Robust state of charge and state of health estimation for batteries using a novel multi model approach
Giovanni Guida
Davide Faverato
Marco Colabella
Gianluca Buonomo
Innovation Department,Brain Technologies,Corso Tazzoli 215/12B,Turin 10137,Italy
摘要：Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery man-agement system (BMS). Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system. We propose a new robust method, called ERMES (extendible range multi-model estimator), for determining an estimated state-of-charge (SoC), an estimated state-of-health (SoH) and a predic-tion of uncertainty of the estimates (state-of-uncertainty—SoU), thanks to which it is possible to monitor the validity of the estimates and adjust it, extending the robustness against a wider range of uncertainty, if necessary. Specifically, a finite number of models in state-space form are considered starting from a modified Thevenin battery model. Each model is characterized by a hypothesis of SoH value. An iterated extended Kalman filter (EKF) is then applied to each model in parallel, estimating for each one the SoC state variable. Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF, yielding the overall SoC and SoH estimates, respectively. In addition, a figure of uncertainty of such estimates is also provided.
机标关键词：staterobustmultinovelmodelapproachbatteriescharge
论文发表日期：2022-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：21( 418-438 )
英文信息