Richard Threlfall

Richard is CTO of Warwick Control EV, a company dedicated to introducing electric powertrains, fuel cells and battery technology into the transport sector. He has 30 years of experience in applications and integration covering the marine, aerospace, off-highway, bus and truck and automotive sectors. His specialty is control systems integration, ensuring they are fit for purpose by developing a thorough understanding of the application.

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Presentation

Machine learning methods for battery state-of-health assessment

Energy Storage Systems in the form of lithium-based batteries are increasingly popular and still relatively expensive. The economics suggest a circular lithium economy should be achievable, one of the principal steps being extended use. BMS systems track in-service battery health with accuracy reducing as cycle life increases. This presentation investigates methods for developing Machine Learning algorithms to accurately assess the state-of-health of a pack. Starting with some used cells with a range of measured SoH values, we’ll demonstrate the feasibility of Machine Learning to accurately determine the pack health with a range of dynamic charge-discharge profiles.