【作者】 常江； 魏中宝； 何洪文；

【Author】 Chang Jiang;Wei Zhongbao;He Hongwen;National Engineering Laboratory for Electric Vehicles,Beijing Institute of Technology;

【机构】 北京理工大学电动车辆国家工程实验室；

【摘要】 随着气候变化、资源紧缺和环境污染的状况愈加突出,电动汽车近些年来快速发展。锂离子电池也广泛用作电动汽车的车载能源。为了使锂电池能够在电动汽车使用过程中安全、高效地工作,一个完善的电池管理系统(BMS)不可或缺。电池荷电状态(SOC)估计是BMS需要完成的一项关键任务。SOC不仅能表征目前电池的剩余容量,还能用于某些系统级能量管理策略。本文提出了一种基于自适应模型的在线SOC估计方法,它将Thevenin等效电路模型、递推最小二乘法和扩展卡尔曼滤波算法相结合,在线按序完成模型参数辨识和SOC估计。经过仿真验证和试验验证,结果显示此方法能够有效地跟踪模型参数的变化,并实时完成SOC准确估计,试验验证中SOC估计的最大误差控制在4%以内。更多还原

【Abstract】 Electric vehicles(EVs) have developed rapidly in the face of critical problems of climate change,resource scarcity and environmental pollution,while lithium-ion batteries(LIBs) have been widely used as the onboard power source of EVs.As a key state in the battery management system(BMS),state of charge(SOC) not only defines the safety margin of battery to avoid over-charge/discharge,but also underlies the system-level energy management.This paper proposes an online adaptive model-based SOC estimator.This method combines the Thevenin battery model,the recursive least squares(RLS) algorithm and the extended Kalman filter(EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner.Simulations and experiments are performed to evaluate the proposed method.Results suggest that the proposed method can effectively track the change of model parameters,and thus estimate the SOC accurately in real time.更多还原