The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS)

elmehdi Nasri; Tarik  Jarou; Jawad  Abdouni; Salma Benchikh; Sofia  Elidrissi

Authors

elmehdi Nasri Advanced systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University https://orcid.org/0009-0009-5179-3067
Tarik Jarou 1Advanced systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco
Jawad Abdouni Advanced systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco
Salma Benchikh Advanced systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco
Sofia Elidrissi Advanced systems Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

Keywords:

Battery parameter estimation, Parameter identification, Battery management system, State-of-charge, Extended kalman filter, Recursive least square method, Lithium-ion battery

Abstract

The lithium-ion battery is the key power source of an electric vehicle. The cornerstone of safe transportation vehicles is reliable real-time state of charge (SOC) information. Since batteries are the primary form of energy storage in electric vehicles (EVs) and the smart grid, estimation of the state of charge is a critical need for batteries. The SOC estimate approach is considered to be precise and simple to apply for such applications. In this paper, After studying a battery model with an appropriate resistor-capacitor (RC) circuit, A lookup table derived from experimental studies describes the nonlinear connection between the Open Circuit Voltage Voc and the the state of charge. However, if temperature or SOC varies, the equivalent circuit model's characteristics will vary, decreasing the accuracy of SOC calculation. The recursive least squares (RLS) and nonlinear Extended Kalman filters are used in this research to offer a charge estimate technique with online parameter identification to handle this problem. RLS dynamically updates the Thevenin model's parameters. In order to improve the precision of SOC prediction under charge and discharge settings, we presented a regression least-squares-extended Kalman filter (RLS-EKF) estimation approach in this study. The objective of this research is to ensure the updating of the battery parameters and to evaluate the influence of this improvement on the convergence of the state of charge towards the real value. The simulation results suggest that the RLS EKF estimation technique, which is based on precise modeling, may greatly increase SOC estimation accuracy.

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