Abstract
It is very important for the battery management system (BMS) in electric vehicles to estimate the state of charge (SOC) of lithium-ion battery (LiB) accurately. This paper firstly established the Thevenin battery model, and the parameters of which are determined by off-line identification method. Then, the bias compensation recursive least squares with forgetting factor (BCRLS) method are used to online identify the parameters of the battery, which can effectively reduce the interference of the noise on the estimation results. Finally, the extended Kalman filter (EKF) method is used to estimate the SOC, and the results of online identification can update the parameters of EKF, so as to achieve a higher estimated accuracy. The results indicate that the maximum estimation errors of voltage and SOC are less than 30 mV and 1%, respectively.
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Wang, Z., Liu, Z., Li, Z. (2019). BCRLS-EKF-Based Parameter Identification and State-of-Charge Estimation Approach of Lithium-Ion Polymer Battery in Electric Vehicles. In: (SAE-China), S. (eds) Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017: Selected Papers. SAE-China 2017. Lecture Notes in Electrical Engineering, vol 486. Springer, Singapore. https://doi.org/10.1007/978-981-10-8506-2_43
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DOI: https://doi.org/10.1007/978-981-10-8506-2_43
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