Joint estimation of SOC and SOH for Li-ion battery based on AEKPF algorithm
1. School of Automobile, Chang′an University, Xi′an，Shaanxi 710061, China; 2. China Auto Research Automobile Inspection Center(Guangzhou) Corporation, Guangzhou，Guangdong 511340，China
Abstract： To improve the estimation accuracy of SOC and SOH for Li-ion battery, the adaptive extended Kalman particle filter（AEKPF） algorithm was used to estimate SOC and SOH. The algorithm could effectively solve the problem of noise error accumulation when using extended Kalman filter （EKF）algorithm by modifying the noise, and as the proposed distribution of particle filter （PF）algorithm, the adaptive extended Kalman filter （AEKF）algorithm was used to update particles in real time to solve the particle degradation of PF algorithm. To improve the accuracy of SOC, considering the deterioration characteristics of batteries, SOH was combined to realize the modified estimation of SOC. The simulation results in Matlab environment show that AEKPF algorithm can obtain more accurate SOC and SOH estimates than AEKF algorithm, and AEKPF algorithm combining with SOH can effectively improve the estimation accuracy of SOC with absolute error less than ±1%.
张新锋1，姚蒙蒙1，宋瑞1,2，崔金龙1. 基于AEKPF算法对锂离子电池SOC与SOH的联合估计[J]. 江苏大学学报（自然科学版）, 2022, 43(1): 24-31.
ZHANG Xinfeng1, YAO Mengmeng1, SONG Rui1,2, CUI Jinlong1. Joint estimation of SOC and SOH for Li-ion battery based on AEKPF algorithm[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2022, 43(1): 24-31.
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2022, Vol. 43 
