Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study

Nicolae Tudoroiu, Mohammed Zaheeruddin and Roxana-Elena Tudoroiu
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Nicolae Tudoroiu: John Abbott College, Sainte-Anne-De-Bellevue, QC H9X 3L9, Canada
Mohammed Zaheeruddin: Department of Building, Civil and Environmental Energy Concordia University Montreal, Montreal, QC H3G 1M8, Canada
Roxana-Elena Tudoroiu: Department of Mathematics-Informatics, University of Petrosani, 332006 Petrosani, Romania

Energies, 2020, vol. 13, issue 11, 1-45

Abstract: Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis.

Keywords: lithium-ion cobalt battery; state of charge; state of energy; adaptive EKF SOC estimation; linear observer SOC estimation; MATLAB; Simscape (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: 2020
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