paper 150213132011

State of charge (SOC) is an important parameter of battery management system (BMS) which reflects the reliability, safety, and lifetime of batteries. However, SOC cannot be measured directly and we have to estimate it via analyzing some other parameters such as voltage, current, and temperature. An accurate estimation strategy of SOC is necessary for the BMS and we propose a novel model based on particle swarm optimization (PSO) in this paper. The PSO is an optimization method which originated from artificial intelligence and evolutionary computation. It is a simple, effective, and universal theory which solves problems by seeking their individually best and globally best solutions. We apply this theory to optimally estimate the SOC parameters for LiFeO4 Batteries and the simulated and experimental results have demonstrated its effectiveness.

Citation

Mei Song Tong, Lan Chen, Tao Geng, C. H. Jiang, Guo Chun Wan, and Q. Zhang, "Estimation of Equivalent Model Parameters for LiFeO4 Batteries Based on Particle Swarm Optimization," Proceedings of 2015 Photonics & Electromagnetics Research Symposium, Prague, July 6 - 9，Page(s)1072-1076

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