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

György Károlyi, Anna I. Pózna, Katalin M. Hangos and Attila Magyar
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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
Attila Magyar: Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary

Energies, 2022, vol. 15, issue 2, 1-23

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.

Keywords: Li-ion battery; battery charging; fuzzy logic control; genetic algorithm; optimization (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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