Deep learning to estimate lithium-ion battery state of health without additional degradation experiments.

Lu, Jiahuan; Xiong, Rui; Tian, Jinpeng; Wang, Chenxu; Sun, Fengchun

Lu, Jiahuan; Xiong, Rui; Tian, Jinpeng; Wang, Chenxu; Sun, Fengchun.

Afiliación

Lu J; Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Xiong R; Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China. rxiong@bit.edu.cn.
Tian J; Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China. tianjinpeng@bit.edu.cn.
Wang C; Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Sun F; Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Nat Commun ; 14(1): 2760, 2023 May 13.

Article en En | MEDLINE | ID: mdl-37179411

RESUMEN

State of health is a critical state which evaluates the degradation level of batteries. However, it cannot be measured directly but requires estimation. While accurate state of health estimation has progressed markedly, the time- and resource-consuming degradation experiments to generate target battery labels hinder the development of state of health estimation methods. In this article, we design a deep-learning framework to enable the estimation of battery state of health in the absence of target battery labels. This framework integrates a swarm of deep neural networks equipped with domain adaptation to produce accurate estimation. We employ 65 commercial batteries from 5 different manufacturers to generate 71,588 samples for cross-validation. The validation results indicate that the proposed framework can ensure absolute errors of less than 3% for 89.4% of samples (less than 5% for 98.9% of samples), with a maximum absolute error of less than 8.87% in the absence of target labels. This work emphasizes the power of deep learning in precluding degradation experiments and highlights the promise of rapid development of battery management algorithms for new-generation batteries using only previous experimental data.

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Aspecto: Patient_preference Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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