基于环境变量建模的锂电池SOC估计方法  

SOC estimation method based on lithium-ion cell model considering environmental factors

作　　者：刘新天[1] 孙张驰 何耀[1] 郑昕昕[1] 曾国建[1] Liu Xintian Sun Zhangchi He Yao Zheng Xinxin(Clean Energy Automotive Research Institute, Hefei University of Technology Zeng Guojian Hefei 230009, Chin)

机构地区：[1]合肥工业大学新能源汽车工程研究院,合肥230009

出　　处：《东南大学学报（自然科学版）》2017年第2期306-312,共7页Journal of Southeast University：Natural Science Edition

基　　金：国家自然科学基金资助项目(21373074);安徽省国际科技合作计划资助项目(1303063010)

摘　　要：通过对不同温度和锂电池荷电状态(SOC)下电池内部参数测定和评估,分析了影响参数变化的环境因素,建立了可变参数的锂电池Thevenin模型.讨论了模型的分段依据以及相关参数的测定和拟合方法,并采用扩展卡尔曼滤波算法(EKF)对锂电池SOC进行估算,给出了基于温度修正的改进SOC估计方法.所提出的电池模型解决了现有算法中模型适用范围局限性的问题,仿真和实验结果表明,所建立的基于锂电池Thevenin模型的SOC估计方法在较宽的温度范围内都能够获得较高的估算精度.The internal parameters of the cell at different temperatures and state-of-charge（ SOC）were tested and calculated. The factors affecting the variations of parameters were analyzed. The Thevenin model of the lithium-ion cell with variable parameters was established. The gist of segmentation and the method for determining the correlation parameters of the model were discussed. The extended Kalman filter（ EKF） algorithm was used to estimate SOC. An improved SOC estimation method which is based on the temperature was given. The proposed cell model can avoid the application limitation of the existing models without considering the influences of environmental factors.Simulation and experimental results showthat the SOC estimation method based on the established model can achieve higher accuracy in a wide temperature range.

关 键 词：动力锂电池 荷电状态 温度补偿 Thevenin模型 扩展卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]