锂离子电池建模及其参数辨识方法研究  

Research on Lithium-ion Battery Modeling and Model Parameter Identification Methods

作　　者：陈息坤[1] 孙冬[1] 

机构地区：[1]上海大学机电工程与自动化学院,上海市闸北区200072

出　　处：《中国电机工程学报》2016年第22期6254-6261,共8页Proceedings of the CSEE

基　　金：上海市科委重点项目(14DZ1206302);上海市经信委重大技术装备项目(ZB-ZBYZ-02-14-0825)~~

摘　　要：建立精确的锂电池动态模型是保证锂电池储能系统安全、可靠运行的前提,针对建模过程中检测量伴有不确定性噪声信号以及最小二乘法出现数据饱和、辨识参数有偏差等问题,基于锂电池RC等效电路,提出采用偏差补偿最小二乘法在线辨识模型参数,与常规最小二乘法进行对比研究;依据3种常用锂电池开路电压测试方法,设计6个典型OCV测试实验,研究基于分段三次Hermite插值法的开路电压曲线辨识问题。实验结果表明:基于偏差补偿的最小二乘法具有较好的算法收敛性,所建模型精度更高、性能较好,验证了此方法的可行性和有效性;对比分析不同开路电压测试方法及其曲线建模性能,建模性能相当、耗时较少、可实现性较好的恒流充放电间歇法是最优之选。In lithium-ion battery power management systems, dynamic modeling accurately is the key techniques that maintain the battery energy storage system operating safely and reliably. For the issue about test data with uncertain noise signals, data saturation and biased identification parameters, the bias compensation recursive least-squares（BCRLS） method was used to identify model parameters online based on the second-order RC equivalent model. The proposed method were validated in four different tests and compared with the conventional RLS method. According to the three commonly used open circuit voltage（OCV） test methods, six typical OCV test profiles were designed and then the piecewise cubic Hermite interpolation was proposed for improvement of the identified OCV curves. The experimental results show that BCRLS algorithm has better convergence than RLS and the obtained model has higher precision and better performance, hence the feasibility and effectiveness of the proposed method was verified. Through comparative analysis of the modeling performance of the identified OCV curves, the OCV test method, constant current charging and discharging with short-time rest test profile, is considered as the preferred choice due to its less duration time and equivalent modeling performance.

关 键 词：锂电池建模 模型参数辨识 开路电压 偏差补偿最小二乘法 分段三次Hermite插值 

分 类 号：TM912[电气工程—电力电子与电力传动]