摘要
针对标准无迹卡尔曼滤波(Unscented Kalman filter,UKF)算法本身存在着因状态误差协方差矩阵无法实现Cholesky分解而导致滤波发散的隐患,以及在电池状态估计过程中由离线标定的电池等效模型参数而造成的累积误差的问题,本文发展了一种平方根无迹卡尔曼滤波(Square-root unscented Kalman filter,SR-UKF)算法,并设计了一种电池状态联合估计策略.首先快速SR-UKF算法通过对观测方程进行准线性化处理,降低了每次无迹变换时的计算开销;然后在迭代过程中,用状态误差协方差矩阵的平方根代替状态误差协方差矩阵,该平方根是由QR分解与Cholesky因子的一阶更新得到,解决了UKF算法迭代过程中可能由计算累积误差引起状态误差协方差矩阵负定而导致滤波结果发散的问题,保证了电池荷电状态(State of charge,SOC)在线滚动估计的数值稳定性;最后采用联合估计策略,对电池等效模型参数进行实时辨识,保证了电池等效模型的准确性与有效性,从而提高了电池SOC的估计精度.仿真对比结果验证了快速SR-UKF算法以及电池状态联合估计策略的可行性与鲁棒性.
The Li-ion battery is an important energy source for electric vehicles(EVs),and the accurate estimation of the battery power state provides a reliable reference for balancing the battery packing and battery management system(BMS).It also has great practical significance for making full and reasonable utilization of batteries,and improving the battery life cycle and vehicle operation efficiency.Practical issues that must be addressed include the filtering divergence caused by the non-positive definite error covariance matrix in the standard unscented Kalman filter(UKF)and the state estimation errors that accumulate from the simplified mathematical modeling of the Li-ion battery,with its inherently strong non-linearity,time variation,and uncertainty.To resolve these issues,in this article,a real-time state co-estimation algorithm was proposed based on a fast square-root unscented Kalman filter(SR-UKF)framework.First,during the iteration process,the non-linear measurement function,which describes the propagation of each sigma point,is called by an unscented transform.A reduction in computational complexity can be achieved if the non-linear measurement function is quasi-linearized.Second,instead of a state error covariance matrix,the square root of the state error covariance matrix is used,which is obtained by QR decomposition and first-order updating of the Cholesky factor.This step deals with the problem that arises if the state error covariance matrix is negative definite due to the computational errors accumulated while performing recursive estimation with the standard UKF.This guarantees the numerical stability of the battery’s estimated state of charge(SOC)in real time.Third,the inner ohmic resistance and nominal capacity that indirectly characterize the state of health can be estimated online,and a highly precise SOC estimation can be realized due to the accuracy and efficiency of the battery model.Comparative experimental results confirm and validate the feasibility and robustness of the proposed fast SR-UKF algorithm and co-estimation strategy.
作者
章军辉
李庆
陈大鹏
赵野
ZHANG Jun-hui;LI Qing;CHEN Da-peng;ZHAO Ye(Institute of Microelectronics of Chinese Academy of Sciences,Beijing 100029,China;Jiangsu R&D Center for Internet of Things,Wuxi 214135,China;Wuxi Internet of Things Innovation Center Co.,Ltd.,Wuxi 214135,China;Institute of Microelectronic Technology of Kunshan,Kunshan 215347,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2021年第7期976-984,共9页
Chinese Journal of Engineering
基金
江苏省博士后科研计划资助项目(2020Z411)
国家重点研发计划“新能源汽车专项”资助项目(2016YFB0100516)。