Effective management and just-in-time maintenance of lithium-ion batteries require the knowledge of unmeasured (internal) variables that need to be estimated. Observers are thus designed for this purpose using a mathematical model of the battery internal dynamics. It appears that it is often difficult to tune the observers to obtain good estimation performances both in terms of convergence speed and accuracy, while these are essential in practice. In this context, we demonstrate how a recently developed hybrid multi-observer can be used to improve the performance of a given observer designed for an electrochemical model of a lihium-ion battery. Simulation results, obtained with standard parameters values, show the estimation performance improvement using the proposed method.
Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries
VITO（维托研究所）；EnergyVille（能源村）；Institute for Materials Research (IUMAT), Hasselt University（哈瑟尔特大学材料研究所）；College of Tianyin Lake Science and Technology Innovation, Nanjing Institute of Technology（南京工程学院天目湖学院）；School of Vehicles and Intelligent Transportation, Fuyao University of Science and Technology（福耀科技大学车辆与智能交通学院）；IUMAT, IMEC（IMEC-哈瑟尔特大学材料研究所）
