Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models

Lu Liu, Wei Sun, Chuanxu Yue, Yunhai Zhu () and Weihuan Xia
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Lu Liu: Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Wei Sun: Jin Lei Technology Co., Ltd., Jinan 250000, China
Chuanxu Yue: Institute of Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Yunhai Zhu: Science and Technology Service Platform, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250000, China
Weihuan Xia: School of Information Management and Mathematics, Jiangxi University of Finance and Economics, Nanchang 330013, China

Energies, 2024, vol. 17, issue 19, 1-17

Abstract: Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

Keywords: lithium-ion battery; remaining useful life; CEEMDAN; PSO; BiGRU (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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