中型锂电池组管理系统研究

Research on Medium-scale Lithium-ion Battery Management System

针对当前BMS中存在的电池电量估算不准、电池组不易均衡等关键技术问题。利用扩展卡尔曼滤波(EKF)在Thevenin电池模型上对SOC(电池剩余电量)初值进行了修正,并使用改进的安时法(增加了自恢复因子)对SOC进行估算,提高了SOC估算的精度。在传统旁路均衡法的基础上,提出了低耗能自动均衡法,该方法能够对电池组自动均衡,且对电压、电流、温度进行实时监控,在减少能量耗散的同时,大大提高了系统的安全。以设计的BMS为平台来对电池组进行了充放电及均衡实验,实验结果表明该BMS能够较好地对电池组进行管理,满足中型锂离子电池组管理的需求。

In this paper,the medium-scale battery management system includes the key technical SOC（state of charge）estimation and cell balancing.The improved Ah algorithm is presented and the SOC initial value is revised using the extension Kalman filter（EKF） based on Thevenin battery model,which increases the SOC estimation accuracy.Low-energy automatic equal method is proposed on the basis of traditional bypass balancing method,automatic to balance the battery and available to monitor the the voltage,current and temperature continuously,which reduces energy dissipation and greatly improves the security of the system.