基于可变温度模型的锂电池SOC估计方法  

SOC estimation method for lithium battery based on variable temperature model

作　　者：何耀 曹成荣 刘新天 郑昕昕 曾国建 

机构地区：[1]合肥工业大学新能源汽车工程研究院,安徽合肥230009

出　　处：《电机与控制学报》2018年第1期43-52,共10页Electric Machines and Control

基　　金：安徽省国际合作项目(1303063010);国家自然科学基金青年基金项目(61603120);国家自然科学基金青年基金项目(51607052)

摘　　要：动力电池的荷电状态(state-of-charge,SOC)是电动汽车的重要参数之一,而准确的电池模型是提高SOC估算精度的前提。温度对电池相关参数的影响是目前研究的热点,然而现有的电池模型难以适应连续变化的温度环境,且测试工作量大。基于Nernst电化学方程,提出了一种新型的电池建模方法,运用统计学原理,通过测量较少的数据得到较为精确的电池模型,相关参数能够用包括连续变化的温度等多因素进行拟合。通过在不同温度环境下模拟电动汽车实际工况,对锂电池进行放电实验,通过试验设计的方法建立电池模型,结合扩展卡尔曼滤波算法实现对锂电池SOC的动态估计,仿真和实验结果验证了所提方法的优越性。State- of-charge （ S0 C ） of the power batteries is one of the important parameters of the electric vehicles （ EVs） . Accuracy of the S0 C estimation should Xe guaranteed Xy the accuracy of the battery model. Recently, influence of temperature on the b a te r -reated parameters has gained aHowever, the continuously changed temperature can ’ t be adapted by the existing bheavy work to test the parameters in different S0 Cs and temperatures. A S 0 C estimation the Nemst electrochemical equation was proposed. Statistical tlieory was used to gatery model by measuring less data. The parameters of the model can be fitted by multiple factors including continuously changing temperature. The discharging experiments of the lithium battery were operatedthrough working condition simulation of the EVs in different temperatures. A novel b a te r model was es-tablislied by means of design of experiment （ D0E ）. The Extended Kalman Fapplied to estimate lithium battery S0 Cdynamically. The superiority of the proposed estimation method was verified by simulation and experimental results.

关 键 词：动力锂电池 荷电状态 Nemst模型 试验设计 扩展卡尔曼滤波 

分 类 号：TM315[电气工程—电机]