Inside Wikipedia’s Cleanup of AI-Generated Content

Source: https://spectrum.ieee.org/wikipedia-wikiproject-ai-writing-cleanup

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How Wikipedia’s Editors Keep the Online Encyclopedia Human-Written

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AI Magazine Interview

How Wikipedia’s Editors Keep the Online Encyclopedia Human-Written

AI-generated text is harder to spot, but that hasn’t deterred them

Eliza Strickland

21 Oct 2026

4 min read

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Eliza Strickland is IEEE Spectrum's features editor. She also covers AI and biomedical engineering.

!Image 1: Surreal collage of a woman’s face with geometric shapes, pencil, and Wikipedia globe

Ellia Lebleu is a cofounder of the WikiProject AI Cleanup effort to keep Wikipedia human-written.

Stuart Bradford

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wikipedia ai models type:departments 5 questions

Ellia Lebleu is a founding member ofthe WikiProject AI Cleanup, a volunteer effort launched in 2023 to find and remove AI-generated material from Wikipedia that breaks the website’s guidelines. When the cleanup began, AI writing was often betrayed by conspicuous clichés. Today, the project’s volunteers are developing tools to detect subtler forms of machine-generated content and debating AI’s role in the world’s most widely used encyclopedia.

**When did you realize Wikipedia had a problem with AI-generated material?**

**Ellia Lebleu:** It was around December 2023. A few other editors and I were chatting on Wikipedia’s Discord server, and we had all seen very strange articles with the same odd characteristics. They repeated phrases like “stands as a testament” or “rich cultural heritage.” They sometimes had conclusion sections, or sections called “Why is this important?” That was very typical of ChatGPT in 2023 and early 2024 but very odd for Wikipedia articles—and very easy to spot. Then I decided, let’s create an official project on Wikipedia so more people can deal with this.

Ellia Lebleu

Ellia Lebleu is a founding member of the WikiProject AI Cleanup. Lebleu is pursuing a Ph.D. in AI at the Inria Center of the University of Rennes, part of France’s national research institute for digital science and technology.

**How do editors spot AI-generated writing today?**

**Lebleu:** By 2025, the signs of AI writing were becoming subtler and more structural. For example, AI loves the “rule of three,” negative parallelisms, and other structures of speech that aren’t conclusive by themselves but become meaningful as they accumulate. We also look beyond the article. If someone writes five articles of thousands of words each in 5 minutes, there is reason to suspect AI.

**Does the AI problem just appear in the main text, or also in citations?**

**Lebleu:**Fake or misleading citations have become a major worry. Sometimes an AI hallucinates a citation entirely, although that’s much rarer today because of retrieval-augmented generation that allows the AI to find sources. More often, it finds a real source but gets its content completely wrong. AI citations that don’t actually support what is being written are extremely commonplace.

**English Wikipedia recently****prohibited editors****from using LLMs to generate articles. How much AI-generated content is still getting through?**

**Lebleu:** I’ve been working with [AI-detection company]Pangram to estimate the proportion of AI-generated content. I tested it on Wikipedia content from before ChatGPT and found no false positives, so its results provide a very conservative lower bound. In late 2025, it flagged perhaps 20 percent of new articles and drafts as AI-generated. In early 2026, that went down to 15 or 16 percent. But we don’t yet know whether that’s because our recent policies have had an effect or because models are getting more powerful and Pangram hasn’t had time to adapt.

**Is Wikipedia using AI to combat AI?**

**Lebleu:** Another Wikipedia editor has made an AI source verifier, which goes through the sources on a page and checks whether they match the claims. Since it is itself an AI model, there is no guarantee that it will be correct every time. But if you want something that works at scale and filters out obvious cases, it’s a good first step. The Wikimedia Foundation is also working on a tool called Paste Check, which can flag text pasted directly from some AI chatbots.

And we’ve seen Claude sometimes say, “I can’t write a Wikipedia article; it would go against Wikipedia’s policies.” If all major AI companies implemented that kind of prompt refusal, it wouldn’t solve the problem entirely, because there are still open-weight models that can be tweaked to avoid restrictions. But it would help with people who use commercial models and aren’t especially familiar with the technology.

**Can you tell if the rule prohibiting AI writing is working?**

**Lebleu:**It’s still a little early to tell. We see possible effects, such as prompt refusal and the apparent slight drop in AI-generated content. But again, we don’t yet know if that is because models are getting better or because beginners are learning the rules. The vast majority of AI-generated content is submitted by beginner editors, so we’ve made many efforts to make the rule very visible in welcome messages, editing help, and advice.

**What if AI becomes capable of writing truly reliable Wikipedia articles?**

**Lebleu:** That’s one of the big questions we’re facing. When the guideline was passed, it was for very practical reasons: We were flooded with AI content, we didn’t have good tools, and we needed to put a stop to it. But since then, it has taken on a more philosophical dimension. Many people have called Wikipedia “the last good place on the internet.” Being a place that was, in theory, free from AI slop has become part of Wikipedia’s identity.

A lot of Wikipedia’s communication is now centered on the idea that we’re a human-written encyclopedia. Even AI companies find that interesting. It can be valuable to have a purely human-written corpus on which to [train] AI systems. The fact that Wikipedia is still that, and will remain that for at least the near future, makes it a precious resource that isn’t replicated anywhere else.

But will the people who read and contribute to Wikipedia want it to become partially AI-generated if that makes it more comprehensive? We could preserve Wikipedia’s identity as a human-written corpus while also offering an AI-assisted option.

**Has Wikipedia reckoned with the idea of “Google Zero”—a possible future in which websites no longer get traffic from Google search?**

**Lebleu:** If everything goes through AI search and websites plummet to zero visibility, how do we expose Wikipedia and show that it can be edited and that people can still contribute? Wikipedia is frequently cited in [Google’s] AI Overviews, but users may only see an AI answer citing Wikipedia. They may not realize that Wikipedia is a living project they can edit and contribute to.

The Wikimedia Foundation is working on an attribution framework that could give AI users more information about the article [being cited]: when it was last edited, how comprehensive it is, whether editors have flagged problems, and perhaps even a call to action. In the future, readers might even contribute through AI models: “The AI cited Wikipedia, but there seems to be an error. Can I make an edit request and add this other source I found?”

I can see a very strong symbiotic relationship between Wikipedia and AI models in the future. I don’t see Wikipedia disappearing, but I can see it taking a very different shape. Yesterday, AI was something to clean up. Today, it’s a tool to help us with the cleanup. Tomorrow, it will be the air we breathe.

_A condensed version of this article appears in the November 2026 print issue as “Ellia Lebleu.”_

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The Robot I Didn’t Expect to Build

Agility Robotics’ first practical robotic worker is nothing like I dreamed

Jonathan W. Hurst

19 Oct 2026

13 min read

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!Image 6: A body of water with a layer of solid matter on its surface, with industrial facilities in the background.

**Agility Robotics, the**company I cofounded out of my robotics lab at Oregon State University in 2015, has just launched Digit 5. It’s designed to be the first humanoid robot that can safely and economically do useful work at scale, allowing us to bring tens of thousands of humanoid workers into the real world. Engineered to lift 23-kilogram bins onto high shelves and with a battery that can run for 90 minutes on a 9-minute charge, Digit 5’s goal is to handle heavy warehouse tasks shift after shift, filling the need for labor in jobs that are too physically stressful or too dull for humans to want to do.

Digit 5 is what Agility has been working toward for over a decade. But the robot is heavy and bulky, lacking the graceful natural motion of its predecessors, and it’s farther than I thought possible from the robot I dreamed of building when I started this company. To deliver a useful commercial product, Agility has taken a path that is very different from what I first imagined.

From ATRIAS to Digit

My dream has always been to build robots that combine locomotion and manipulation to move through the world and interact with it as people do. I seek a mastery of motion, achieved through devices designed to navigate the constraints of physics with elegance. When I started the Dynamic Robotics Laboratory at Oregon State in 2008, we began with a theory derived from decades of work in biomechanics: that a simple “spring-mass” model (a body supported by legs that act like springs) should be able to reproduce every known walking and running gait found in animals.

We put theory into practice by building the bipedal robot ATRIAS—short for “Assume The Robot Is A Sphere,” a joke among physicists about reducing a complex system to its simplest possible form. ATRIAS looked like a large microwave on stilts because it was only intended to demonstrate the physics of human walking and running. This was the first glimmer of achieving my dream; even though ATRIAS wasn’t designed to look like us, it still seemed natural, and even somehow alive, when it settled into a walking gait.

With the success of ATRIAS, the barriers to progress shifted from scientific to engineering and organizational, and a startup company seemed like the way to bridge the gap from a one-off research concept to an actual robotic product. I felt confident of two things: that I would be able to build robots that could go where people go, and that this capability would be incredibly useful. This combination of ambition and naivete is necessary for anyone to found a new company—how hard could it be?

Good advice for starting a company is to understand a customer problem, and then find a way to solve it using your unique capabilities. My cofounders, Damion Shelton and Mikhail Jones, and I did not do this. We relied on our belief that this technology would enable new possibilities and trusted ourselves to figure out the business as we went. So we simply focused on building our first product: the bipedal robot Cassie.

Cassie was the first dynamic bipedal robot that researchers could buy, able to maintain its balance through active, fluid motion rather than careful, rigid steps. It had ostrichlike legs, not because we were imitating ground-running birds, but as a result of engineering strictly for performance. It was not intended to be commercially useful, and had no torso, arms, or head. We sold Cassie robots to Harvard, Caltech, the University of Michigan, the University of California, Berkeley, and a handful of other research institutions, ultimately saturating the market for US $250,000 bipedal research robots after having sold a total of 10 of them.

!Image 7: Three bipedal robots lined up in a warehouse, showing their evolving designs.ATRIAS, Cassie, and Digit 4 EVT [left to right] are on display at Agility Robotics’ RoboFab in Salem, Ore. The 70,000-square-foot facility has the capacity to produce more than 10,000 Digit 5 robots per year.Agility Robotics

Because Cassie’s hardware enabled research into the physics of walking and running, the legged-robot research frontier shifted to control software. Traditionally, engineers controlled legged robots with detailed mathematical models that determined how the machines should move. With Cassie, researchers pioneered the new approach of reinforcement learning for bipedal legged locomotion: A simulated robot learns through trial and error, and the resulting neural network controls the physical machine. This approach is what enables virtually all of the dynamic behaviors that you see on legged robots today, and Cassie looked alive as it recovered from stumbles while climbing stairs, ran 5 kilometers across the Oregon State campus, and broke the robot world record for a 100-meter dash.

Cassie got us closer to being able to move through the world with confidence and grace, but to make it useful, we needed manipulation. After exhaustively exploring different configurations, we found that an upright torso helped the robot balance, while two arms mounted near the top of the torso allowed it to pick up large objects and to catch itself when it fell. Digit 1, which added a torso and arms to the Cassie legs, was our first mobile manipulation robot, and another step on the path toward a product that could do useful work for commercial customers.

Why Digit Began With Warehouse Bins

As we explored the first applications for Digit, potential customers kept asking us: Why legs? And why a humanoid? These were tough questions for me to answer, because they exposed a core business reality: It didn’t matter to Agility Robotics if we solved a cool science problem in a beautiful way. What mattered to the business was solving real problems for customers.

After years of engagement with industry partners, we found one answer in warehouses and manufacturing facilities: moving bins. Bin handling is a clearly defined task where two arms are needed to lift large bins up high and legs are the best way to balance and move in narrow spaces. Because the task can vary widely from one deployment to another, it hasn’t been broadly addressed by single-purpose automation. Yet it was constrained enough to give Digit an economically viable first job while it developed a broader set of skills.

In 2019, Digit began demonstrating its capabilities to do real work. With Ford Motor Company, we showed how Digit 1 could carry packages from an autonomous vehicle to a front porch. We developed Digit 2 and 3 while working with a certain global package delivery company on moving and sorting cardboard boxes. Amazon tested Digit 4 on moving and stacking bins, and it became the world’s first commercially deployed humanoid at the logistics provider GXO. In 2024, Digit appeared on the cover of _\_Time\__ magazine.

Despite Digit’s apparent success, we began to run into conflicts between my dream and the needs of actual customers. We were no longer working with relatively patient university research labs. Agility has to sell robots to survive, and customers will only purchase robots that provide value. If bins weren’t moving, our customers were losing money. We spent a huge amount of time and effort keeping our first deployments operational, which arguably eliminated any profit we made. Yet this also kept our company learning and alive the way no lab demo ever could.

I had no idea how hard it would be, or that running a company would involve so many things that have nothing to do with solving technical problems: deployments across the country; shipping, service, and customer support; fundraising and investor relations; more fundraising; human resources and IT; developing fleet-management software; managing a supply chain; and even more fundraising. I had no experience with any of these things. My career had focused on solving the technical challenges of robots interacting with the world, and it turned out that was only a tiny piece of building a successful robotics company. But Agility was the only path I saw to continuing the technical work and having an impact.

Turning Digit Into a Product

I knew that Agility needed to focus more on robots as a product, but I didn’t know how to make that happen. Hiring the people we needed to run the company meant giving up an immense amount of control, but we had no other choice. We recruited a new executive team with the experience required to commercialize this new technology, and they quickly made changes that were both painful and necessary.

![Image 8: A series of images showing three headless humanoid robots with each one getting somewhat bulkier and less graceful.](blob:http://localhost/4d9d16d517c455b95ebd1f6abb95841e)Digit versions 1, 2, and 3 [left to right] show the evolution of the robot from a dynamic walking platform into a useful mobile manipulator. Agility

We had intended the next revision of Digit, Digit 4 EVT (Engineering Validation and Test), to be a relatively minor upgrade. But Agility’s new leadership insisted that the robot was missing key features that were essential if Digit was going to become a real product. So Digit 4 EVT instead incorporated a series of major changes in a highly compressed timeline, such as larger batteries and system-wide upgrades for safety and regulatory compliance, which added enough mass to dramatically alter the robot’s appearance and capabilities.

![Image 9: A relatively bulky humanoid robot worker with a white head, simple grippers, and reversed legs stands for a portrait looking directly at the camera.](blob:http://localhost/f41bd75c4336255dd557715d1473d650)While early Digits were headless, Agility Robotics was able to justify a head for Digit 4 to optimize antenna and camera placement, and it turned out to be surprisingly useful for human-robot interaction.Christie Hemm Klok

I could only watch as others brought their own talents to bear to make Digit into a successful product, in gut-wrenching ways that didn’t always align with my vision. There were some especially difficult times when I felt like there were compromises made that didn’t seem necessary, but making those decisions was no longer my sole responsibility. I had to accept that Digit had grown far beyond me, and look for ways of influencing decisions rather than directing them. In the moment, it was incredibly frustrating, as Digit 4 EVT took a few steps backward from my vision of how humanoids should look and move.

Agility is in our name because agility is core to the future of the company. I want Digit to be lithe and graceful. When people see Digit, I want them to like the robot, to understand what it can do at a glance, and to feel that it’s there to help. But with Digit 4 EVT, all of that was sacrificed to functionality, because of course it had to be—the customer just needs the robot to do the work. The design process became a ruthless triage, with aesthetics and elegance of movement being delayed over and over again in favor of brutal functionality. For an engineer whose mind lives more in the future than in the present, it has been hard for me to accept this trajectory. And yet, through Digit 4 EVT’s commercial deployments, we began to really understand specific applications and what they required. We mapped the first 10 use cases Digit would perform, and then began designing Digit 5 from scratch based on everything we had learned.

Digit 5

Digit 5 will be Agility’s first real step toward a general-purpose robot. It marks our transition from imagining what a future humanoid robot should be to designing a product around the realities of customer deployments. These realities include things like European CE regulatory compliance, North American UL electrical-safety certifications, and Federal Communications Commission limits for electromagnetic radiation—all of which felt tangential to my dream for Digit, but were absolute requirements.

![Image 10: A strong and bulky humanoid robot with a teal torso and legs, silver arms, and a black head stands with its arms upraised.](blob:http://localhost/f41bd75c4336255dd557715d1473d650)Digit 5 is designed to be the first humanoid robot that can operate safely next to human coworkers while working 20 hours a day and lifting more than a human can.Christie Hemm Klok

Customer requirements helped us realize that safety in particular was one of the most critical barriers to scaling. In Digit 4’s commercial deployments, the robots had to work behind physical barriers, separated from humans at all times, to meet insurance and regulatory demands. This seemed silly to me, because I’ve never felt that our robots were dangerous, but we have to _\_prove\__ that they’re safe. And since keeping the robots in safety cages severely limited their usefulness, enabling Digit 5 to operate safely around people became a primary design goal. We spent two years on this challenge.

Digit 5 is designed to be the first humanoid robot that can operate safely next to human coworkers. It will rely on an array of sensors to locate nearby people in a manner that’s certifiably safe, and then take actions based on that information. As a person approaches, Digit 5 is programmed to stop what it’s doing, set down its payload, sit on the floor, and cut power to its motors before the person gets close enough to touch the robot. This conservative approach reduces the risk to nearby people in a verifiable way, and we believe it’s the only approach that makes sense as a first step toward working alongside humans.

Yet this strategy also feels like a massive slowdown on the path toward robots becoming part of everyday life. Enabling coexistence with humans has influenced almost every aspect of Digit 5’s design: the shape of the legs and the body, the safety-rated sensors within protective housings, the isolated computers dedicated to safety, and more. All of these features increase the robot’s complexity, power consumption, mass, and physical size.

!Image 11: Turquoise humanoid robot with labeled safety, battery, cooling, and mobility featuresDigit 5 is Agility’s largest, heaviest, strongest, and most capable humanoid robot worker.

!Image 12: Turquoise humanoid robot with cameras, swappable grippers, and lifting-optimized legsThe robot’s design was driven by safety, with integrated protection for the robot and for the humans it will work around.Agility Robotics

Weighing over 120 kg, Digit 5 is a _\_huge\__ robot. It needed to achieve the lifting limits recommended under the U.S. National Institute for Occupational Safety and Health guidelines for human workers, which include repeatedly lifting 23-kg bins and reaching shelves 1.75 meters high. Digit is designed to perform tasks like these for 20 hours a day—something no human can do. This requires motors with enough power and torque to lift the load, but also enough precision to delicately control the force Digit applies when it makes contact with the world. To power those motors, we needed a large battery and a dock for rapid autonomous charging. The dock will deliver 240 amperes at 48 volts, which is enough current to make its thick charging cables visibly wiggle under the induced magnetic forces, while powerful fans blow air through cooling channels in the robot’s torso to keep the battery from overheating. The battery and cooling system takes up about 75 percent of Digit’s torso, but it will allow the robot to operate for 90 minutes and fully recharge during a 9-minute break, enabling near-continuous work.

All of this extra bulk means that Digit 5 can no longer run or jump the way previous generations could, and the svelte form I imagined has become physically impossible. In some ways, Digit 5 feels more like a forklift than the graceful humanoid I set out to build. But looking at the situation rationally, I believe this trade-off is necessary for any company that claims its humanoid can do this kind of work today: If you see a robot that looks like a human working in a factory or warehouse, it either can’t lift the required weight or can’t make it through the whole shift. And if it’s not covered with sensors, it’s likely not safe to stand next to.

The most visible change with Digit 5 is that humanlike legs replace the ostrichlike legs of our earlier versions. While the “backward” leg configuration was optimized for walking and running, we discovered that it created problems with squatting because the ankle will strike the ground. Digit 5 will spend a lot of time lifting heavy objects from near the floor, so we used a humanoid leg configuration with knee and hip motors that both contribute torque to help the robot stand up again.

!Image 13: Small teal robot sitting on factory floor as a worker walks past in the backgroundWhen Digit detects an approaching human, the robot will lower itself to the floor and cut power to its motors. Agility believes this is the only near-term approach to verifiably reduce risk when the robot is working near people.Agility Robotics

Other companies building humanoid robots have copied the human form without ever understanding its purpose. It’s very frustrating that, to an outside observer, it now looks like we discarded Agility’s distinctive leg design and simply did what everyone else was doing. Yet I know that we arrived at this shape based purely on functional engineering choices. Making a robot look like a human is the easy part—without the function to match, and the understanding of the physics underlying that function, the whole point of the human form may be lost.

With the power, battery life, and safety features that we are designing into Digit 5, we will have a truly useful robot. But it also compromises on some things that are core to my vision and mission. I understand why we made the choices we did and can justify them all, but together they leave me unsatisfied. It’s the curse of the engineer.

What Comes After Digit 5

I believe Digit 5 will be the scaling moment for Agility Robotics, the inflection point where we can begin deploying robots in large numbers. While the robot will continue carrying bins, our road map includes many more tasks, from picking individual items and tending machines, to preparing parts for assembly and shipping, and eventually doing assembly work itself. Its interchangeable tool interfaces and modular design will enable us to gradually upgrade its subsystems. It will be a platform for improvement, a source of data for future behavior training, and a viable product all at the same time.

![Image 14: Author Jonathan Hurst, a tall slim white man wearing a black long sleeve shirt and dark jeans, stands in between a smaller slimmer humanoid robot with a white head and a larger bulkier humanoid robot with a black head.](blob:http://localhost/844e9e51f469f14c468d718a2089da9d)Hurst stands between the previous version of Digit, Digit 4 [left], and Digit 5.Christie Hemm Klok

But I cannot love Digit 5. It’s the largest, heaviest, most expensive robot Agility will ever build, full of design compromises that enable it to be exactly the robot it needs to be right now rather than the robot I dreamed of. In the beginning, I envisioned steady progress on agility and dexterity, with each version becoming more dynamically capable. Instead, developing Digit 5 has required large detours to incorporate requirements that seemed unrelated to my original vision. However, I now recognize that tackling these challenges was always necessary. Customers and product requirements didn’t ruin my vision; they revealed what my vision was missing. This is how robots will achieve commercial success and make a real-world impact, which will bring me closer to my scientific dream as well.

We won’t have a humanoid that matches the capabilities of humans until it achieves that mastery of motion that inspired me to found Agility over a decade ago. I saw glimmers of it with the spring-mass locomotion of ATRIAS, and more glimmers with Cassie’s running gait.

I know that such mastery is physically possible with the tools and hardware that we have right now, and despite all of its compromises, Digit 5 will give us a practical foundation for pursuing that dream. I want every future version of Digit to be lighter and more graceful, to interact more naturally with humans, and to be better at manipulating objects and moving through the world, enabling robots to enter aspects of our lives that they haven’t been able to help with before. By the time I’m old enough to need assistance at home, I expect Digit will be up to the task.

With Digit 5, Agility has built a robot that will be useful in our world. Now I’m getting back to work to make it beautiful, too.

_This article appears in the November 2026 print issue._

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Simulation App Speeds Up Electric Truck Battery Design

Using a laser-welding simulation app, Volvo Trucks has accelerated the design of long-lasting, high-performance batteries

Brianne Costa

16 Oct 2026

5 min read

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!Image 15: Light blue Volvo FH electric semi‑truck cab shown from front‑side view.

A consortium including Volvo Trucks used COMSOL-based multiscale simulation and a custom app to optimize laser-welded battery pack design and manufacturing, improving the robustness and lifetime of batteries for electric trucks to enable longer-range, more reliable heavy-duty electric fleets.

Volvo

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_This article is brought to you by COMSOL._

These days, no one blinks an eye at two-day, one-day, and even same-day package deliveries. Despite the plethora of localized fulfillment and distributi on centers that have cropped up to meet the demand, delivery trucks still need to make considerably long drives, often over highways. To reduce fuel and maintenance costs as well as carbon emissions, companies are investing in electric trucks for their delivery fleets. While turning to electric trucks is a promising solution, the battery packs incorporated into these automotive designs often fall short in the lifetime needed to make long-distance highway travel feasible.

To help overcome the issue of battery lifetime, a consortium of universities and industry partners incorporated multiscale simulation and a custom simulation app into the battery manufacturing process. The organizations involved in this project were Aurobay, Resolvent, the European Commission, the Technical University of Denmark (DTU), Vinnova (the Swedish Agency for Innovation Systems), Innovation Fund Denmark, and Volvo Group Trucks Operations.

The simulation app, built by Resolvent, a Denmark-based engineering consultancy that specializes in green energy, enables the R&D department at Volvo to easily test battery lifetime performance.

Laser _Who?_

The Multiscale Simulation of Laser Welding for Optimal Battery Pack Manufacturing project — referred to as “LaserBATMAN” — was initiated in May 2022 and wrapped up in May 2025 (Ref. 1). Although _BATMAN_ here refers to _battery manufacturing_ and not the famous masked superhero, the goals for the project also strove for greatness.

The LaserBATMAN project aimed to optimize two key performance metrics for battery packs: robustness and lifetime (Ref. 1). One element of the battery manufacturing process that affects both of these metrics is the laser welding used in component joining processes (for example, to connect battery cells in a pack via a busbar). If done correctly, laser welding can positively impact battery life, but if done incorrectly, laser welding can increase the risk of battery degradation. Computational modeling can be used to analyze and predict the effects of welding and joining processes at different stages of the manufacturing process.

Martin Refslund Nielsen, former chief commercial officer and partner at Resolvent, explained that for this type of multiphysics and multiscale problem, “COMSOL Multiphysics is just the right tool.”

R&D Workflow for Multiscale Model and App

The Resolvent team used the COMSOL Multiphysics software to study how laser welding affects the electrochemistry and degradation of battery cells. At least, that’s how the project started.

“Ambitions grew,” Nielsen said, explaining that the team ended up modeling the performance of the entire battery, from cell to pack level, and even “turned [the model] into a design tool in the end”, in reference to the app.

After training the deep neural network of the battery model, the team built a custom simulation app to efficiently optimize busbar tabs in the configuration of a battery pack.

First, the team built an electrochemical model of a battery cell by inputting the formulas for the relevant chemical reaction kinetics (Figure 2). The resulting cell-layer model was then fed input data, and the output data was used to train a deep neural network (DNN). With this reduced-order model, the team was able to quickly gain insight into cell behavior.

After training the DNN of the battery model, the team built a custom simulation app to efficiently optimize busbar tabs in the configuration of a battery pack.

!Image 16: Flowchart of cell layer and reduced order models using DNN and physics to get resultsFigure 2: The Resolvent battery model.COMSOL

A “Tool Within a Tool” for Busbar Weld Design

The different features of the simulation app, as described by Nielsen, include its options to input dimensions for the pack geometry (Figure 3), the number of cells, and operating conditions such as ambient temperature and charge–discharge current profiles. The app also makes it possible to apply a simple charging current and load customized cycles. Users are then able to see how the battery performs with each set of specific inputs.

!Image 17: Blue rectangular battery pack with metal terminals on a transparent backgroundFigure 3. The battery pack model developed by Resolvent.COMSOL

Another important aspect of Resolvent’s app is the _Busbar Weld Design Tool_ button, which Nielsen described as “a tool within a tool.” This button can be used to efficiently calculate busbar weld performance after a number of cycles. After clicking the button, a window opens with options for setting different parameters, including the shape of the weld, busbar thickness, and penetration depth of the laser (Figure 4).

“[This button] is interesting from a robust design perspective,” said Nielsen. Users can apply a variant to the input parameters to see what happens in production if the weld does not reach far enough into the busbar.

!Image 18: COMSOL interface showing 3D busbar weld current density simulation and Volvo logoFigure 4. Resolvent’s simulation app, showing how certain parameters can be customized. The model shows how the busbar weld is affected by a cooling airflow.COMSOL

“We certainly get deep technical insight here, and we can dimension the design in the sweet spot. Deep technical insight is also key to optimizing product performance,” Nielsen said. For example, if the results suggest better cooling, the team could potentially reduce the design by two battery cells and retain the same performance, easily quantifying the tradeoff between cooling and setup.

Predicting Pack Performance

The app’s user interface also includes a _Compute Battery_ butto

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