17 Dr Richard Ahlfeld | In conversation E-Mobility Engineering | May/June 2024 (sometimes thousands) of the pertinent cell type and cycle them in test machinery, charging and discharging them exhaustively across different environmental conditions. As a result, every major OEM and many other organisations worldwide are investing in cavernous cell-testing labs full of multi-channel battery testers. The average battery testing lab is a project worth hundreds of millions of dollars, designed around creating masses of data, with each organisation operating their lab independently, without any sharing of best practices or data between companies that could make the work shorter or easier. “We have tried in the past to get groups to publicise data, especially when it has been scientifically superrelevant, but the reality is: anyone who is investing close on billions of dollars in testing and characterising cells to get them to market is adamantly against giving any kind of advantage or help to their competitors. No-one is sharing information,” Ahlfeld says. This reticence to share battery testing data, even between labs testing the exact same cell model, leads to a few problems. For one, it means companies must spend (and arguably waste) months of time and effort generating 1-2 TB of cell data per week to understand how long the cells will last in application. As such massive quantities of data are too much for humans to realistically analyse, AI analytics are of obvious use. Second, battery testing labs will experience more cell failures than those seen in EVs, e-bikes and smartphones, from minor cell swellings to severe leakages and explosions. These tend to occur in controlled spaces without risk to researchers, but can still cause delays and setbacks to module development, lasting potentially months. “If you’re supposed to release an EV in three years – which China is achieving, though in Europe it still takes maybe five – and you’ve wasted six months testing a cell only for it to go bang and cause you to start from scratch, that is of potentially huge consequence on your time-to-market,” Ahlfeld notes. Lastly, organisations cannot be certain of the most efficient test plans, encompassing the numbers of cells and cycles per cell, and ranges of conditions to repeat tests under, along with other parameters and permutations. “This, from my view, is the biggest problem in battery testing: there are so many conditions to test that the default fallback strategy for most OEMs is to test everything, so you end up with more than 800,000 possible test combinations of cell data; each test takes between six people wanted to buy my algorithm, and with their encouragement, we founded a company,” Ahlfeld recounts. Since founding Monolith in 2016 as a spin-out from Imperial, Ahlfeld and the company have worked with not only NASA but also McLaren Automotive, BMW, Honeywell, Mercedes, Michelin, Siemens, among others, to improve and shorten vehicle and powertrain development roadmaps using AI. “The goal was always to examine how engineers could adapt to this new world in which terabytes of data were being produced by organisations making new aircraft engines, new cars and so on,” Ahlfeld says. “So, we built our Monolith software platform around this idea of making it easier for engineers to take sensor data, vehicle data and so on, and stream it into a processing engine that figures out key answers to specific questions and problems that they have.” The problem of data For a tangible understanding of how AI can make a difference to e-mobility, Ahlfeld puts forward the example of battery testing. “In many cases, the first decision you need to make when designing a new EV is choosing a cell chemistry for your needs, and today we’re on the cusp of sodium-ion, solid-state, aluminium, silicon anode, and other new cell technologies becoming commercially available, but it is most likely you will pick an NMC chemistry today,” he says. “You need to prototype modules and packs around that, but essentially, no-one really knows how a cell behaves down to its electrochemical elements. It’s incredibly complicated and intractable, and that is the reason some cells die after 12 months and others after 60 months. It is true for current batteries and even more true for next-generation batteries.” Hence, the only way to believably tell someone if their new EV (and its pack) will last for five years is to take hundreds AI tools such as the Next Test Recommender treat battery testing as a game, learning (based on the game’s rules) how to build a test plan much faster and better than humans can
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