荷电状态
卡尔曼滤波器
电池(电)
算法
颗粒过滤器
融合
计算机科学
健康状况
扩展卡尔曼滤波器
控制理论(社会学)
人工智能
功率(物理)
语言学
物理
哲学
量子力学
控制(管理)
作者
Zhou Hua,Jing Luo,Zhihao Yu
标识
DOI:10.1016/j.egyr.2023.11.017
摘要
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method.
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