基于联合参数辨识的粒子群优化扩展粒子滤波的锂电池荷电状态估计

doi:10.19595/j.cnki.1000-6753.tces.222007

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Abstract

Improving the accuracy of parameter identification and SOC algorithm is the key to improving state of charge (SOC) estimation. This paper proposed a SOC estimation method by using particle swarm optimization extended Kalman particle filter (EPF) based on joint parameter identification. In the early stage of parameter identification, forgetting factor recursive least squares (FFRLS)is used for parameter identification. When the error in the low SOC region becomes larger, the swarm optimization (PSO) algorithm is used for parameter identification. PSO takes the voltage data set accumulated during the previous FFRLS parameter identification as inputand uses the minimum voltage difference as the objective function to calculate the circuit model parameters. The joint parameter identification method combined the advantages of online identification of the FFRLS method to make up for the disadvantages of high accuracy of PSO identification but lack of data in the early stage.Based on parameter identification, the state of charge of the lithium battery is estimated. 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, and 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 is used to optimize the resampling strategy to improve the sampling process and alleviate particle impoverishment. Finally, the proposed method is compared with PF and PSO-PF algorithms under Federal Urban Driving Schedule (FUDS) and US06 HighwayDriving Schedule (US06) conditions. The comparison results are as follows.Under FUDS condition, in terms of 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. In terms of mean absolute error, PSO-EPF based on joint identification 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. In terms of root mean square error, PSO-EPF based on joint identification 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, in terms of maximum error, PSO-EPF based on joint identification 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. In terms of mean absolute error, PSO-EPF based on joint identification 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. In terms of root mean square error, PSO-EPF based on joint identification 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 comparison results show that the SOC estimation method of PSO-EPF lithium battery based on joint identification can meet the accuracy requirements in the low SOC region, and has higher estimation accuracy than PF and PSO-PF, showing the strong robustness and generalization ability of the proposed algorithm.

Key words： Li-ion battery particle swarm optimizationalgorithm extended Kalman particle filter state of charge (SOC)

Received: 21 October 2022

PACS:

TM912

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	Yun Xiang
	Zhang Xin
	Wang Chao
	Fan Xingming

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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, 0, (): 8911-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222007 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/8911

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