16 May/June 2024 | E-Mobility Engineering Monolith’s CEO tells Rory Jackson how AI can help improve and shorten vehicle and powertrain development Rules of the game For much of the 21st century, automotive applications of big data have been limited to some attempts at predictive maintenance in vehicles and powertrains. This is beginning to change, with the growing use of analytics by many major organisations to characterise current and future battery cell chemistries, based on vast testing data, and to optimise vehicle qualities such as structural integrity, aerodynamics or power efficiency, based on accumulated test data on respective parameters. However, the benefits of analytics depend on the algorithms at their core. Naturally, a more intelligent algorithm could more accurately predict a cell’s future behaviour, generate a more aerodynamic fairing geometry with a higher success rate, and yield myriad other benefits for battery and vehicle development. Creating such an algorithm thrust Dr Richard Ahlfeld, chief executive officer of Monolith, into the public spotlight in 2016. At that time, he had been working with NASA to explore how the next Mars mission, the Artemis project, or other space systems could benefit from his work on machine learning for complex engineering tasks. He presented his research to wide and positive acclaim from NASA’s global supporters, with someone immediately approaching him afterwards to try to license his technology. “As I was still an employee at Imperial College, I couldn’t license anything, so I went to the university, told them that Monolith’s AI tools are being applied to a range of EV development processes across vehicle, pack and powertrain (Images courtesy of Monolith)
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