摘要
精确的锂电池荷电状态(SOC)在线估算可以有效地延长电池使用寿命,提高电池的安全性,对于电动汽车电池管理系统(BMS)而言至关重要。针对自适应扩展卡尔曼滤波(AEKF)算法运行初期收敛速度缓慢问题,该文提出模糊AEKF(FAEKF)算法可以改善收敛速度。以NCR18650B型三元锂电池的实际端电压与预测端电压差值的绝对值及其变化率作为模糊输入,以卡尔曼滤波器的系统测量噪声R作为模糊输出,通过对R进行模糊控制来调节算法在迭代过程中的增益K,进而实现收敛速度的模糊调节。实验结果表明,在0.5C倍率恒流放电工况和动态应力测试工况(DST)条件下,改进的算法相比于扩展卡尔曼(EKF)和AEKF算法,在不降低估算精度的情况下能够明显地提高收敛速度,在SOC在线估算中更具有实用性。
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.
作者
宫明辉
乌江
焦朝勇
Gong Minghui;Wu Jiang;Jiao Chaoyong(School of Electrical Engineering Xi’an Jiaotong University,Xi’an 710049 China;Nari Group Research Institute,Xi’an 710000 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2020年第18期3972-3978,共7页
Transactions of China Electrotechnical Society
基金
中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室开放基金资助项目(DGB51201801575)。
