基于RFF及GWO-PF的锂电池SOC估计

doi:10.3969/j.issn.1000-1158.2022.09.15

Abstract
Figure/Table
References (20)
Related Citation (15)

Download: PDF (3095 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In order to improve the accuracy of estimation of residual charge of lithium battery, a method of estimation of SOC of lithium battery based on online parameter identification and improved particle filter algorithm is proposed. Aiming at the problem of particle degradation in particle filtering, gray wolf optimization is introduced to optimize particle distribution with its strong global optimization ability to ensure particle diversity, effectively suppress particle degradation, and improve the filtering accuracy. The recursive least square method with forgetting factor is used to update the model parameters in real-time and run alternately with the improved particle filter algorithm to further improve the estimation accuracy of SOC. The experimental results show that the average estimation error of the improved algorithm is always less than ±0.15%. Compared with the extended Kalman filter and unscented Kalman filter, the improved algorithm has higher estimation accuracy and stability in the estimation of battery SOC.

Key words： metrology SOC estimation lithium battery particle filter grey wolf algorithm parameter identification

Received: 30 October 2020 Published: 19 September 2022

PACS:

TB971

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WU Zhong-qiang
	HU Xiao-yu
	MA Bo-yan
	HOU Lin-cheng
	CAO Bi-lian

Cite this article:

WU Zhong-qiang,HU Xiao-yu,MA Bo-yan, et al. SOC Estimation of Lithium Batteries Based on RFF and GWO-PF[J]. Acta Metrologica Sinica, 2022, 43(9): 1200-1207.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.09.15 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I9/1200

［1］刘淑杰, 郝昆昆, 王永, 等. 基于改进粒子滤波算法的动力锂离子电池荷电状态估计［J］. 大连理工大学学报, 2020, 60 (4): 392-401.
Liu S J, Hao K K, Wang Y, et al. Estimation of charged state of power lithium ion batteries based on improved particle filter algorithm ［J］. Journal of Dalian University of Technology, 2020, 60 (4): 392-401.
［2］陈则王, 杨丽文, 赵晓兵, 等. 基于改进无迹卡尔曼滤波的锂电池SOC在线估计［J］. 计量学报, 2019, 40 (1):40-48.
Chen Z W, Yang L W, Zhao X B, et al. Online SOC estimation of lithium batteries based on improved traceless kalman filter ［J］. Acta Metrologica Sinica, 2019, 40 (1):40-48.
［3］Zhou D, Zhang K, Ravey A, et al. Online estimation of lithium polymer batteries state-of-charge using particle filter-based data fusion with multi-models approach ［J］. IEEE Transactions on Industry Applications, 2016, 52 (3):2582-2595.
［4］陈则王, 李福胜, 林娅, 等. 基于GA-ELM的锂离子电池RUL间接预测方法［J］. 计量学报, 2020, 41 (6):735-742.
Chen Z W, Li F S, Li Y, et al. RUL indirect prediction method for lithium ion battery based on GA-ELM ［J］. Acta Metrologica Sinica, 2020, 41 (6):735-742.
［5］刘湘东, 刘承志, 杨梓杰, 等. 基于无迹卡尔曼滤波的全钒液流电池状态估计［J］. 中国电机工程学报, 2018, 38 (6):1769-1777+1914.
Liu X D, Liu C Z, Yang Z J, et al. State estimation of full vanadium fluid flow based on traceless kalman filter ［J］. Proceedings of the CSEE, 2018, 38 (6):1769-1777+1914.
［6］Guha A, Patra A. Online Estimation of the Electrochemical Impedance Spectrum and Remaining Useful Life of Lithium-Ion Batteries ［J］. IEEE Transactions on Instrumentation and Measurement, 2018, 67 (8):1836-1849.
［7］Fan B, Luan X Y, Zhang R, et al. Research on SOC Estimation Algorithm for Lithium Battery Based on EKF Algorithm and Ampere-hour Integration Method ［C］//Proceedings of the 2017 2nd Int-ernational Conference on Electrical, Control and Automa-tion Engineering (ECAE 2017). 2017.
［8］Liu F, Ma J, Su W X, et al. SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle ［J］. Journal of Central South University, 2019, 26 (6):1402-1415.
［9］吴忠强,王国勇,谢宗奎, 等. 基于WOA-UKF算法的锂电池容量与SOC联合估计［J］. 计量学报, 2022, 43(5): 649-656.
Wu Z Q, Wang G Y, Xie Z K, et al. Joint Estimation of the Capacity and SOC of Lithium Battery Based on WOA-UKF Algorithm［J］. Acta Metrologica Sinica, 2022, 43(5): 649-656.
［10］Lotfi N, Landers R G, Li J, et al. Reduced-Order Electrochemical Model-Based SOC Observer With Out-put Model Uncertainty Estimation ［J］. IEEE Transactions on Control Systems Technology, 2017, 25 (4):1217-1230.
［11］Zhang S W, Sun H, Lyu C. A method of SOC estimation for power Lion batteries based on equivalent circuit model and extended Kalman filter ［C］//2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). Wuhan, China, 2018:2683-2687.
［12］Cai T, Liu Y, He Z, et al. SOC estimation of Lithium-ion battery based on an Extended H-infinity filter ［C］//2019 IEEE 17th International Conference on Industrial Informatics (INDIN). Helsinki, Finland, 2019:1700-1705.
［13］Feng Y, Du J C, Xue C, et al. Estimation of SOC of Batteries Using Terminal Sliding-Mode Observer ［C］//2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Int-elligent Systems (CYBER). Tianjin, China, 2018:1260-1263.
［14］王海燕. 基于观测器的非线性高阶滑模电液位置鲁棒控制研究［J］. 中国工程机械学报, 2019, 17 (2):134-140.
Wang H Y. Robust control of nonlinear high-order sliding mode electro-hydraulic Position based on observer ［J］. Chinese Journal of Construction Machinery, 2019, 17 (2):134-140.
［15］毕军, 张栋, 常海涛, 等. 人工免疫粒子滤波算法估计电动汽车电池SOC ［J］. 交通运输系统工程与信息, 2015, 15 (5):103-108.
Bi J, Zhang D, Chang H T, et al. The artificial immunoparticle filter algorithm estimates the battery SOC of electric vehicle ［J］. Journal of Transportation Systems Engineering and Information Technology, 2015, 15 (5):103-108.
［16］ Song Y, Park M, Seo M. Improved SOC estimation of lithium-ion batteries with novel SOC-OCV curve estimation method using equivalent circuit model ［C］//2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). Split, Croatia, 2019: 1-6.
［17］张利, 刘帅帅, 刘征宇, 等. 锂离子电池自适应参数辨识与SOC估算研究［J］. 电子测量与仪器学报, 2016, 30 (1):45-51.
Zhang L, Liu S S, Liu Z Y, et al. Adaptive parameter identification and SOC estimation of Lithium ion batteries ［J］. Journal of Electronic Measurement and Instrumentation, 2016, 30 (1):45-51.
［18］Yu J, Tang Y, Chen X, et al. An adaptive resampling strategy in particle filter ［C］//2011 International Conference on Wavelet Analysis and Pattern Recognition. Guilin, China, 2011:144-149.
［19］张新明, 姜云, 刘尚旺, 等. 灰狼与郊狼混合优化算法及其聚类优化［J］. 自动化学报, 2020, 46(X): 1-17.
Zhang X M, Jiang Y, Liu S W, et al. Gray Wolf and coyote Hybrid Optimization algorithm and its Clustering optimization ［J］. Acta Automatica Sinica, 2020, 46(X): : 1-17.
［20］时浩添, 王顺利, 任哲昆, 等. 基于双指数拟合的电池PNGV模型改进方法研究［J］. 自动化仪表, 2019, 40 (5): 6-12.
Shi H T, Wang S L, Ren Z K, et al. Research on improvement method of PNGV model based on double exponential fitting ［J］. Process Automation Instrumentation, 2019, 40 (5): 6-12.