Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm

被引:8
作者
Zhou, Huan [1 ]
Luo, Jing [1 ]
Yu, Zinbin [2 ]
机构
[1] Jingchu Univ Technol, Acad Elect & Informat Engn, Jingmen 448000, Peoples R China
[2] Xinjiang Univ, Coll Elect Engn, Urumqi 830017, Peoples R China
关键词
State of charge; State of health; Particle filtering; Extended Kalman filtering; STATE-OF-CHARGE; HEALTH ESTIMATION; LITHIUM;
D O I
10.1016/j.egyr.2023.11.017
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
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.
引用
收藏
页码:4420 / 4428
页数:9
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