Abstract Improving the accuracy of parameter identification and SOC (state of charge) algorithm is the key to enhancing SOC estimation. Based on joint parameter identification, this paper proposed a SOC estimation method using particle swarm optimization extended Kalman particle filter (EPF). In the early stage of parameter identification, the forgetting factor recursive least squares (FFRLS) is used. However, when errors in the low SOC region become larger, the swarm optimization (PSO) algorithm is used for parameter identification. PSO uses the voltage data collected during the previous FFRLS parameter identification as input, employing the minimum voltage difference as the objective function to calculate the circuit model parameters. The joint parameter identification method can compensate for the accuracy issue of PSO identification but needs more data in the early stage. The SOC of the lithium battery is estimated based on parameter identification. Aimed at the problem of particle degradation and particle shortage in particle filter (PF), an extended Kalman filter algorithm is used to update each particle. The final approximate posterior probability density is used as the importance density function to overcome particle degradation. At the same time, the particle swarm optimization algorithm optimizes the resampling strategy to improve the sampling process and mitigate particle impoverishment. Finally, the proposed method is compared with PF and PSO-PF algorithms under federal urban driving schedule (FUDS) and US06 Highway Driving Schedule (US06) conditions. Under the FUDS condition, regarding the maximum error, PSO-EPF based on joint identification is 14% higher than PSO-PF based on joint identification, 32.8% higher than PSO-PF based on FFRLS, and 53.2% higher than PF based on FFRLS. Regarding the mean absolute error, PSO-EPF is 56% higher than PSO-PF based on joint identification, 62.5% higher than PSO-PF based on FFRLS, and 67.7% higher than PF based on FFRLS. Regarding the root mean square error, PSO-EPF is 43.5% higher than PSO-PF based on joint identification, 56.2% higher than PSO-PF based on FFRLS, and 65.4% higher than PF based on FFRLS. Under US06 condition, regarding the maximum error, PSO-EPF is 32.2% higher than PSO-PF based on joint identification, 33.2% higher than PSO-PF based on FFRLS, and 52.7% higher than PF based on FFRLS. Regarding the mean absolute error, PSO-EPF is 44.2% higher than PSO-PF based on joint identification, 45% higher than PSO-PF based on FFRLS, and 45.8% higher than PF based on FFRLS. Regarding the root mean square error, PSO-EPF is 35.1% higher than PSO-PF based on joint identification, 35.8% higher than PSO-PF based on FFRLS, and 45.7% higher than PF based on FFRLS. The results show that the SOC estimation method of PSO-EPF for lithium batteries based on joint identification meets the accuracy requirements in the low SOC region. It has higher estimation accuracy than PF and PSO-PF, indicating the strong robustness and generalization ability of the proposed algorithm.
Yun Xiang,Zhang Xin,Wang Chao等. State of Charge Estimation of Li-Ion Battery Using Particle Swarm Optimization Extended Kalman Particle Filter Based on Joint Parameter Identification[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 595-606.
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2024, Vol. 39 
