State of Charge Estimation of Lithium Titanate Battery for Electric Multiple Units
Hao Wenmei1, Zhang Liwei1, Peng Bo1, Cai Jiao1, Yang Rui2
1. School of Electrical Engineering Beijing Jiaotong Universit Beijing 100044 China; 2. China Waterborne Transport Research Institute MOT Beijing 100088 China
Abstract：As an emergency power supply, the battery is widely used in the auxiliary power supply system of electric multiple units (EMU). The charged state of the battery can reflect the remaining power of the battery in the charging and discharging process, which is an important parameter of the EMU battery management system. Accurate estimation of battery charged state can effectively improve the service efficiency and service life of the battery. However, the chemical reaction inside the battery is complex and difficult to measure, and most of the current charged state of the battery can only be obtained by indirect measurement and calculation. In this paper, Lithium Titanate battery of China standard EMU was taken as the research object, the second-order RC equivalent circuit model was established, and the online identification method based on Extended Kalman filtering (EKF) was studied, and the identification accuracy was compared through battery working condition experiments. In order to overcome the disadvantage of fixed noise variance in the process of conventional Kalman filtering, an adaptive Kalman filtering method was proposed to estimate the battery charged state, and its accuracy in estimating the battery charged state was verified in Matlab.
郝文美, 张立伟, 彭博, 蔡娇, 杨瑞. 动车组钛酸锂电池荷电状态估计[J]. 电工技术学报, 2021, 36(zk1): 362-371.
Hao Wenmei, Zhang Liwei, Peng Bo, Cai Jiao, Yang Rui. State of Charge Estimation of Lithium Titanate Battery for Electric Multiple Units. Transactions of China Electrotechnical Society, 2021, 36(zk1): 362-371.
[1] 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. [2] 孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674. Sun Bingxiang, Ren Pengbo, Chen Yuzhe, et al.Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transa- ctions of China Electrotechnical Society, 2021, 36(3): 666-674. [3] 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 Fang, Ma Jie, Su Weixing, et al.State of charge estimation method of electric vehicle power battery life cycle based on auto regression extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707. [4] 颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948. Yan Xiangwu, Deng Haoran, Guo Qi, et al.Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electro- technical Society, 2019, 34(18): 3937-3948. [5] 范兴明, 王超, 张鑫, 等. 基于增量学习相关向量机的锂离子电池SOC预测方法[J]. 电工技术学报, 2019, 34(13): 2700-2708. Fan Xingming, Wang Chao, Zhang Xin, et al.A prediction method of Li-ion batteries SOC based on incremental learning relevance vector machine[J]. Transactions of China Electrotechnical Society, 2019, 34(13): 2700-2708. [6] Waag W, Fleischer C, Sauer D U.Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles[J]. Journal of Power Sources, 2014, 258(14): 321-339. [7] 蔡飞, 王顺利. 基于卡尔曼滤波的锂离子电池组SOC估算方法研究[J]. 电源世界, 2015(6): 39-41. Cai Fei, Wang Shunli.Lithium-ion battery pack SOC estimation method study based on Kalman filter[J]. The World of Power Supply, 2015(6): 39-41. [8] 程泽, 杨磊, 孙幸勉. 基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J]. 中国电机工程学报, 2018, 38(8): 2384-2393, 2548. Cheng Ze, Yang Lei, Sun Xingmian.State of charge and sate of health estimation of Li-ion batteries based on adaptive square-root unscented Kalman filters[J]. Proceedings of the CSEE, 2018, 38(8): 2384-2393, 2548. [9] Ramadan H S, Becherif M, Claude F.Extended Kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis[J]. International Journal of Hydrogen Energy, 2017, 42: 29033-29046. [10] Campestrini C, Heil T, Kosch S, et al.A comparative study and review of different Kalman filters by applying an enhanced validation method[J]. Journal of Energy Storage, 2016, 8: 142-159. [11] 赵佳美. 基于二阶EKF的锂离子电池SOC估计的建模与仿真[D]. 西安: 西安科技大学, 2018. [12] 何瑢. 动力电池SOC估算及充电优化策略的研究[D]. 西安: 长安大学, 2018. [13] Sbarufatti C, Corbetta M, Giglio M, et al.Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks[J]. Journal of Power Sources, 2017, 344(3): 128-140. [14] Yang Yang, Tang Taofeng, Qin Datong, et al.PNGV equivalent circuit model and SOC estimation algorithm of lithium batteries for electric vehicle[J]. Journal of System Simulation, 2012, 24(4): 202-206. [15] Dong Xile, Zhang Caiping, Jiang Jiuchun.Evaluation of SOC estimation method based on EKF/AEKF under noise interference[J]. Energy Procedia, 2018, 152: 520-525. [16] Wang Junping, Guo Jingang, Lei Ding.An adaptive Kalman filtering based state of charge combined estimator for electric vehicle battery pack[J]. Energy Conversion & Management, 2009, 50: 3182-3186. [17] Song Qingping, Liu Rongke.Weighted adaptive filtering algorithm for carrier tracking of deep space signal[J]. Chinese Journal of Aeronautics, 2015, 28(4): 1236-1244.
2021, Vol. 36 
