摘要
为了精确估算电动汽车锂电池的荷电状态(state of charge,SOC),本文通过对主流SOC估算方法进行分析与比较,提出了一种基于卡尔曼滤波算法的电动汽车能量管理系统(energy management system,EMS)SOC估算方法,同时采用联合模型以保证估算过程中有较好的精度,并在实验室条件下进行实测数据及MATLAB仿真分析。仿真结果表明,卡尔曼滤波算法对锂电池SOC进行在线实时估计是有效的,能够较为准确地计算出SOC值,且估算结果与实测值基本一致,该方法可以用于电动汽车锂电池SOC的估算,具有很强的实际应用价值。
By comparison of major SOC estimation methods,the paper proposed a SOC estimation method of battery management system based on Kalman filtering method to accurately predict the state of battery,while using combined model to ensure that the process can have a better estimate of the accuracy and using measured data and MATLAB simulation to analyze under laboratory conditions.The results show that the Kalman filter algorithm for real-time online lithium battery SOC estimation is effective,and it is able to accurately calculate the SOC.The experiment result showed that estimation result of SOC of battery used by the method is consistent with measured values and the method can be applied to battery management system.
出处
《青岛大学学报（工程技术版）》
CAS
2014年第1期60-63,共4页
Journal of Qingdao University(Engineering & Technology Edition)
基金
山东省自然科学基金项目资助(Y2008F23)
山东省科技发展计划项目资助(2011GGB01123)
863计划项目资助(2012AA110407)
