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

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3289 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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.

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

Received: 21 October 2022

PACS:

TM912

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yun Xiang
	Zhang Xin
	Wang Chao
	Fan Xingming

Cite this article:

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.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222007 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I2/595

[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 Electro-technical 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 decompo-sition[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 Electrotechnical 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 lithiumion 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]. Transa-ctions 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.