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

ABSTRACT: 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 prop

SUBMITTER: Lu J 

PROVIDER: S-EPMC10183024 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature