Improved firefly optimization algorithm to optimize back propagation neural network for state of health estimation of power lithium ion batteries

doi:10.19799/j.cnki.2095-4239.2022.0668

Abstract

Abstract:

It is essential to estimate a battery's state of health. This paper constructs a back propagation (BP) neural network optimized based on the improved firefly algorithm (FA) to estimate the state of health of lithium-ion batteries. The aim is to address the shortcomings of traditional modeling methods, such as poor estimation accuracy, numerous parameters, complex calculation, and longtime consumption. The weights and thresholds of the BP neural network are optimized using the FA's global optimization ability and fast convergence speed. Levy flight is introduced to improve the global search ability, expand the search range, and improve the estimation accuracy. The lithium-ion battery dataset of NASA Ames Research Center is used to train and estimate the algorithms before and after improvement and optimization and compare the advantages and disadvantages of each algorithm. Results show that compared with the BP neural network algorithm and FA for optimizing the BP neural network (FA-BP) algorithm, Levy flight improved firefly algorithm to optimize the BP neural network (LFFA-BP) algorithm has a higher determination coefficient, smaller error fluctuation range, higher estimation accuracy, and specific practical value.

Key words: lithium battery, state of health, Levy flight, firefly algorithm, BP neural network

CLC Number:

U 461.7

Xinhao ZHAO, Liang XU. Improved firefly optimization algorithm to optimize back propagation neural network for state of health estimation of power lithium ion batteries[J]. Energy Storage Science and Technology, 2023, 12(3): 934-940.

Figures/Tables 7

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数据集	算法	均方差(MSE)	决定系数(R²)
B0006	BP	0.0872	0.9306
B0007	BP	0.0942	0.9188
B0006	FA-BP	0.0293	0.9872
B0007	FA-BP	0.0342	0.9714
B0006	LFFA-BP	0.0170	0.9973
B0007	LFFA-BP	0.0138	0.9950
B0006	PSO-BP	0.0200	0.9946
B0007	PSO-BP	0.0185	0.9917

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