In the ongoing discussion surrounding the battery value chain, the spotlight frequently falls upon the sources and provisions of battery materials and components. Yet, another challenge is growing in significance: the ability to assess batteries to guarantee exceptional performance, durability, and safety in a timely manner.
As more electric vehicles (EV) models are introduced, OEMs are racing for improvements in battery performance to capture market share as the landscape changes. European and North American-based manufacturers feel locked into a battery supply-chain dominated by China, which is driving even more urgency and investment.
Battery testing is extremely complex, involving thousands of design scenarios that all require a very long time to test. As the physics of complex products like batteries become more and more intricate and time-consuming to understand, engineers find themselves in a dilemma; they either conduct excessive tests to cover all possible operating conditions, or run insufficient tests that risk the omission of critical performance parameters.
According to the head of testing at a European carmaker, competitive pressures are pushing them to invest more and more time, money, and effort into battery testing. However, a $30m investment is nothing when you consider the vulnerability to the China-based supply chain. In their words “We have to find independence at any cost.”
For battery testing, however, the fundamental assumption that “more is better” is a fallacy. Expanding test capabilities to test more cells in parallel appears to be a good strategy on the surface - but it doesn’t actually shorten the testing process for ageing, nor does it give you a better understanding of cell performance and overall ageing characteristics. A better approach is to focus energy and innovation into a more efficient test plan that explores the right combination of conditions to give you an understanding of battery performance with the fewest test steps and test stands possible.
This is where AI and machine learning comes in. Through the ability to learn from data, test engineers can understand behaviour characteristics that are so complex, that without the right tools it is incredibly difficult to decipher. AI that learns from real-world test data is a reliable and effective means for solving the intractable physics of batteries that current simulation and test planning tools don’t efficiently solve.
Delivering on the promise of AI
Cars are becoming more complex and yet engineering teams don’t have more time.
In 2023, researchers at Stanford, MIT, and the Toyota Research Institute conducted experiments applying machine learning techniques to battery testing. The goal was to use AI techniques to reduce the number and duration of tests required to identify the lifecycle of electric vehicle batteries.
By combining multiple AI algorithms, the researchers were able to find the expected lifetime of batteries using a fraction of the tests that traditional methods would require. Where conventional approaches took upwards of 500 days to complete the testing, the teams at Stanford, MIT and Toyota Research were able to apply an iterative, active-learning approach to complete the same result in only 16 days, showing a reduction of nearly 98 per cent.
With these kinds of results, it’s clear that AI is emerging as an intriguing accelerant that can cut time to market significantly for breakthrough products and technologies.
By embracing AI and machine learning principles, engineering teams can navigate the intricate challenges of understanding – and validating – the intractable physics of EV batteries more efficiently, leading to streamlined development, optimised designs, and faster time to market.
Richard Ahlfeld, CEO and founder of Monolith
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