Abstract：The traditional state of charge (SOC) estimation algorithm does not consider the ambient temperature and aging state of the battery changes when using the lithium-ion battery pack for energy storage. In practical applications, the adaptability is poor, and the estimation accuracy is low. In recent years, several SOC estimation algorithms for battery packs have been proposed, taking into account temperature or battery aging. However, most remain incomplete and exhibit high computational complexity. This paper proposes a SOC estimation algorithm for lithium-ion battery packs used in energy storage, which considers temperature and aging, to enhance the accuracy and robustness of SOC estimation in battery packs under various ambient temperatures and aging states. First, the 100 A·h lithium iron phosphate battery produced by Zhongke De Fang (Hebei) New Energy Co., Ltd. is used as the research object, and its characteristics are tested under various temperatures and aging conditions. Depending on the characteristics of the selected battery, the second-order RC equivalent circuit model is chosen as the basic battery model. A nonlinear autoregressive neural network with external input (NARX) is chosen to replace the RC circuit in the second-order RC equivalent circuit model, and the NARX neural network structure is determined by training the model through actual working conditions data. Then, a battery model considering ambient temperature and battery aging is established. The working condition data is randomly selected from the enterprise database. SOC estimation is performed to verify the computation and accuracy of the optimized NARX neural network battery model. The experimental results show that although the convergence rate of the NARX neural network optimized battery model is slightly slower than that of the traditional second-order RC equivalent circuit model, the estimated error of the optimized battery model is much smaller than that of the conventional battery model. Therefore, the improved battery model performs better and is more suitable for SOC estimation of subsequent battery packs. Considering SOC and internal resistance differences, a battery pack mean difference model is proposed. An adaptive unscented Kalman filtering (AUKF) algorithm is selected to estimate the mean model and difference model, and the SOC of the difference model is modified to obtain the SOC of the single battery pack. A fuzzy control strategy determines the battery pack's SOC fusion weight. Finally, a hardware-in-the-loop simulation platform is built to verify the SOC estimation algorithm for battery packs. The proposed algorithm is compared with the traditional extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms at ambient temperatures of -20℃, 0℃, and 50℃, and for aging states of the battery of 90% and 80%. Experimental results demonstrate that the proposed algorithm ensures an average estimation error of less than 3% under various temperatures and aging operating conditions. Under any working conditions, the average estimation error of the proposed algorithm is lower than that of traditional EKF and UKF algorithms.
姬鹏, 吕泽旭. 考虑温度及老化的储能用锂离子电池组荷电状态估算算法[J]. 电工技术学报, 2025, 40(17): 5667-5682.
Ji Peng, Lü Zexu. SOC Estimation Algorithm of Lithium-Ion Battery Pack for Energy Storage Considering Temperature and Aging. Transactions of China Electrotechnical Society, 2025, 40(17): 5667-5682.
[1] 王新栋, 董政, 王书华, 等. 基于改进开路电压模型和自适应平方根无迹卡尔曼滤波的锂离子电池宽温度多工况SOC估计[J]. 电工技术学报, 2024, 39(24): 7950-7964. Wang XinDong, Dong Zheng, Wang Shuhua, et al. Wide temperature multi-condition SOC estimation for Li-ion batteries based on improved open circuit voltage model and adaptive square root untrace Kalman filter[J]. Transactions of China Electro-technical Society, 2024, 39(24): 7950-7964. [2] 宗梦然. 电网储能用电池管理系统设计与实现[D].郑州: 郑州大学, 2020. Zong Mengran.The design and implementation of battery management system for grid energy storage[D]. Zhengzhou: Zhengzhou University, 2020. [3] Zhou W, Zheng Y, Pan Z, et al.Review on the battery model and SOC estimation method[J]. Processes, 2021, 9(9): 1685. [4] Shi Meihua, Yuan Jingchao, Dong Li, et al.Com-bining physicochemical model with the equivalent circuit model for performance prediction and optimization of lead-acid batteries[J]. Electrochimica Acta, 2020, 353: 136567. [5] 刘萍, 李泽文, 蔡雨思, 等. 基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[J]. 电工技术学报, 2024, 39(10): 3232-3243. Liu Ping, Li Zewen, Cai Yusi, et al.Joint SOC and SOH estimation method based on the fusion of equivalent circuit model and data-driven model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243. [6] Yang Jufeng, Huang Wenxin, Xia Bing, et al.The improved open-circuit voltage characterization test using active polarization voltage reduction method[J]. Applied Energy, 2019, 237: 682-694. [7] Boujoudar Y, Elmoussaoui H, Lamhamdi T.Lithium-ion batteries modeling and state of charge estimation using artificial neural network[J]. International Journal of Electrical and Computer Engineering, 2019, 9(5): 3415. [8] 华菁, 阮观强, 胡星, 等. 基于TVFFRLS-ACKF的锂离子电池SOC估算[J].电子测量技术, 2022, 45(24): 22-28. Hua Jing, Ruan Guanqiang, Hu Xing, et al.SOC Estimation of lithium-ion battery based on TVFFLS-ACKF[J]. Electronic Measurement Technology, 2022, 45(24): 22-28. [9] 周韦润. 动力电池参数辨识和荷电状态估计研究[D]. 镇江: 江苏科技大学, 2019. Zhou Weirun.Power battery parameter identification and state of charge estimation[D]. Zhenjiang: Jiangsu University of Science and Technology, 2019. [10] Manh-Kien Tran, Anosh Mevawala, Satyam Panchal, et al.Effect of integrating the hysteresis component to the equivalent circuit model of lithium-ion battery for dynamic and non-dynamic applications[J]. Journal of Energy Storage, 2020, 32: 101785. [11] Luo Yong, Qi Pengwei, Kan Yingzhe, et al.State of charge estimation method based on the extended Kalman filter algorithm with consideration of time varying battery parameters[J]. International Journal of Energy Research, 2020, 44(13): 10538-10550. [12] Afshar S, Morris K, Khajepour A.State-of-charge estimation using an EKF-based adaptive observer[J]. IEEE Transactions on Control Systems Technology, 2018, 27(5): 1907-1923. [13] 钱伟, 王浩宇, 郭向伟, 等. 联合改进滑模观测器的自适应卡尔曼滤波荷电状态估计[J]. 电工技术学报, 2025, 40(6): 1984-1994. Qian Wei, Wang Haoyu, Guo Xiangwei, et al.Adaptive Kalman filter state of charge estimation for a combined improved sliding mode observer[J]. Transactions of China Electrotechnical Society, 2025, 40(6): 1984-1994. [14] Chen Kui, Zhou Shuyuan, Liu Kai, et al.State of charge estimation for lithium-ion battery based on whale optimization algorithm and multi-kernel relevance vector machine[J]. The Journal of Chemical Physics, 2023, 158(10): 104110. [15] 张君鸣. 基于神经网络方法的锂离子电池状态估计研究[D]. 济南: 山东大学, 2021. Zhang Junming.Research on state estimation of li-ion battery based on neural network[D]. Jinan: Shandong University, 2021. [16] 余杰, 廖思阳, 徐箭, 等. 考虑环境温度的磷酸铁锂电池SOC实时修正及频率控制方法[J]. 电工技术学报, 2023, 38(17): 4564-4573. Yu Jie, Liao Siyang, Xu Jian, et al.Real-time SOC correction and frequency control method for lithium iron phosphate batteries considering ambient tempera-ture[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4564-4573. [17] 靳立强, 孙志祥, 刘志茹, 等. 不同温度下锂电池剩余电量估算的仿真研究[J]. 汽车工程, 2019, 41(5): 590-598, 606. Jin Liqiang, Sun Zhixiang, Liu Zhiru, et al.Simulation research on estimation of residual charge of lithium battery at different temperatures[J]. Automotive Engineering, 2019, 41(5): 590-598, 606. [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 batteries based on adaptive CKF[J]. Energy Storage Science and Technology, 2020, 9(4): 1193-1199. [19] 刘光军, 吴思齐. 基于分段聚合和卡尔曼滤波的锂电池组SOC估算[J]. 江苏大学学报(自然科学版), 2024, 45(3): 281-285. Liu Guangjun, Wu Siqi.SOC estimation of lithium battery pack based on segmented polymerization and Kalman filter[J]. Journal of Jiangsu University (Natural Science Edition), 2024, 45(3): 281-285. [20] 申江卫, 刘伟强, 高承志, 等. 宽温度环境下基于迁移模型的锂电池组SOC估计[J]. 中国公路学报, 2024, 37(5): 383-396. Shen Jiangwei, Liu Weiqiang, Gao Chengzhi, et al.State of charge estimation of lithium battery packs in wide temperature environments based on migration model[J]. China Journal of Highway and Transport, 2024, 37(5): 383-396. [21] 张宇翔. 基于NARX神经网络的锂离子电池建模及SOC估计的研究[D]. 上海: 上海交通大学, 2019. Zhang Yuxiang.Lithium-ion battery modelling and SOC estimation based on NARX neural networks[D]. Shanghai: Shanghai Jiao Tong University, 2019. [22] 赵英杰, 张闯, 刘素贞, 等. 基于电极等效电路模型的锂离子电池无析锂快充策略优化研究[J]. 电工技术学报, 2024, 39(18): 5868-5882. Zhao Yingjie, Zhang Chuang, Liu Suzhen, et al.Optimization of fast charging strategy for lithium-ion batteries without dissolving lithium based on elec-trode equivalent circuit model[J]. Transactions of China Electrotechnical Society, 2024, 39(18): 5868-5882. [23] 陈玉伟, 顾钟凡, 李承澳, 等. 基于BP-AUKF算法和FFRLS的蓄电池SOC估计[J]. 电力设备管理, 2021(9): 211-213. Chen Yuwei, Gu Zhongfan, Li Cheng'ao, et al.Battery SOC estimation based on BP-AUKF algo-rithm and FFRLS[J]. Electric Power Equipment Management, 2021(9): 211-213. [24] 刘雨洋. 基于H∞ 滤波的锂电池SOC与SOP联合估计研究[D]. 绵阳: 西南科技大学, 2023. Liu Yuyang.Joint estimation of lithium battery SOC and SOP based on H∞ filtering[D]. Mianyang: Southwest University of Science and Technology, 2023. [25] 王文亮, 何锋, 蒋雪生, 等. 基于双扩展卡尔曼滤波锂电池荷电状态估算研究[J]. 电子测量技术, 2020, 43(19): 49-52. Wang Wenliang, He Feng, Jiang Xuesheng, et al.Research on charge state estimation of lithium battery based on double extended Kalman filter[J]. Electronic Measurement Technology, 2020, 43(19): 49-52. [26] Barcellona S, Piegari L.Lithium ion battery models and parameter identification techniques[J]. Energies, 2017, 10(12): 2007. [27] 徐鹏, 王潺, 万世斌, 等. 基于NARXNN变工况下锂离子电池SOC间接估计[J]. 电源技术, 2022, 46(10): 1161-1166. Xu Peng, Wang Chan, Wan Shibin, et al.Li-ion battery SOC indirect estimation under varying working profile based on NARX neural network[J]. Chinese Journal of Power Sources, 2022, 46(10): 1161-1166. [28] 秦潇涵. 基于循环神经网络的锂电池SOC估计[D].杭州: 杭州电子科技大学, 2020. Qin Xiaohan.SOC estimation for lithium ion batteries based on recurrent neural network[D]. Hangzhou: Hangzhou Dianzi University, 2020. [29] 候亚虹. 动力电池组不一致性建模及SOC融合估计研究[D]. 西安: 长安大学, 2021. Hou Yahong.Inconsistency modeling of power battery pack and SOC fusion estimation[D]. Xi'an: Changan University, 2021. [30] 何志刚, 李尧太, 盘朝奉, 等. 基于AIUKF的锂离子电池SOC估算[J]. 电源技术, 2020, 44(4): 518-521. He Zhigang, Li Yaotai, Pan Chaofeng, et al.Lithium-ion battery SOC Estimation based on AIUKF[J]. Chinese Journal of Power Sources, 2020, 44(4): 518-521. [31] Lin Xinyou, Tang Yunliang, Ren Jing, et al.State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model[J]. Journal of Energy Storage, 2021, 41: 102840. [32] 高文帅. 基于T-S模糊神经网络的Hammerstein模型预测控制[D]. 抚顺: 辽宁石油化工大学, 2020. Gao Wenshuai.Predictive control of Hammerstein model based on T-S fuzzy neural network[D]. Fushun: Liaoning Shihua University, 2020.
2025, Vol. 40 
