考虑能量损失的锂电池能量状态自适应估计

Adaptive Estimation Algorithm for Energy State of Power Li-Ion Battery Considering Energy Loss

针对锂电池模型参数时变性以及端口功率积分法存在累积误差,导致SOE估计精度降低的问题,根据能量守恒原理改进了SOE计算式,并提出一种将多新息最小二乘参数辨识和自适应扩展卡尔曼滤波融合的模型参数自适应的状态估计算法。带遗忘因子多新息最小二乘法辨识动态工况下锂电池Thevenin等效模型的实时参数,自适应扩展卡尔曼滤波器估计电池SOE,通过模型参数辨识和状态估计的交互提高参数辨识和状态估计的精度。通过HPPC测试和BBDST测试,获得电池实测数据,搭建电池能量状态估计的Simulink模型。仿真验证表明,相比于模型参数固定的自适应扩展卡尔曼滤波,多新息最小二乘参数辨识和自适应扩展卡尔曼滤波融合算法具有更高的估计精度。

Aiming at the problem of low SOE estimation accuracy caused by time-varying lithium battery model parameters and the cumulative error in the calculation of the SOE by the port power integration method,a model parameter self-adapting estimation algorithm combining multi-innovation least square parameter identification and adaptive extended Kalman filter is proposed.The multi-innovation least-squares method with forgetting factor identifies the real-time parameters of the lithium battery Thevenin equivalent model under dynamic conditions,and the adaptive extended Kalman filter estimates the battery SOE,and the interaction of model parameter identification and state estimation improves the accuracy of parameter identification and state estimation.Through the HPPC test and BBDST test,the experimental data of the battery is obtained,and the Simulink model of the battery energy state estimation is built.The simulation results show that the fusion algorithm of multi-innovation least squares parameter identification and adaptive extended Kalman filter has higher estimation accuracy compared with the adaptive extended Kalman filter with fixed model parameters.