Joint Estimation of SOC and Usable Capacity of Lithium-Ion Battery with Wide Temperature and Full Life Based on Migration Model
Shen Jiangwei1, Gao Chengzhi1, Shu Xing1, Liu Yonggang2, Chen Zheng1
1. Faculty of Transportation Engineering Kunming University of Science and Technology Kunming 650000 China; 2. College of Mechanical Engineering Chongqing University Chongqing 400030 China
Abstract：At present, the traditional equivalent circuit model is widely used in the research of joint estimation of state of charge (SOC) and available capacity. However, this method seriously depends on the model accuracy, and is affected by temperature changes and battery aging, which leads to the decline of estimation accuracy of SOC and available capacity in the whole life cycle and under complex temperature environment. To solve the above problems, this paper integrates the weight selection particle filter algorithm on the basis of the migration model theory, and studies the estimation of SOC and usable capacity in the wide temperature and full life range of lithium-ion batteries. The proposed method can greatly reduce the amount of data required for modeling, and the estimated SOC value has the advantages of high accuracy and strong ability to correct the initial value error. First of all, this paper selects the traditional second-order RC equivalent circuit model as the basic model for building the migration model, the recursive least square method with forgetting factor is employed to identify the model parameters, and further uses polynomials to fit the correlation between the model parameters and SOC to complete the construction of the migration model. Secondly, the weight selection particle filter algorithm is used to realize the online migration of migration factors and complete the online determination of the real parameter information of the model. Further, the inaccurate SOC value can be migrated to obtain the real SOC value under the influence of temperature and aging. Finally, the available capacity estimation is realized by capacity inversion based on the obtained SOC estimation value. In order to verify the estimation accuracy of the migration model for the battery SOC in a wide temperature environment, UDDS cycle condition experiments were conducted at -10℃, 10℃, 30℃ and 50℃ respectively. In addition, the initial value of SOC in the algorithm was set to 75%, while the actual initial value of the battery SOC was 100%, to verify the ability to correct the initial error. The results show that under the wide temperature environment of -10℃, 10℃, 30℃ and 50℃, the root mean square error (RMSE) of SOC estimation obtained by the migration model is 2.22%, 2.07, 2.29% and 1.83% respectively, and the time taken to converge to the true value within the 2% error band is 550s, 700s, 750s and 600s respectively, which shows that the migration model has a high estimation accuracy and a good correction ability for the initial value error in estimating SOC in a wide temperature range. At the same time, in order to verify the estimation accuracy of the battery SOC of the migration model under different aging conditions during the whole life cycle, it was verified based on the data of UDDS operating conditions in the initial aging stage (SOH=96.7%), moderate aging (SOH=93.1%) and late aging stage (SOH=85.1%) of the lithium-ion battery. The results show that under three different aging conditions, the RMSE of SOC estimation results based on migration model is 1.66%, 1.84% and 1.99%, respectively, indicating that the proposed migration model based SOC estimation method has high accuracy in the whole life range. Finally, this paper uses the aging experimental data of the selected battery to verify the battery capacity estimation results at different aging stages every 50 cycles. The capacity estimation results show that the maximum error between the real capacity and the model estimated capacity at different aging stages in the battery life cycle is 0.047A·h, less than 2%. It shows that the capacity estimation method proposed in this paper has high estimation accuracy.
申江卫, 高承志, 舒星, 刘永刚, 陈峥. 基于迁移模型的锂离子电池宽温度全寿命SOC与可用容量联合估计[J]. 电工技术学报, 2023, 38(11): 3052-3063.
Shen Jiangwei, Gao Chengzhi, Shu Xing, Liu Yonggang, Chen Zheng. Joint Estimation of SOC and Usable Capacity of Lithium-Ion Battery with Wide Temperature and Full Life Based on Migration Model. Transactions of China Electrotechnical Society, 2023, 38(11): 3052-3063.
[1] 索鎏敏, 李泓. 锂离子电池过往与未来[J]. 物理, 2020, 49(1): 17-23. Suo Liumin, Li Hong.The past, present and future of lithium ion batteries[J]. Physics, 2020, 49(1): 17-23. [2] Zhang Xu, Wang Yujie, Yang Duo, et al.An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model[J]. Energy, 2016, 115: 219-229. [3] Liu Kailong, Li Kang, Peng Qiao, et al.A brief review on key technologies in the battery management system of electric vehicles[J]. Frontiers of Mechanical Engineering, 2019, 14(1): 47-64. [4] Weng Caihao, Cui Yujia, Sun Jing, et al.On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235: 36-44. [5] 程泽, 吕继考, 刘继光, 等. 等效滞回模型在锂离子电池SOC估计中的应用[J]. 湖南大学学报(自然科学版), 2015, 42(4): 63-70. Cheng Ze, Lv Jikao, Liu Jiguang, et al.Application of equivalent hysteresis model in estimaion of state of charge of lithium-ion battery[J]. Journal of Hunan University (Natural Sciences), 2015, 42(4): 63-70. [6] Sun Li, Li Guanru, You Fengqi.Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer[J]. Renewable and Sustainable Energy Reviews, 2020, 131: 109994. [7] 胡晓松. 电动车辆锂离子电池模型辨识、优化与状态估计[D]. 北京: 北京理工大学, 2012. [8] Shi Meihua, Yuan Jingchao, Dong Li, et al.Combining physicochemical model with the equivalent circuit model for performance prediction and optimization of lead-acid batteries[J]. Electrochimica Acta, 2020, 353: 136567. [9] 武龙星, 庞辉, 晋佳敏, 等. 基于电化学模型的锂离子电池荷电状态估计方法综述[J]. 电工技术学报, 2022, 37(7): 1703-1725. Wu Longxing, Pang Hui, Jin Jiamin, et al.A review of SOC estimation methods for lithium-ion batteries based on electrochemical model[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1703-1725. [10] Xiong Rui, He Hongwen, Sun Fengchun, et al.Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach[J]. IEEE Transactions on Vehicular Technology, 2013, 62(1): 108-117. [11] 来鑫, 李云飞, 郑岳久, 等. 基于SOC-OCV优化曲线与EKF的锂离子电池荷电状态全局估计[J]. 汽车工程, 2021, 43(1): 19-26. Lai Xin, Li Yunfei, Zheng Yuejiu, et al.An overall estimation of state-of-charge based on SOC-OCV optimization curve and EKF for lithium-ion battery[J]. Automotive Engineering, 2021, 43(1): 19-26. [12] Vasebi A, Partovibakhsh M, Bathaee S M T. A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications[J]. Journal of Power Sources, 2007, 174(1): 30-40. [13] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964. Xie Changjun, Fei Yalong, Zeng Chunnian, et al.State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3958-3964. [14] 熊巍, 梅华平, 徐刚, 等. 基于改进H无穷滤波的锂离子电池SOC估计[J]. 电源技术, 2020, 44(10): 1488-1491, 1528. Xiong Wei, Mei Huaping, Xu Gang, et al.Lithium-ion battery SOC estimation based on improved H-infinity filter[J]. Chinese Journal of Power Sources, 2020, 44(10): 1488-1491, 1528. [15] 庞辉, 郭龙, 武龙星, 等. 考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算[J]. 电工技术学报, 2021, 36(10): 2178-2189. Pang Hui, Guo Long, Wu Longxing, et al.An improved dual polarization model of Li-ion battery and its state of charge estimation considering ambient temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2178-2189. [16] 宫明辉, 乌江, 焦朝勇. 基于模糊自适应扩展卡尔曼滤波器的锂电池SOC估算方法[J]. 电工技术学报, 2020, 35(18): 3972-3978. Gong Minghui, Wu Jiang, Jiao Chaoyong.SOC estimation method of lithium battery based on fuzzy adaptive extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3972-3978. [17] 刘新天, 彭泳, 何耀, 等. 基于温度补偿的动力锂电池SOC估算方法[J]. 计算机仿真, 2021, 38(5): 66-69, 328. Liu Xintian, Peng Yong, He Yao, et al.State of charge estimation of lithiumion power battery considering temperature influence[J]. Computer Simulation, 2021, 38(5): 66-69, 328. [18] 郑涛, 张里, 侯杨成, 等. 基于自适应CKF的老化锂电池SOC估计[J]. 储能科学与技术, 2020, 9(4): 1193-1199. Zheng Tao, Zhang Li, Hou Yangcheng, et al.SOC estimation of aging lithium battery based on adaptive CKF[J]. Energy Storage Science and Technology, 2020, 9(4): 1193-1199. [19] Tang Xiaopeng, Wang Yujie, Zou Changfu, et al.A novel framework for lithium-ion battery modeling considering uncertainties of temperature and aging[J]. Energy Conversion and Management, 2019, 180: 162-170. [20] Tang Xiaopeng, Wang Yujie, Yao Ke, et al.Model migration based battery power capability evaluation considering uncertainties of temperature and aging[J]. Journal of Power Sources, 2019, 440: 227141. [21] 李亚滨, 袁学庆, 汤琦, 等. 基于权值选择粒子滤波的锂电池RUL预测研究[J]. 计算机仿真, 2020, 37(11): 75-80. Li Yabin, Yuan Xueqing, Tang Qi, et al.Research on RUL prediction of lithium-ion batteries based on weight select particle filter[J]. Computer Simulation, 2020, 37(11): 75-80. [22] Farmann A, Sauer D U.Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles[J]. Applied Energy, 2018, 225: 1102-1122. [23] Krishnan S R, Seelamantula C S.On the selection of optimum savitzky-golay filters[J]. IEEE Transactions on Signal Processing, 2013, 61(2): 380-391. [24] Press W H, Teukolsky S A.Savitzky-golay smoothing filters[J]. Computers in Physics, 1990, 4(6): 669-672.
2023, Vol. 38 
