Abstract
The performance and driving range of electric vehicles are largely determined by the capabilities of their battery systems. To ensure optimal operation and protection of these systems, Battery Management Systems rely on key information such as State of Charge, State of Health, and sensor readings. These critical factors directly impact the range of electric vehicles and are essential for ensuring safe and efficient operation over the long term. This paper presents the development of a battery State of Charge estimation model based on a 1-D convolutional neural network. The data used to train this model are theoretical operating data as well as driving cycles of lithium-ion batteries. An Explainable Artificial Intelligence method is then applied to this model to verify the physical behavior of the black box model. Finally, a testing platform is currently under development to assess the effectiveness of the State of Charge estimation model. Our explainable model, called SocHAP, is compared to other contemporary methods to evaluate its predictive accuracy.
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Acknowledgements
This work was carried out as part of HALFBACK and VEHICLE project, sponsored by INTERREG V A Upper Rhine Programme, FEDER and Franco-German regional funds (Bade-Wurtemberg, Rhénanie-Palatinat and Grand-Est).
This paper has received funding from the European Union under the program Horizon Europe and the innovation program under GAP-101103667.
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Heitzmann, T., Samet, A., Mesbahi, T., Soufi, C., Jorge, I., Boné, R. (2023). SocHAP: A New Data Driven Explainable Prediction of Battery State of Charge. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14077. Springer, Cham. https://doi.org/10.1007/978-3-031-36030-5_37
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