Abstract
A particle swarm optimization algorithm to search for an optimal five-stage constant-current charge pattern is proposed. The goal is to maximize the objective function for the proposed charge pattern based on the charging capacity, time, and energy efficiency, which all share the same weight. Firstly, an equivalent circuit model is built and battery parameters are identified. Then the optimal five-stage constant-current charge pattern is searched using a particle swarm optimization algorithm. At last, comparative experiments using the constant current-constant voltage (CC-CV) method are performed. Although the charging SOC of the proposed charging pattern was 2.5% lower than that of the CC-CV strategy, the charging time and charging energy efficiency are improved by 15.6% and 0.47% respectively. In particular, the maximum temperature increase of the battery is approximately 0.8 °C lower than that of the CC-CV method, which indicates that the proposed charging pattern is more secure.
Highlights
A particle swarm optimization algorithm to search for an optimal five-stage constant-current charge pattern is proposed
To evaluate the performance of the obtained charging pattern, a comparative experiment is carried out using the current-constant voltage (CC-CV) method and the obtained charging pattern
The charging SOC of the proposed charging pattern is 2.5% lower than that of the CC-CV strategy, the temperature rise showes an obvious decrease of 4.3%
Summary
A particle swarm optimization algorithm to search for an optimal five-stage constant-current charge pattern is proposed. Li-ion batteries with high energy density and a low self-discharge rate are becoming more and more popular in EVs, and have motivated studies to enhance their charging performance. These issues directly point to the kind of charging strategy that should be adopted. Yixiao Wang et al.: PSO-based Optimization for Constant-current Charging Pattern for Li-ion Battery been proposed recently [9,10,11,12,13,14,15]. A Taguchi-based algorithm was used in Ref. [15] to achieve multi-objective optimization for the charging capacity and time, which reduced the cost of the experiment
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