Application of extended Kalman filter algorithm in optimizing SOC estimation

Yang Yu; Kun Zhang; Fanzhao Meng; Changzhao Xu; Lixiang Mao; Cuicui Wei

doi:10.1117/12.3015054

4 March 2024 Application of extended Kalman filter algorithm in optimizing SOC estimation

Yang Yu, Kun Zhang, Fanzhao Meng, Changzhao Xu, Lixiang Mao, Cuicui Wei

Author Affiliations +

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129815E (2024) https://doi.org/10.1117/12.3015054
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Nowadays, in the automotive industry, pure electric vehicles are gradually becoming the focus of attention. As the core energy storage component of electric vehicles, the battery plays an increasingly important role. Among them, The parameter of SOC plays an important role in the practical application of the battery. It can directly reflect the remaining power of the car, so that users can have a clearer understanding of the remaining mileage of their car, thereby improving the user's driving experience. At present, the parameter of SOC can not be obtained directly by experimental equipment, which requires us to find the most suitable method to solve the SOC parameters of the battery. Our research will start with the 18650 lithium battery, which is widely used at present, and study the application of the extended Kalman filter algorithm model to optimize SOC estimation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Yu, Kun Zhang, Fanzhao Meng, Changzhao Xu, Lixiang Mao, and Cuicui Wei "Application of extended Kalman filter algorithm in optimizing SOC estimation", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129815E (4 March 2024); https://doi.org/10.1117/12.3015054

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