基于WOA-UKF算法的锂电池容量与SOC联合估计

doi:10.3969/j.issn.1000-1158.2022.05.14

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摘要锂电池的荷电状态(SOC)和有效容量是表征电池当前剩余电量和电池寿命的重要参数,提出一种锂离子电池有效容量和SOC的联合估计方法。在电池全寿命周期内,给出一种开路电压与SOC和电池有效容量非线性模型的两变量多项式描述;当电池循环使用次数超过预设值,采用鲸鱼优化算法估计当前电池容量与电池模型参数,根据模型参数与容量值采用无迹卡尔曼滤波器估计电池SOC;在SOC估计过程中,采用鲸鱼优化算法更新无迹卡尔曼滤波器的观测噪声方差和过程噪声方差,实现噪声方差的自适应调节,进而提高估计精度。实验结果验证了该方法的有效性和联合估计方案的可行性。

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	吴忠强
	王国勇
	谢宗奎
	何怡林
	卢雪琴

关键词 ：计量学, 荷电状态, 有效容量, 锂电池, 无迹卡尔曼滤波器, 鲸鱼优化算法, 联合估计

Abstract：The state of charge (SOC) and effective capacity of lithium batteries are important parameters to characterize the current remaining capacity and life of the battery. A joint estimation method for the effective capacity and SOC of lithium-ion batteries is proposed. During the battery life cycle, a two-variable polynomial description for the non-linear model of open circuit voltage to SOC and battery effective capacity is given; when the number of battery cycles exceeds a preset value, whale optimization algorithm is used to estimate the current battery capacity and battery model parameters, and then an unscented Kalman filter is used to estimate the SOC of battery according to the model parameters and capacity values. In the estimation process of SOC, the whale optimization algorithm is used to update the noise variance of unscented Kalman filter, furthermore, the estimation accuracy is improved. Experimental results test the effectiveness of the method and the feasibility of the joint estimation scheme.

Key words： metrology state of charge effective capacity;lithium battery unscented Kalman filter whale optimization algorithm joint estimation

收稿日期: 2020-08-10 发布日期: 2022-05-18

PACS:

TB971

基金资助:河北省自然科学基金项目(F2020203014)

通讯作者: 王国勇(1995-),男,山东潍坊人,燕山大学硕士研究生,主要研究方向为动力电池的SOC估计。Email: 3026179994@qq.com E-mail: mewzq@163.com

作者简介: 吴忠强(1966-),男,上海人,燕山大学教授,主要从事动力电池SOC估计方面的研究。Email: mewzq@163.com

引用本文:

吴忠强,王国勇,谢宗奎,何怡林,卢雪琴. 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 Zhong-qiang,WANG Guo-yong,XIE Zong-kui,HE Yi-lin,LU Xue-qin. Joint Estimation of the Capacity and SOC of Lithium Battery Based on WOA-UKF Algorithm. Acta Metrologica Sinica, 2022, 43(5): 649-656.

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http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.05.14 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I5/649

［1］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. On-line estimation of lithium battery SOC based on improved unscented Kalman filter ［J］. Acta Metrologica Sinica, 2019, 40(1): 40-48.
［2］刘征宇, 朱诚诚, 尤勇, 等. 面向SOC估计的计及温度和循环次数的锂离子电池组合模型［J］. 仪器仪表学报, 2019, 40(11): 117-127.
Liu Z Y, Zhu C C, You Y, et al. Lithium-ion battery combination model considering temperature and cycle times for SOC estimation ［J］. Chinese Journal of Scientific Instrument, 2019, 40(11): 117-127.
［3］吴忠强, 申丹丹, 尚梦瑶, 等. 基于改进蝗虫优化算法的光伏电池模型参数辨识［J］. 计量学报, 2020, 41(12): 1536-1543.
Wu Z Q, Shen D D, Shang M Y, et al. Parameter identification of photovoltaic cell model based on improved locust optimization algorithm ［J］. Acta Metrologica Sinica, 2020, 41(12): 1536-1543.
［4］Li X J, Li J Q, Xu L F, et al. Online management of lithium-ion battery based on time-triggered controller area network for fuel-cell hybrid vehicle applications ［J］. Journal of Power Sources, 2009, 195(10): 3338-3343.
［5］Yu Q Q, Xiong R, Wang L Y, et al. A comparative study on open circuit voltage models for lithium-ion batteries ［J］. Chinese Journal of Mechanical Engineering, 2018, 31(4): 84-91.
［6］吕延卓, 肖明清, 唐希浪, 等. 基于置信规则库的锂电池SOC估计［J］. 空军工程大学学报(自然科学版), 2019, 20(4): 39-45.
Lv Y Z, Xiao M Q, Tang X L, et al. Lithium battery SOC estimation based on confidence rule base ［J］. Journal of Air Force Engineering University (Natural Science Edition), 2019, 20(4): 39-45.
［7］Luzi M, Mascioli F M F, Paschero M, et al. A white-box equivalent neural network circuit model for SOC estimation of electrochemical cells ［J］. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(2): 371-382.
［8］刘芳, 马杰, 苏卫星, 等. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法［J］. 电工技术学报, 2020, 35(4): 698-707.
Liu F, Ma J, Su W X, et al. A state-of-charge estimation method for the full life cycle of electric vehicle power batteries based on adaptive regression extended Kalman filter ［J］. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707.
［9］廖勇, 张楠, 姚海梅, 等. 高速场景下基于叠加导频的迭代EKF信道估计方法［J］. 电子学报, 2019, 47(11): 2399-2406.
Liao Y, Zhang N, Yao H M, et al. Iterative EKF channel estimation method based on superimposed pilots in high-speed scenarios ［J］. Acta Electronica Sinica, 2019, 47(11): 2399-2406.
［10］吴忠强, 尚梦瑶, 申丹丹, 等. 基于神经网络和MS-AUKF算法的蓄电池荷电状态估计［J］. 中国电机工程学报, 2019, 39(21): 6336-6344.
Wu Z Q, Shang M Y, Shen D D, et al. Estimation of battery state of charge based on neural network and MS-AUKF algorithm ［J］. Proceedings of the CSEE, 2019, 39(21): 6336-6344.
［11］李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法［J］. 电工技术学报, 2020, 35(9): 2051-2062.
Li C R, Xiao F, Fan Y X, et al. Lithium-ion battery state of charge estimation method based on the fusion method of gated recurrent unit neural network and Huber-M estimation robust Kalman filter ［J］. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
［12］Chen L. Remaining useful life prediction of battery using a novel indicator and framework with fractional grey model and unscented particle filter ［J］. IEEE Transactions on Power Electronics, 2020, 35(6): 5850-5859.
［13］Chen Y, He Y, Li Z, et al. Remaining useful life prediction and state of health diagnosis of lithium-ion battery based on second-order central difference particle filter ［J］. IEEE Access, 2020, 8: 37305-37313.
［14］贺林, 胡敏康, 石琴, 等. 一种分阶段锂离子电池荷电状态估计算法［J］. 电力电子技术, 2020, 54(2): 8-11.
He L, Hu M K, Shi Q, et al. A staged lithium-ion battery state of charge estimation algorithm ［J］. Power Electronical Technology, 2020, 54(2): 8-11.
［15］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, Lin Y, et al. Indirect prediction method for RUL of lithium-ion batteries based on GA-ELM ［J］. Acta Metrologica Sinica, 2020, 41(6): 735-742.
［16］Zeng M M, Zhang P, Yang Y, et al. SOC and SOH joint estimation of the power batteries based on fuzzy unscented Kalman filtering algorithm ［J］. Energies, 2019, 12(16): 3122-3136.
［17］Zhang C, Xiao J P, Shu X, et al. Model-based adaptive joint estimation of the state of charge and capacity for lithium-ion batteries in their entire lifespan ［J］. Energies, 2020, 13(6): 1410-1424.
［18］Mirjalili S, Lewis A. The whale optimization algorithm ［J］. Advances in Engineering Software, 2016, 95: 51-67.
［19］滕德云, 滕欢, 刘鑫, 等. 考虑多个分布式电源接入配电网的多目标无功优化调度［J］. 电测与仪表, 2019, 56(13): 39-44.
Teng D Y, Teng H, Liu X, et al. Multi-objective reactive power optimal dispatch considering multiple distributed power sources connected to the distribution network ［J］. Electrical Measurement & Instrumentation, 2019, 56(13): 39-44.
［20］褚鼎立, 陈红, 王旭光. 基于自适应权重和模拟退火的鲸鱼优化算法［J］. 电子学报, 2019, 47(5): 992-999.
Chu D L, Chen H, Wang X G. Whale optimization algorithm based on adaptive weights and simulated annealing ［J］. Acta Electronics Sinica, 2019, 47 (5): 992-999.
［21］郑威迪, 李志刚, 贾涵中, 等. 基于改进型鲸鱼优化算法和最小二乘支持向量机的炼钢终点预测模型研究［J］. 电子学报, 2019, 47(3): 700-706.
Zheng W D, Li Z G, Jia H Z, et al. Research on steelmaking end point prediction model based on improved whale optimization algorithm and least squares support vector machine ［J］. Acta Electronica Sinica, 2019, 47(3): 700-706.
［22］李晓帆, 于少娟. 基于改进的AUKF锂离子电池荷电状态估计［J］. 计算机仿真, 2019, 36(9): 120-125.
Li X F, Yu S J. Based on the improved AUKF lithium-ion battery state of charge estimation ［J］. Computer Simulation, 2019, 36(9): 120-125.
［23］杨峰, 郑丽涛, 王家琦, 等. 双层无迹卡尔曼滤波［J］. 自动化学报, 2019, 45(7): 1386-1391.
Yang F, Zheng L T, Wang J Q, et al. Double-layer unscented Kalman filter ［J］. Acta Automatica Sinica, 2019, 45(7): 1386-1391.
［24］Saha B, Goebel K. Battery Data Set: NASA Ames prognostics data repository ［EB/OL］. (2007). http://ti.arc.nasa.gov/project/prognostic-data-repository.