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An optimum fast charging pattern search for Li-ion batteries using particle swarm optimization

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4 Author(s)
Chun-Liang Liu ; Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan ; Shun-Chung Wang ; Yi-Hua Liu ; Meng-Chung Tsai

In order to maximize the available performance of the Li-ion batteries, a searching algorithm for an optimal fast charging pattern using particle swarm optimization (PSO) with an evaluation index based on the fuzzy-deduced cost function is proposed in this paper. An optimal five-stage charging strategy is obtained by the PSO searching according to the decision-making in the fitness evaluation index that is computed from the cost function. The cost function formulated by two paramount parameters, charge time and discharge capacity, is employed to assess the cost benefit of the applied charging pattern. Regulating rules of weighting within the cost function are derived from the fuzzy logic inference to attain the best fitness evaluation. The proposed searching methodology for optimal multistage charging pattern features characteristics of fast convergence, effectiveness and easy to implement. Experimental results show that the obtained rapid charging pattern is capable of charging the batteries to 90% available capacity within 51 minutes and also provides 22% more cycle life than the conventional constant current-constant voltage (CC-CV) method.

Published in:

Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on

Date of Conference:

20-24 Nov. 2012

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