作者：李小虎¹, 王军^1,2

作者单位：1. 苏州科技大学，江苏 苏州 215009;
2. 中国科学院长春光学精密机械与物理研究所，吉林 长春 130033

关键词：锂电池;荷电状态;带遗忘因子的最小二乘法;奇异值分解;无迹卡尔曼滤波

摘要：

针对无迹卡尔曼滤波算法（UKF）估算锂电池荷电状态（SOC）存在的精度低、稳定性差的问题，在二阶模型的基础上，提出一种基于奇异值分解（SVD）的改进无迹卡尔曼滤波算法。建立锂电池的数学模型，通过带遗忘因子的最小二乘法（FFRLS）得到电池模型参数，将辨识出的模型参数实时导入改进UKF算法中，估计锂电池的荷电状态，并与UKF进行比较。在DST工况下，通过仿真实验可知，与UKF相比，SVD-UKF算法的AAE降低3.29%，RMSE降低3.78%。实验结果表明，改进算法的SOC估算精度和自适应性能更高。

SOC estimation of lithium battery based on improved unscented Kalman filter
LI Xiaohu¹, WANG Jun^1,2
1. Suzhou University of Science and Technology, Suzhou 215009, China;
2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Abstract: Aiming at the problems of low accuracy and poor stability of unscented Kalman filter algorithm in estimating the state of charge of lithium battery, an improved unscented Kalman filter algorithm based on singular value decomposition is proposed on the basis of the second-order model. Firstly, the mathematical model of lithium battery is established, and the battery model parameters are obtained by the least square method with forgetting factor. The identified model parameters are introduced into the improved unscented Kalman filter algorithm in real time to estimate the charge state of lithium battery and compared with the unscented Kalman filter algorithm. Under DST condition, the simulation results show that compared with UKF, the AAE of SVD-UKF algorithm is reduced by 3.29% and the RMSE is reduced by 3.78%. Experimental results show that the SOC estimation accuracy and adaptive performance of the improved algorithm are higher.
Keywords: lithium battery;state of charge;forgetting factor recursive least square;singular value decomposition;unscented Kalman filter
2023, 49(1):105-110,130  收稿日期: 2021-09-07;收到修改稿日期: 2021-10-27
基金项目: “十三五”江苏省重点学科项目（20168765）；江苏省研究生科研创新项目（KYCX17_2060）
作者简介: 李小虎(1998-),男,安徽滁州市人,硕士研究生,专业方向为建筑环境中的储能设备
参考文献
[1] WANG X, XU J, CAO B, et al. A Kalman filter SOC estimation method for lithium-ion batteries based on discrete wavelet transform de-noising[J]. Journal of Xi'an Jiaotong University, 2017, 51(10): 71-76
[2] 丁镇涛, 邓涛, 李志飞, 等. 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究[J]. 中国机械工程, 2020, 31(15): 1823-1830
[3] MENG J, RICCO M, LUO G, et al. An overview and comparison of online implementable SOC Estimation methods for lithium-ion battery[J]. IEEE Transactions on Industry Applications, 2017, 54(2): 1583-1591
[4] 刘新天, 刘兴涛, 何耀, 等. 基于Vmin-EKF 的动力锂电池组SOC 估计[J]. 控制与决策, 2010, 25(3): 445-448
[5] ZHANG C, JIANG J, ZHANG L, et al. A generalized SOC-OCV model for lithium-ion batteries and the SOC estimation for LNMCO battery[J]. Energies, 2016, 9(11): 900
[6] GUO Y, ZHAO Z, HUANG L. SOC estimation of Lithium battery based on improved BP neural network[J]. Energy Procedia, 2017, 105: 4153-4158
[7] 吴从秀, 耿攀, 鲁东冉, 等. 动力锂电池荷电状态估算的改进方法[J]. 科学技术与工程, 2019, 19(14): 201-206
[8] 董祥祥, 武鹏, 葛传久, 等. 基于自适应无迹卡尔曼滤波的锂电池荷电状态估计[J]. 电工电能技术, 2020, 40(2): 58-66
[9] 章军辉, 李庆, 陈大鹏, 等. 基于快速 SR-UKF 的锂离子动力电池 SOC 联合估计[J]. 工程科学学报, 2021, 43(7): 976-984
[10] 陈万, 蔡艳平, 苏延召, 等. 基于改进粒子滤波的锂离子电池剩余寿命预测[J]. 中国测试, 2021, 47(7): 148-153
[11] DONG X L, ZHANG A P, JIANG J C. Evaluation of SOC estimation method based on EKF / AEKF under noise interference[J]. Energy Procedia, 2018, 152: 520-525
[12] 陈息坤, 孙 冬. 锂离子电池建模及其参数辨识方法研究[J]. 中国电机工程学报, 2016, 36(22): 6254-6261