锂离子电池SOC与模型参数联合估算研究  

Research on joint estimation for SOC and model parameters of Li-ion battery

作　　者：张利[1,2] 朱雅俊[1] 刘征宇[1,2] 

机构地区：[1]合肥工业大学机械与汽车工程学院,合肥230009 [2]安全关键工业测控技术教育部工程研究中心,合肥230009

出　　处：《电子测量与仪器学报》2012年第4期320-324,共5页Journal of Electronic Measurement and Instrumentation

基　　金：国家电子信息产业发展基金(编号:[2010]301)资助项目;安徽高等学校省级自然科学研究(编号:KJ2011A220)资助项目;温州市科技计划(编号:G20100089)资助项目

摘　　要：电池荷电状态(SOC)的估算精度是影响新能源汽车性能的重要因素之一。针对电池参数动态变化影响SOC估算精度的问题,在确定二阶RC等效电路模型的基础上,采用渐衰记忆的递推最小二乘算法和扩展卡尔曼滤波算法对模型参数与SOC在线联合估算。经过实验与仿真验证,在模拟城市道路工况的放电条件下,与安时法和卡尔曼法相比,联合估算方法得到的SOC估算值与真实值的误差缩小到1.29%。该方法能够适应电池特性的动态变化,保证较高的SOC估算精度。The estimation accuracy of the battery state of charge （SOC） is one ot the Key tactors mat altect me per- formance of new energy vehicles. For the dynamic changes of the battery parameters will influence SOC estimation accu- racy. Based on the second-order RC equivalent circuit model, this paper adopted fading memory recursive least-squares algorithm and extended Kalman filter algorithm to joint estimate the model parameters and SOC on line. Through the ex- periment and simulation under the discharge condition of simulated urban road schedule, the SOC estimation error be- tween the value from joint estimation method and the true value reduced to 1.29% compared with the AH method and Kalman method. Therefore, this method is able to adapt to SOC estimation accuracy. dynamic changes of battery characteristics and ensure high

关 键 词：荷电状态(SOC) 新能源汽车 磷酸铁锂电池 等效电路模型 联合估算 

分 类 号：TM912.9[电气工程—电力电子与电力传动]