Genetic Algorithm-Based Multi-Objective Optimization of Fast-Charging Parameters of a Lithium-Ion Battery by a Coupled Equivalent Electric Circuit-Heat Transfer Model

Abstract

Optimization of fast battery charging parameters of electric vehicles is essential for improved reliability and lifecycle. Conventional lithium-ion battery charging mostly adopts constant current-constant voltage (CC-CV) method, but continuous and frequent charging may cause accelerated capacity reduction under low temperature conditions, leading to inefficiency and safety issues. This study proposes an optimization strategy for lithium-ion batteries and a charging method in cold environments based on an improved coupled electric-thermal model. A second-order RC equivalent circuit battery model is parameterized by means of hybrid pulse power characteristic (HPPC) experiment and the coupled electric-thermal model of the lithium-ion battery is created. Multi-stage constant current charging (MCC) method not only prevents battery degradation, but also reduces the charging time by increasing efficiency of the charging process at low temperatures compared to the batteries charged with the conventional CC-CV charging method. Therefore, optimized charging strategy will provide a certain theoretical basis for the thermal management of the battery system, ensuring safe charging by limiting the temperature rise during fast charging. This innovative study is expected to contribute to the literature due to the limited published research on battery charging at low environmental temperatures. © 2024 Elsevier B.V., All rights reserved.