first model in the Omni family: Gemini Omni Flash, to the Gemini app, Google Flow and YouTube Shorts. In time we will support output modalities like image and audio.
And what about burgeoning agentic AI assistants such as OpenClaw? How’s that saying go—”Imitation is the sincerest form of flattery”—albeit this time with innate Google services and account-data access?
We’re also introducing Gemini Spark, a 24/7 personal AI agent that helps you navigate your digital life. Spark represents a big shift for Gemini, transforming it from an assistant that can answer your questions into an active partner that does real work on your behalf and under your direction.
Gemini Spark runs on Gemini 3.5 and uses the Antigravity harness. It’s deeply integrated with the Workspace tools you rely on daily, like Gmail, Docs, Slides and more. Even better, because it is a cloud-based agent, Spark keeps working in the background even when you close your laptop or lock your phone. That combination means Spark is ready to take complex tasks off your plate so you can be more present for what matters most.
“Intelligent Eyewear”Last but not least, a few words about head-located wearables, including those with integrated displays. Google seems to be reluctant to refer to them as “smart glasses” (or VR headsets, for that matter). Gee, I wonder why? And why? Snark off (again). As regular readers may already recall, I’ve been following this market quite closely in recent years, even personally investing in a few trendsetting product examples. And we’ve in-parallel been hearing about (and I’ve been writing about) Google’s Android XR operating system and application suite for augmented, virtual and hybrid reality systems for a while now, too.
Well, the reality behind the hype is finally coming to market starting this fall. Supposedly. Conceptually, they sound a lot like Meta’s counterparts (albeit perhaps a bit sleeker) which I’d suggest have been meaningful from an implementation standpoint since at least the October 2023 unveil of the second-generation AI Glasses. That said, Meta’s success has to date been held back by (among other factors) a dearth of third-party support. Here’s a reality calibration: even if Google and partners’ competitive devices are no better off in this regard, their inherent coordination with the aforementioned “Google services and account data” will still give them a “leg up”. More generally, you’ve got to admit this was one heck of a compelling live demo suite:
We shall see.
Wrapping upThere was plenty more interesting news released at the Tuesday keynote and more broadly across the two-day event (which is still underway as I type these words mid-day on Wednesday). Browse other writeups on the Google event portal page, along with coverage at 9to5Google and elsewhere. And then share your thoughts with me and your fellow readers in the comments!
The post Google I/O 2026: Agentic AI gets serious appeared first on EDN.
Lightning hit close by and fried the chip.
 | submitted by /u/hremmingar [link] [comments] |
More EcoFlow woes: So it goes
Portable power station design apparently isn’t easy. So suggests this engineer’s most recent two case studies, not to mention the long-term history (kudos to Kurt Vonnegut for the inspiration).
My first writeup last month discussed why one battery (or a few) in a larger cluster always seems to drain faster than the others, and how this imbalance would affect the system powered by that multi-battery cluster. The answer depended in part on whether the batteries were connected in a series, parallel or a hybrid combo of the two, to “boost the effective voltage (serial) and/or increase overall system runtime (parallel)”. Here’s how I concluded that piece:
I’m covering today only situations where the installed batteries are either non-rechargeable or are removed for recharging. Multi-battery packs recharged in situ (while installed inside a portable power unit, for example) translate to an even more complicated scenario involving, among other factors, the critical importance (and difficulty) of balancing the various cells within the likely series/parallel cluster.
At that point in time, for readers who wanted to know more about this topic, I referenced a Vitron Energy white paper I’d previously recommended in that same piece for other reasons, which also explored this topic at length. Here’s an example of what I’m talking about in the form of a quote from that same white paper:
If a large battery bank is needed, we do not recommend that you construct the battery bank out of numerous series/parallel 12V lead acid batteries. The maximum is at around 3 (or 4) paralleled strings. The reason for this is that with a large battery bank like this, it becomes tricky to create a balanced battery bank. In a large series/parallel battery bank, an imbalance is created because of wiring variations and slight differences in battery internal resistance.
And later in that section, as further elaboration:
When creating a lead-acid battery bank with a higher voltage, like 24 or 48V you will need to connect multiple 12V batteries in series. But there is one problem with connecting batteries in series, and this is that batteries are not electrically identical. They have slight differences in internal resistance. So, when a series string of batteries is charged, this difference in resistance will cause a variance in terminal voltages on each battery. Their voltages become “unbalanced”. This “unbalance” will increase over time and will lead to one of the batteries being constantly overcharged while the other battery is constantly undercharged. This will result in a premature failure of one of the batteries in the series string.
Again, I commend the entire white paper to your attention, not only because it delves in greater detail into the topics discussed in the two excerpts I selected but also because in doing so, it covers not only lead-acid but also lithium-based and other emerging chemistries such as “flow”.
I wrapped up that prior writeup by saying that “I’ll likely have more to say about these topics in future posts as well.” I didn’t necessarily think at the time that I’d be revisiting rechargeable in situ batteries this quickly…but then again, I also didn’t think that, a short time later, I’d personally experience what I still suspect was the outcome of an unbalanced multi-cell battery bank (along with a couple of other functional hiccups, details of which I’ll also share).
The DOA DELTA 3 Smart Extra BatteryI’ve had my EcoFlow DELTA 3 “stack”, the combo of a DELTA 3 Plus and its companion Smart Extra Battery, for one day shy of a year as I write these words in late April:
Until recently, all’s been well. It had only received a couple of firmware updates, all of which had been drama-free. And although it won’t seemingly power my refrigerator reliably, Xcel Energy’s increasingly frequent (or at least so it seems) power outages have provided plenty of other opportunities for me to tap into its stored-electron stash. The most recent outage (again as I write this …another is sooner-or-later-likely-sooner inevitable) in mid-March thankfully lasted only a bit more than seven hours, not several days, but alas, the “stack” didn’t survive it.
During the outage, I’d dragged upstairs its DELTA 2 base unit-plus-smart extra battery “stack” siblings to run an interior lights and recharge flashlights and various mobile devices:
When the utility company-sourced premises electricity came back up just after midnight, I took the DELTA 2 “stack” back downstairs to the workbench in the furnace room, its normal on-standby location. I’d left the DELTA 3 gear there; the base unit’s front panel display was now illuminated but that of the smart extra battery wasn’t, nor seemingly was the latter more broadly functional any longer. And looking more closely, I noticed an “Error 726” indicator on the base unit display that I’d never seen before:
I hit up Google search for suggestions, which were scant, dubious (I don’t think “turn off the unit and wait a few hours for the cells to rebalance themselves” makes much if any sense, particularly given that the only way to turn the unit off is to unplug it first) and more generally indeterminate save for the revelation that Error 726 indicates that “cell voltage differences are too great”. My next and increasingly common step was to publish a Reddit post. One respondent pointed me toward some Facebook group traffic that I’d already come across. Another noted that he/she had experienced the same issue after a recent firmware update, which I’d also done. And a third offered a “Possible it’s a bad cell” suggestion. Hold that thought.
Base unit BMS reset attempts were ineffective; it was also running the latest-available firmware:
I hooked up a fan to one of the AC outputs to drain the base unit’s batteries, in the hope that a full recharge might resurrect its cognizance of the smart extra battery. No dice; the base unit worked fine standalone but threw an Error 726 with the smart extra battery connected. So, I reached out to EcoFlow technical support, who confirmed that the smart extra battery had gone bad and offered to send me a replacement in exchange for my failed unit.
Two weeks later, the new smart extra battery was in my hands. Connecting it to the base unit initially resulted in the generation of another error code in the latter, the nebulous “Error 014”:
After a brief panic, and acting on a hunch, I checked to see if the base unit needed a firmware update before the two devices could be sympatico. Indeed, that was the case, two update cycles’ worth, in fact:
And, at least as of today, the setup once again seems to be working OK, leaving me with one lingering question: what went wrong in the first place?
- Was it a hardware failure, such as (but not necessarily) an unrecoverable cell-imbalance issue, in my specific original smart extra battery?
- Was there a broader fundamental hardware flaw in initial smart extra battery units, suggested by the prompt to do a firmware update (which I hadn’t seen before) when I plugged in the replacement?
- Did a firmware update to the base unit initiate the failure sequence in the first place, as the comments of one of the respondents to my Reddit post indicates might be the case?
- Or were the firmware-update timings (both prior to the original-unit failure and after installation of its replacement) purely coincidental?
Reader theories (and broader thoughts) are as-always welcome in the comments! And now, speaking of botched firmware updates…
A DELTA 2 double-whammyRegular readers may recall that I’ve had issues after doing firmware updates on EcoFlow gear before. Generally speaking, I’ve therefore subsequently waited for an appropriate period, combing Reddit and relevant Facebook groups for posted evidence of others’ troubles, before taking the plunge myself. However, when I got prompted for an update to the DELTA 2 in late February, I (over)confidently decided to plunge ahead absent any preparatory research:
After all, the previous update I’d done to the DELTA 2 back in late December had gone well. I was so overoptimistic, in fact, that I updated my RIVER 2 at the same time:
The RIVER 2 survived the update just fine. The DELTA 2 on the other hand…
I admittedly didn’t immediately notice the issue, because it only happened occasionally. With the combo “awake”, everything seemed to be fine (well, mostly…keep reading). But at some random point after the units displays turned off (I stuck with the default “5 minute” setting), the power LED on the smart extra battery would extinguish, the base unit’s fan would kick on and perpetually run at low speed, and it would unceasingly (generally) or cyclically (briefly) draw ~20W of power from the AC outlet connected to it:
Punch either unit’s front panel power switch and the displays would wake up, the fan would stop and the trickle charge would cease…until after the displays turned back off again, that is. Lather, rinse, repeat. The trickle-charge behavior particularly worried me, because I didn’t want to end up with an overcharged, overheated and potentially exploded-and-burning battery situation on my hands. So, after several cycles of BMS resets and draining-then-recharging the battery sets, all of which “fixed” the issue only temporarily, I reached out to EcoFlow tech support once again with the proactive suggestion that a firmware downgrade might be in order.
They agreed. I never received (again, keep reading) their first attempt to “push” me a rollback from v1.0.2.176 to firmware v1.0.2.163, but the second attempt was successful:
The DELTA 2 “stack” is once again stable, at least from a charging standpoint. And although it took a while for another invitation to update back to firmware v1.0.2.176 to appear, leaving me wondering if either my “rollback” package had been customized to suppress the subsequent update or EcoFlow had pulled firmware v1.0.2.176 completely:
Turns out I just didn’t wait long enough (and yes, I declined when the update invitation eventually arrived):
Unfortunately, EcoFlow doesn’t publish firmware version histories for its devices, so I couldn’t check for an answer to my question that way. And customer service’s silence in (non-)response to my repeated queries about this particular topic weren’t helpful either (in contrast, I’m compelled to note, to their overall general excellent support).
Connectivity troublesWhile I was in communication with EcoFlow, I also brought up an unrelated DELTA 2 issue that I’d been having on-and-off, albeit seemingly more frequently with the passage of time, with the base unit. After some random time period, hours-to-days after I’d established Wi-Fi connectivity, it’d drop Wi-Fi and revert to Bluetooth-only communication with my controlling smartphone:
Other times, even when Wi-Fi was supposedly still operational:
the DELTA 2 base unit, therefore entire “stack”, became “invisible” when I was attempting to reach it from outside my LAN via the EcoFlow “cloud” intermediary:
EcoFlow tech support suggested that the IoT module inside the unit might be gradually failing. I almost didn’t bother pressing the issue further—returning the unit for repair or replacement is something of a hassle, further complicated by the fact that the included flammable batteries mean that I can’t just drop it off at a FedEx Office location but need to arrange for front-door pickup, and then there’s the delay for a replacement unit to arrive—while the loss of Wi-Fi connectivity is annoying, it’s not a functional “death sentence”.
But then EcoFlow confirmed what I’d already suspected, that the IoT module also implements the Bluetooth subsystem, whose functional loss would completely sever further communication with the unit. Couple that with the fact that I’ve been promised a brand-new (not refurbished) replacement, with a zero-cycle fresh battery pack, and it was an offer I couldn’t refuse. I’m awaiting a return-shipping label as I type these words; I’ll report back on the status of the replacement unit via a posted comment on this post once published.
Design is hardYou may have already noticed a commonality to both primary issues noted in this writeup, as well as those in prior EcoFlow problem-themed coverage from me: the smart extra battery. I’m guessing that it’s relatively uncommon for base unit owners to also have this additional-charge (not to mention additional credit card charge) storage peripheral. As such, the prevalence of user problems is also likely to be uncommon. Therefore, I suspect, it’s relatively easy for smart extra battery firmware-related issues in particular to slip through any EcoFlow pre-release testing cracks.
In no way am I making excuses for the various EcoFlow issues I’ve come across; I’m just striving to be pragmatic about root causes. As I noted at the beginning of this writeup, battery pack design is fundamentally challenging. Make a device increasingly “smart” and the level of difficulty further ramps up. Is EcoFlow unique in this regard? I don’t know. I welcome feedback from owners of other manufacturers’ portable power stations (as well as from both EcoFlow and other manufacturers’ representatives themselves) regarding their comparative reliability. And I’d also appreciate insights from other EcoFlow owners re the commonality-or-not of their experiences. Sound off with your thoughts in the comments, please!
—Brian Dipert is the associate editor, as well as a contributing editor, at EDN.
Related Content
- Single-battery failures in multi-battery arrangements: diagnosing selective cell derangements
- EcoFlow’s DELTA 3 Plus and Smart Extra Battery: Product line impermanence curiosity
- EcoFlow’s Delta 2: Abundant Stored Energy (and Charging Options) for You
- Portable power station battery capacity extension: Curious coordination
- Firmware-upgrade functional defection and resurrection
The post More EcoFlow woes: So it goes appeared first on EDN.
Сторінки
