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摘要： This paper presents a novel data-driven based approach for the estimation of the state of charge (SoC) of multiple types of lithium ion battery (LiB) cells with adaptive extended Kalman filter (AEKF). A modified second-order RC network based battery model is employed for the state estimation. Based on the battery model and experimental data, the SoC variation per mV voltage for different types of battery chemistry is analyzed and the parameters are identified. The AEKF algorithm is then employed to achieve accurate data-driven based SoC estimation, and the multi-parameter, closed loop feedback system is used to achieve robustness. The accuracy and convergence of the proposed approach is analyzed for different types of LiB cells, including convergence behavior of the model with a large initial SoC error. The results show that the proposed approach has good accuracy for different types of LiB cells, especially for C/LFP LiB cell that has a flat open circuit voltage (OCV) curve. The experimental results show good agreement with the estimation results with maximum error being less than 3%. |
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图1 The implementation flowchart of the AEKF algorithm. | 
图2 The implementation flowchart of the data driven-based SoC estimation approach with AEKF algorithm. | 引文信息： Xiong R , Gong X , Mi C C , et al. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources, 2013, 243(6):805-816. (下载链接) | 其他相关论文： 1. Yu Q , Xiong R , Lin C , et al. Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10):8693-8701.（下载链接）
2. Xiong R , Li L , Yu Q , et al. A Set Membership Theory based Parameter and State of Charge Co-Estimation Method for All-climate Batteries[J]. Journal of Cleaner Production, 2019, 249:119380. (下载链接)
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