How Supreme BMS Compute Power Can Leverage End-User Experience and xEV Market Success – Presentation of Future Potential for Fast-Charging Applications Through Real-Time-Capable Implementation of Electro-Chemical Battery Models

Authors

C. Weber, K. Nürnberger, T. Danner, Infineon Technologies AG, Neubiberg; M. Göhring, H. Pegel, E. Richter, Mercedes-Benz AG, Stuttgart; A. Fill, K. P. Birke, University of Stuttgart

Year

2025

Print Info

Production/Publication ÖVK

Summary

The long-term market success of battery electric vehicles (BEV) currently appears to be challenging. A large number of external factors, such as charging infrastructure or the energy industry, can hardly or not at all be influenced by vehicle manufacturers. However, there is great potential for improvement in terms of customer benefits and cost savings in important areas of battery technology. A current area of research involves enabling physical and artificial intelligence (AI) based battery models for real-time applications in the vehicle. Models of this type enable detailed knowledge of the internal cell states. The precise and immediate knowledge of the battery condition results in benefits such as major cost savings, shorter charging times while avoiding premature ageing or the possibility of a more precise determination and prediction of the state-of-health (SoH), a key figure for battery health. Real-time capable implementation of such complex models on an automotive battery management system (BMS) poses particular challenges for the microcontroller hardware. This contribution shows how the use of innovative, high-performance microcontrollers in the battery control unit enables the on-the-edge use of more accurate battery models instead of conventional, highly simplified models. Based on the implementation of an electrochemical battery model on the Infineon AURIXTM TC4x microcontroller, the necessary optimizations in the model for successful implementation are presented. Initial runtime analyses confirm the real-time capability of the implementation and provide information about future potentials for applications of this type.

ISBN

978-3-9504969-4-9

DOI

https://doi.org/10.62626/ubod-h1xb Copy link

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