Kalman Filter Tuning for State Estimation of Lithium‐Ion Batteries by Multi‐Objective Optimization via Hyperspace Exploration

Abstract

For the estimation of the state of charge of lithium‐ion batteries Kalman filters are the state of the art. To ensure precise and reliable estimations these filters use covariance matrices, which need to be tuned correctly by the developer. This process is time‐consuming and depends largely on the experience and skill of the developer. Hence, filter tuning is not reproducible and not optimal with regard to goals as accuracy and convergence speed. Herein a multiobjective optimization framework called hyperspace exploration is used for the first time to automate the filter tuning procedure for an extended Kalman filter and two versions of adaptive extended Kalman filters. Four key performance indicators, including the maximum error in the estimation of the state of charge and the according root mean square error, are used to describe, validate, and compare the filter performance. This automated process enables optimal usage of the degrees of freedom in filter tuning and no longer requires manual tuning while the whole hyperspace, including different use cases and validation scenarios, is considered in the optimization. Furthermore, the proposed approach yields a novel method for the evaluation of filter parameters and their influence on the estimation behavior.