Digital twin for electric vehicle monitoring

Cavanini, Luca and Majecki, Pawel and Grimble, Michael and Devine, Alan and Hillier, Curt; (2024) Digital twin for electric vehicle monitoring. In: 2024 IEEE International Conference on Automation Science and Engineering (CASE 2024). IEEE International Conference on Automation Science and Engineering . IEEE, ITA, 705 - 710. ISBN 979-8-3503-5851-3 (https://doi.org/10.1109/CASE59546.2024.10711691)

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Abstract

The results of a study that investigated the use of Digital Twin technologies for Electric Vehicle propulsion system state of health monitoring is considered. Modern vehicles can share large amounts of data in the cloud through wireless connections. Digital twins represent an effective approach to exploit data-sharing and modern data-driven Artificial Intelligence and Machine Learning technologies, including vehicle monitoring or driving scenarios analysis. This study describes the design and development of a proof-of-concept digital twin demonstrator, that can detect fault/fault-free conditions in electric motor components. It can be used to assess the overall electric drive Failure Rate and to estimate the Remaining Useful Lifetime of the motor. The demonstrator developed within a simulation environment has been validated over a wide set of simulated operating scenarios demonstrating the effectiveness of the proposed approach.

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