新能源汽车锂离子电池组SOC-SOP联合估计算法  

Joint estimation algorithm of SOC-SOP for lithium-ion battery pack in new energy vehicles

作　　者：谢翌[1] 江迪生 张扬军[2] 李伟[1] 杨瑞[3] 钱煜平[2] XIE Yi;JIANG Disheng;ZHANG Yangjun;LI Wei;YANG Rui;QIAN Yuping(College of Mechanical and Vehicular Engineering,Chongqing University,Chongqing 400044,China;State Key Laboratory of Automotive Safety and Energy,Tsinghua University,Beijing 100084,China;School of Energy and Power Engineering,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学机械与运载工程学院,中国重庆400044 [2]清华大学,汽车安全与节能国家重点实验室,中国北京100084 [3]重庆大学能源与动力工程学院,中国重庆400044

出　　处：《汽车安全与节能学报》2022年第3期580-589,共10页Journal of Automotive Safety and Energy

基　　金：汽车安全与节能国家重点实验室开放课题(KF2031);国家自然科学基金联合项目(U1864212)。

摘　　要：为了精确地估计新能源汽车锂离子电池组的状态,该文将串并联电池包中并联电池组简化为大单体,基于一阶阻容模型构建了电池包的等效电路模型。基于自适应扩展Kalman滤波(AEKF)和电池系统多约束算法构建了电池包的荷电状态-功率状态(SOC-SOP)双状态联合估计算法。该算法在精确估计SOC的基础上,考虑了电池多约束对其功率状态(SOP)影响,使电池组SOP算法能够精确地预测电池电流峰值,实现电池组精确SOP估计。结果表明:在模拟循环高速公路燃料经济性试验(HWFET)工况下,SOC估计值收敛后的最大绝对误差仅为2.7%;SOP估计值与日本电动车标准(JEVS)实验测试结果相同且平均相对误差小于3.5%。The parallel-connected submodule in the serial-parallel connected battery pack was considered as the single battery unit to accurately estimate the state of the lithium-ion battery pack of a new energy vehicle,and the first-order resistance-capacitance model was applied to modelling the battery pack.The adaptive extended Karlman filter(AEKF)and the algorithm with multiple constrains were used for the state of chargestate of power(SOC-SOP)estimation algorithm for the battery pack.Based on the accurate estimation of SOC and multiple-constrain algorithm,SOC-SOP joint estimation algorithm precisely predicted the maximum current through the battery pack and realized the accurate SOP estimation.The results show that the maximum absolute error of the state of charge(SOC)estimate after convergence is only 2.7%under the simulated cyclic highway fuel economy test(HWFET);the state of power(SOP)estimate result is the same as that obtained in the Japanese Electric Vehicle Standard(JEVS)experimental test and the average relative error is less than 3.5%.

关 键 词：新能源汽车 锂离子电池组 自适应扩展Kalman滤波(AEKF) 荷电状态(SOC) 功率状态(SOP) 

分 类 号：TM912[电气工程—电力电子与电力传动]