摘要
针对噪声的未知统计特性会导致锂电池荷电状态估计精度不高的问题,提出一种自适应无迹卡尔曼滤波算法。算法以锂电池的Thevenin一阶RC模型为研究基础,初始阶段基于协方差匹配判据对状态初值偏差情况进行判断,若存在偏差则引入次优渐消因子修正均值协方差进行抑制,能有效克服状态初值偏差问题,在后继估计过程中,利用Sage-Husa估计器在线估计未知观测噪声的统计特性,以减小荷电状态的估计误差。最终实验结果表明,自适应无迹卡尔曼滤波算法能明显改善锂电池荷电状态的估计精度和收敛速度。
Aiming at the problem that the estimation accuracy of SOC of lithium battery is not high due to the unknown statistical characteristics of noise,an adaptive unscented Kalman filter algorithm is proposed in this paper.The algorithm is based on Thevenin first-order RC model of lithium battery.In the initial stage,the covariance matching criterion is used to judge the initial value deviation of the state.If there is a deviation,a suboptimal fading factor is introduced to modify the mean covariance to suppress the deviation,which can effectively overcome the problem of initial state value deviation.In the subsequent estimation process,the Sage-Husa estimator is used to estimate the statistical characteristics of the unknown observed noise online to reduce the estimation error of the state of charge.
出处
《工业控制计算机》
2021年第1期136-139,共4页
Industrial Control Computer
基金
江苏理工学院科研横向项目(KYH20163)。
关键词
荷电状态
次优渐消因子
噪声
精度
state of charge
suboptimal fading factor
noise
accuracy
