Lithium-ion Battery RUL Prediction Method Based on Double Gaussian Model

doi:10.11985/2022.04.005

Abstract

Abstract:

For the performance maximization and maintenance of lithium-ion batteries, accurate remaining useful life(RUL) predictions are essential. To accurately predict the RUL of lithium-ion batteries, a novel double Gaussian model is proposed to describe the aging process of lithium-ion batteries. Specifically, several popular empirical models for battery capacity degradation are analyzed and evaluated, and a double Gaussian model with better performance is proposed. Afterward, a double Gaussian aging model is established utilizing the particle filter(PF) technique, based on the historical capacity data. The fitted correlation coefficient and root mean square error are also introduced to assess the model. Finally, the RUL prediction experiments are conducted to verify the verification of the proposed aging model based on the battery aging data from the laboratory’s battery cells and the National Aeronautics and Space Administration(NASA) Ames Prognostics Center of Excellence. The experimental results demonstrate that the proposed aging model can predict the RUL accurately, and the prediction error is significantly improved compared to other models.

Key words: Lithium-ion battery, RUL prediction, double Gaussian model, particle filter(PF)

CLC Number:

TM912

LI Yanmei, LIU Huihan, ZHANG Chaolong, LUO Laijing. Lithium-ion Battery RUL Prediction Method Based on Double Gaussian Model[J]. Journal of Electrical Engineering, 2022, 17(4): 32-40.

Figures/Tables 13

References 25

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电池	a₀	b₀	c₀	d₀	e₀	f₀
电池1	2.355	11.65	1 285	0.196 8	883.9	248.8
电池2	0.122 9	40.964 7	37.547 9	66.947 5	3 644.993 7	1 917.023

电池	R	RMSE
电池1	0.999 5	0.008 0
电池2	0.996 9	0.015 0

电池	MAPE	RMSE	Error
电池1	0.339 0	0.009 9	2
电池2	1.749 7	0.031 7	1

电池	指标	提出的模型	双指数模型	多项式模型
电池2	R	0.996 9	0.993 1	0.987 8
电池2	RMSE	0.015 0	0.022 3	0.029 6

电池	指标	提出的模型	双指数模型	多项式模型
	MAPE	0.339 0	0.976 4	0.656 6
电池1	RMSE	0.009 9	0.022 0	0.015 6
	Error	2	26	10
	MAPE	1.749 7	1.912 5	1.930 6
电池2	RMSE	0.031 7	0.034 2	0.034 9
	Error	1	3	2