19 cell-testing plan (or the remaining parts of an incomplete test plan) based on driving cycles, charging habits, ambient environmental conditions and other factors that the pack will be subjected to. Naturally, the rate of error in one’s prediction models for cell ageing will be very high when testing starts and drop as tests conclude. If AI is applied correctly to train one’s models, the rate of error drops much more sharply against the rate of tests, so the Next Test Recommender routinely reduces the number of tests that its users need to run by 30-60%. “That system is very similar to the sort of reinforcement learning that became popularised through Google DeepMind years ago. It basically treats battery testing as a game with specific rules, and essentially the victory condition is discovering with very high certainty how long the battery will last, when it will get too hot and those sorts of end-of-life conditions,” Ahlfeld explains. “If you’ve looked at Google Go, or any other chess robot, then you know this is something AI has been good at for decades. If you give AI the rules to a game, it can usually learn them much better and faster than a human can, and become very hard to beat. So, it’s very hard to beat an AI at building a test plan if you’re trying to do it yourself manually, and each test costs you time and money, so there is no sense in not using AI to do it.” The last solution for battery testing is Monolith’s Early Stopping Model, which forecasts the results of the remaining planned tests (typically up to six to 12 months ahead) and aims to judge whether running the remaining tests on the plan is unnecessary; for instance, if a cell seems unfit for purpose, based on test results, then it makes little sense to AI to continue characterising it. “In many cases the AI can, for instance, see faster than a human when a cell is ageing too quickly, and when it makes no sense to go all the way to, say, 5000 cycles if, after 800 cycles, you’ve got a whole batch of them that are degrading faster than is ideal,” Ahlfeld says. Future intelligence Beyond cell characterising, Monolith can be applied to anything with a test prototype to be validated, sensors to extract data from, and at least a moderately complex range of different conditions across which that prototype should be tested (to ensure the system is being used to its full potential). “For instance, if you are investigating tyre dynamics or friction in automotive or motorsport, you have so many different conditions that you need to run the tyre in that it quickly gets mind-boggling. Similarly, if you’re in some facet of powertrain optimisation or dyno testing – for example, maybe motor durability testing – you have hundreds of different scenarios to deal with,” Ahlfeld says. “If you have a prototype motor, you need to figure out the best combinations of test parameters, how long you need to test it for, and how far you can trust the data. Empowering engineers to quickly figure that out is what we sought to achieve through Monolith AI since founding the company eight years ago.” Today, Ahlfeld and his team are shifting their work towards visualising what laboratories will look like five to 10 years into the future, particularly as generative AI makes it possible to produce code or compile test reports faster than ever before. “Using AI for anything languagebased is a lot easier now than it was five years ago, but ChatGPT will never be able to understand the intractable physics of batteries,” he says. “If you could ask ChatGPT how to make a functioning sodium-ion battery, that would be great, but it can only learn off the internet, and no-one on the internet has solved that yet. “Advanced thermal-management techniques, new battery chemistries, tyre cooling and plenty of other vehicular problems will always require a real, physical laboratory for experimenting and investigating to see what works. “But if we can build the AI algorithms behind those labs to allow, say, faster discovery of new battery materials, next-generation powertrain configurations and so on, we can bring the future, high-throughput test laboratory to life.” E-Mobility Engineering | May/June 2024 Dr Richard Ahlfeld Dr Richard Ahlfeld was born in Hausen ob Verena, near Munich, Germany, and he achieved his Bachelor of Engineering degree at Bundeswehr University in 2010 (in Mathematical Engineering). He went on to study Aerospace Engineering and Mathematics in a Master’s capacity at Delft University of Technology from 2011, briefly working as an intern at MTU Aero Engines that year, before graduating Summa cum Laude and with Honours in 2013 (completing his Master Thesis at Airbus Defence and Space). After completing a PhD in Aerospace Engineering and Data Science in 2017, at Imperial College London, he founded Monolith AI. Soon after, the company started its first paid work with McLaren Automotive, aimed at reducing a new supercar’s early-stage engineering deviations and accelerating its virtual validation lifecycle. Today, he continues to lead Monolith as CEO. In his spare time, he plays the piano, and he has written articles for The AI Journal as an editorial contributor since 2021.
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