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An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Author

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  • György Károlyi

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

  • Anna I. Pózna

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

  • Katalin M. Hangos

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary
    Systems and Control Laboratory, Institute for Computer Science and Control, Kende Street 13-17, H-1111 Budapest, Hungary)

  • Attila Magyar

    (Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

Abstract

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase.

Suggested Citation

  • György Károlyi & Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2022. "An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm," Energies, MDPI, vol. 15(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:481-:d:721699
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    References listed on IDEAS

    as
    1. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    2. Shuo Zhang & Chengning Zhang & Rui Xiong & Wei Zhou, 2014. "Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles," Energies, MDPI, vol. 7(10), pages 1-15, October.
    3. Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2019. "Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries," Energies, MDPI, vol. 12(19), pages 1-18, September.
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    Cited by:

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    2. Jeong-Eon Park, 2023. "A Control Algorithm for Tapering Charging of Li-Ion Battery in Geostationary Satellites," Energies, MDPI, vol. 16(15), pages 1-15, July.
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