锂电池的荷电状态(state of charge,SOC)估计是电池管理系统的重要组成部分,针对锂电池非线性的特性,提出了采用离散滑模观测器估计锂电池荷电状态的方法,给出了离散滑模观测器的设计方法及其稳定性证明。基于锂电池的戴维南等效电路模型,给出了该方法的设计过程,在不同的充放电电流倍率和环境温度下,进行了锂电池模型的参数辨识,通过与常用的扩展卡尔曼滤波法相比较,分析了离散滑模观测器对锂电池SOC估计的精度、鲁棒性和算法复杂度等方面的性能。实验结果表明,采用该算法可实现锂电池SOC快速精确地估计,误差可控制在约3%,验证了该方法的可行性。
Modeling and state of charge(SOC) estimation of lithium-ion(Li-ion) battery are the key techniques of battery pack management system(BMS) and critical to its reliability and safety operation.An auto-regressive with exogenous input(ARX) model is derived from RC equivalent circuit model(ECM) due to the discrete-time characteristics of BMS.For the time-varying environmental factors and the actual battery operating conditions,a variable forgetting factor recursive least square(VFFRLS)algorithm is adopted as an adaptive parameter identification method.Based on the designed model,an SOC estimator using cubature Kalman filter(CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure.In the battery tests,experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter(EKF) algorithm,which is widely used for Li-ion battery SOC estimation,and the maximum estimation error is about 2.3%.
针对动力锂电池组充放电过程中,各单体电池之间存在的不一致性,设计了超级电容与双向DC-DC变流器相结合的无损均衡管理系统。采用无迹卡尔曼滤波法估算锂电池的荷电状态,与通常采用的扩展卡尔曼滤波器进行了对比研究,经实验验证,本系统能够快速、高效地实现锂电池组的均衡控制,实现精确地锂电池SOC(State of Charge)估计,提高了动力锂电池组的可靠性和安全性。
