Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

Automated summary

This paper presents a step-by-step guide for implementing and tuning an extended Kalman filter (EKF) to estimate the state of charge (SoC) of batteries in the growing renewable energy and electric vehicle markets. The structured approach reduces efforts and is adaptable to various battery models and systems.