无损卡尔曼滤波在估算动力电池SOC中的应用

电动车电池管理系统(BMS)能精确估算电池荷电状态(SOC),是电池安全和优化控制充放电能量的必要保证。针对整车环境下动力电池的非线性、强耦合特性,在多维动态补偿安时积分与电池模型融合的基础上,提出一种无损卡尔曼滤波(UKF)方法估算电池的SOC。应用Simulink仿真工具及Stateflow有限状态机工具建立一个简单可靠易移植的电池管理系统应用层控制策略模型。仿真结果验证了模型的可靠性,同时表明无损卡尔曼滤波能获得准确的SOC估算值。

Unscented kalman filtering for state of charge estimation of power battery

State of Charge (SOC) was the core part of Battery Management System (BMS) of Electric Vehicles (EVs).Its accurate estimation was the assurance of battery safety and optimal control of charge/discharge energy.Power battery in vehicle was under the environment of nonlinear and strong coupling characteristic.An Unscented Kalman Filter (UKF-Unscented Kalman Filter) method was proposed.It combined Ampere Hour (AH) integral method which took account of Multiple and Dynamic Compensation with battery model.A simple and reliable control strategy model of battery management system (BMS) was built.Tests were made to verify the performance of model.The results indicate that our model was reliable and the method could provide accurate SOC estimation.