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.
贠祥, 张鑫, 王超, 范兴明. 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[J]. 电工技术学报, 0, (): 8911-.
Yun Xiang, Zhang Xin, Wang Chao, Fan Xingming. State of Charge Estimation of Li-Ion Battery Using Particle Swarm Optimization Extended Kalman Particle Filter Based on Joint Parameter Identification. Transactions of China Electrotechnical Society, 0, (): 8911-.
[1] 高德欣, 郑晓雨, 王义, 等. 电动汽车充电状态监测与多级安全预报警方法[J]. 电工技术学报, 2022, 37(9)2252-2262
Gao Dexin, Zheng Xiaoyu, Wang Yi, et al. A state monitoring and multi-level safety pre-warning method for electric vehicle charging process[J]. Transactions of China Electrotechnical Society, 2022, 37(9)2252-2262
[2] 崔淑梅, 宋贝贝, 王志远. 电动汽车动态无线供电磁耦合机构研究综述[J]. 电工技术学报, 2022, 37(3): 537-554.
Cui Shumei, Song Beibei, Wang Zhiyuan.Overview of magnetic coupler for electric vehicles dynamic wireless charging[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 537-554.
[3] Xiong Xin, Wang Shunli, Fernandez C, et al.A novel practical state of charge estimation method: an adaptive improved ampere-hour method based on composite correction factor[J]. International Journal of Energy Research, 2020, 44(14): 11385-11404.
[4] 李宁,何复兴,马文涛,等.基于经验模态分解的门控循环单元神经网络的锂离子电池荷电状态估计[J].电工技术学报,2022,37(17):4528-4536.
Li Ning, He Fuxing, Ma Wentao, et al.State-of-charge estimation of lithium-ion battery based on gated recurrent unit using empirical mode decomposition[J]. Automation of Electric Power Systems, 2022, 37(17): 4528-4536.
[5] 刘素贞, 袁路航, 张闯, 等.基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J].电工技术学报, 2022,37(22): 5872-5885.
Liu Suzhen, Yuan Luhang, Zhang Chuang, et al.State of charge estimation of LiFeO4 batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotech -nical Society, 2022, 37(22): 5872-5885.
[6] 郭向伟, 邢程, 司阳, 等. RLS锂电池全工况自适应等效电路模型[J].电工技术学报, 2022, 37(16): 4029-4037.
Guo Xiangwei, Xing Cheng, Si Yang,et al.RLS adaptive equivalent circuit model of lithium battery under full working condition[J]. Transactions of China Electrotechnical Society, 2022,37(16): 4029-4037.
[7] He Lin, Wang Yangang, Wei Yujiang, et al.An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery[J]. Energy, 2022, 244: 122627.
[8] Wang Zuolu, Feng Guojin, Liu Xiongwei, et al.A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system[J]. Journal of Energy Storage, 2022, 49: 104124.
[9] Shi Na, Chen Zewang, Niu Mu, et al.State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification[J]. Journal of Energy Storage, 2022, 45: 103518.
[10] 巫春玲,胡雯博,孟锦豪,等.基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J].电工技术学报,2021,36(24):5165-5175.
Wu Chunling, Hu Wenbo, Meng Jinhao, et al.state of charge estimation of lithium-ion batteries based on maximum correlation-entropy criterion[J]. Transactions of China Electrotechnical Society,2021, 36(24): 5165-5175.
[11] Li Guidan, Peng Kai, Li Bin, et al.A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF[J].Energy, 2022, 259: 124933.
[12] Liu Fang,Shao Chen,Su Weixing, et al.Online joint estimator of key states for battery based on a new equivalent circuit model[J]. Journal of Energy Storage, 2022, 52: 104780.
[13] Li Bin, Peng Kai, Li Guidan.State-of-charge estimation for lithium-ion battery using the gauss-hermite particle filter technique[J]. Journal of Renewable and Sustainable Energy, 2018, 10(1): 014105.
[14] Li Guidan, Peng Kai, Li Bin, et al.An improved state of harge and state of power estimation method basedon genetic particle filter for lithium-ion batteries[J].Energies, 2020, 13(2): 478.
[15] Liu Qinghe, Liu Shouzhi, Liu Haiwei, et al.Evaluation of LFP battery SOC estimation using auxiliary particle filter[J]. Energies, 2019, 12(11): 2041.
[16] Jouin M, Gouriveau R, Hissel D, et al.Particle filter-based prognostics: Review, discussion and perspectives[J]. Mechanical Systems and Signal Processing, 2016, 72: 2-31.
[17] Chen Lei.Decreasing weight particle swarm optimization combined with unscented particle filter for the non-linear model for lithium battery state of charge estimation[J]. International Journal of Electrochemical Science, 2020, 15(10):10104-10116