Abstract：Accurate online estimation of the state of charge (SOC) of the lithium battery can effectively extend the battery life and improve the safety of the battery, which is very important for the battery management system (BMS) of the electric vehicle. Aiming at the problem of slow convergence in the initial running of the adaptive extended Kalman filter (AEKF) algorithm, this paper proposes a fuzzy AEKF (FAEKF) algorithm to improve the convergence speed of the AEKF algorithm. Taking the absolute value of the difference between the actual terminal voltage and the predicted terminal voltage of the NCR18650B ternary lithium battery and its change rate as the fuzzy input, using the noise measured R in the Kalman filter system as the fuzzy output, and adjusting the gain K of the algorithm by fuzzy controlling R in the iterative process, then realize the fuzzy adjustment of the convergence speed. Experimental results show that compared with the extended Kalman (EKF) and AEKF algorithms under the conditions of 0.5C rate constant current discharge condition and dynamic stress test condition (DST), the improved algorithm can improve the convergence speed, while not reduce the estimation accuracy, which is more practical in the online estimation of SOC.
[1] Hu Xiaosong, Sun Fengchun.Fuzzy clustering based multi-model support vector regression state of charge estimator for lithium-ion battery of electric vehicle[C]// IEEE International Conference on Intelligent Human- Machine Systems and Cybernetics, Hangzhou, 2009: 392-396. [2] 孙亚辉, 耿云海, 方向, 等. 基于改进卡尔曼滤波的变参数迭代估计方法: 黑龙江, CN103473477A[P].2013-12-25. Sun Yahui, Geng Yunhai, Fang Xiang, et al. Variable parameter iterative estimation method based on improved Kalman filter: Heilongjiang, CN103473477A[P].2013- 12-25. [3] Done W.Use of the fast Kalman estimation algorithm for adaptive system identification[C]//IEEE Inter- national Conference on Acoustics, Speech, and Signal Processing, Atlanta, Georgia, USA, 1981: 886-889. [4] 秦振华. 用于自适应均衡的快速卡尔曼算法[J]. 系统工程与电子技术, 1998(1): 48-52. Qin Zhenhua.Fast Kalman algorithm for adaptive equalization[J]. System Engineering and Electronic Technology, 1998(1): 48-52. [5] 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 Elec- trotechnical Society, 2019, 34(18): 3937-3948. [6] 曲正伟, 董一兵, 王云静, 等. 用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法[J]. 电力系统自动化, 2018, 42(10): 87-92. Qu Zhengwei, Dong Yibing, Wang Yunjing, et al.Improved robust unscented Kalman filter algorithm for power system dynamic state estimation[J]. Auto- mation of Electric Power Systems, 2018, 42(10): 87-92. [7] 牛新亮, 赵国庆, 张永顺, 等. 可以稳定快速收敛的无源时差定位系统[J]. 仪器仪表学报, 2010, 31(5): 1114-1119. Niu Xinliang, Zhao Guoqing, Zhang Yongshun, et al.Stable fast convergence passive TDOA location system[J]. Chinese Journal of Scientific Instrument, 2010, 31(5): 1114-1119. [8] Mohammaddadi Gh, Pariz N, Karimpour A.Extended modal Kalman filter[J]. International Journal of Dynamics and Control, 2019, 7(3): 981-995. [9] 赵琳. 非线性系统滤波理论[M]. 北京: 国防工业出版社, 2012. [10] 李兆洋. 基于自适应卡尔曼滤波的GPS精密单点定位研究[D]. 成都: 西南交通大学, 2017. [11] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426. Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on com- prehensive Kalman filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426. [12] 张志勇, 张淑芝, 黄彩霞, 等. 基于自适应扩展卡尔曼滤波的分布式驱动电动汽车状态估计[J]. 机械工程学报, 2019, 55(6): 156-165. Zhang Zhiyong, Zhang Shuzhi, Huang Caixia, et al.State estimation of distributed drive electric vehicle based on adaptive extended Kalman filter[J]. Journal of Mechanical Engineering, 2019, 55(6): 156-165. [13] 张佳倩, 刘志虎. 基于模型参数辨识和扩展卡尔曼滤波的锂电池荷电状态估计[J]. 工业控制计算机, 2019, 32(9): 153-156. Zhang Jiaqian, Liu Zhihu.State of charge estimation of lithium-ion battery based on model parameter identification and extended Kalman filter[J]. Indu- strial Control Computer, 2019, 32(9): 153-156. [14] 李培强, 丰云鹤, 李欣然, 等. 考虑超短期负荷预测的储能电池参与电网一次调频控制策略[J]. 电力系统自动化, 2019, 43(19): 87-95, 148. Li Peiqiang, Feng Yunhe, Li Xinran, et al.Energy storage battery considering ultra-short-term load forecasting participates in primary frequency modu- lation control strategy of power grid[J]. Automation of Electric Power Systems, 2019, 43(19): 87-95, 148. [15] 吕航, 刘承志, 尹栋, 等. 深海动力磷酸铁锂电池组均衡方案设计优化[J]. 电工技术学报, 2016, 31(19): 232-239. Lü Hang, Liu Chengzhi, Yin Dong, et al.The design and optimize of equalization schemes for underwater power LiFePO4 battery stack[J]. Transactions of China Electrotechnical Society, 2016, 31(19): 232-239. [16] 温正, 孙华克. MATLAB智能算法[M]. 北京: 清华大学出版社, 2017. [17] 周湛杰, 王新生, 王岩. 基于模糊自适应算法的航天器姿态控制[J]. 电机与控制学报, 2019, 23(2): 123-128. Zhou Zhanjie, WangXinsheng, Wang Yan. Spacecraft attitude control based on fuzzy adaptive algorithm[J]. Electric Machines and Control, 2019, 23(2): 123-128. [18] 曾珂, 徐文立, 张乃尧. 特定Mamdani模糊系统的通用逼近性[J]. 控制与决策, 2000, 35(4): 435-438. Zeng Ke, Xu Wenli, Zhang Naiyao.Universal approximation of special Mamdani fuzzy systems[J]. Control and Decision, 2000, 35(4): 435-438. [19] 张振宇, 汪光森, 聂世雄, 等. 脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2019, 34(8): 1769-1779. Zhang Zhenyu, Wang Guangsen, Nie Shixiong, et al.State of charge estimation of LiFePO4 battery under the condition of high rate pulsed discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 1769-1779. [20] 刘伟龙, 王丽芳, 廖承林, 等. 充电模态下电动汽车动力电池模型辨识[J]. 电工技术学报, 2017, 32(11): 198-207. Liu Weilong, Wang Lifang, Liao Chenglin, et al.Parame- ters identification method of battery model for electric vehicles under the charging model[J]. Transactions of China Electrotechnical Society, 2017, 32(11): 198-207.
2020, Vol. 35 
