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

Benedikt Rzepka, Simon Bischof and Thomas Blank
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Benedikt Rzepka: Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
Simon Bischof: Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany
Thomas Blank: Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany

Energies, 2021, vol. 14, issue 13, 1-17

Abstract: 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.

Keywords: Li-ion batteries; battery modeling; hysteresis; state of charge estimation; extended Kalman filter; process noise (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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