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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nicolae Tudoroiu & Mohammed Zaheeruddin & Roxana-Elena Tudoroiu, 2020. "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," Energies, MDPI, vol. 13(11), pages 1-45, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2749-:d:365332
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    References listed on IDEAS

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    4. Bizhong Xia & Wenhui Zheng & Ruifeng Zhang & Zizhou Lao & Zhen Sun, 2017. "A Novel Observer for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(8), pages 1-20, August.
    5. Xiudong Cui & Weixiang Shen & Yunlei Zhang & Cungang Hu, 2017. "A Novel Active Online State of Charge Based Balancing Approach for Lithium-Ion Battery Packs during Fast Charging Process in Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-17, November.
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