A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC

Abstract An intelligent control proposal for battery equalization is presented by genetic algorithm optimization integrated with fuzzy logic control–neural network algorithm. First, an effective two-stage DC/DC converter architecture is developed, which pave the way for the hardware module. Then, an equivalent circuit model in weighted combination with ampere-hour counting method is adopted by fuzzy logic control scheme to obtain static SOC estimation. Then the dynamic battery SOC is precisely estimated on basis of static SOC by means of neural network. The most important is the genetic algorithm optimization for battery equalization to improve the energy efficiency and time efficiency of the equalization system. Finally, certification of simulation is demonstrated to validate the proposed novel equalization scheme.

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