Research on SOC Prediction of Lithium Battery Based on Whale and Genetic Algorithm Optimized Elman Neural Network

Xiao-yi MAO, Yao-yi TANG, Xiao-qi DING, Hao-yan JIANG, Jian-fei CHEN, Sheng ZHANG

Abstract


Accurate estimation of the state of charge (SOC) of lithium batteries is the research hotspot. As a dynamic recurrent neural network, Elman neural network has a simple structure and can adapt to time-varying characteristics, which have led to its widespread application in the field of SOC prediction. However, the simple network structure can also cause problems such as low learning efficiency, easy to fall into local extremes, and difficulty converge to the optimal weight solution. This paper proposes to combine the whale optimization algorithm (WOA) with genetic algorithm (GA) to optimize the weights and thresholds of the Elman neural network, thus improving the learning speed and prediction accuracy. We select three parameters of current, voltage and temperature as system variables, and simulate the experimental data. The experimental results show that, compared with the traditional Elman neural network and the Elman neural network optimized only by genetic algorithm, the hybrid algorithm performs best. The number of network iterations is small, and the average error drops below 1%.

Keywords


State of charge, Elman neural network, Whale and genetic algorithm.


DOI
10.12783/dtcse/ica2019/30760

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