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

Engineering and Technology

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

J. Lu, R. Xiong, et al.

Explore groundbreaking advances in lithium-ion battery technology with a novel deep-learning framework developed by Jiahuan Lu, Rui Xiong, Jinpeng Tian, Chenxu Wang, and Fengchun Sun. This framework estimates battery state of health with remarkable accuracy, eliminating the need for extensive degradation experiments.

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Playback language: English
Abstract
This article proposes a deep-learning framework for estimating lithium-ion battery state of health (SOH) without requiring additional degradation experiments. The framework uses a swarm of deep neural networks with domain adaptation to achieve accurate estimations, even without target battery labels. Validation results on 71,588 samples from 65 commercial batteries showed absolute errors below 3% for 89.4% of samples and below 5% for 98.9% of samples, with a maximum error under 8.87%. This method significantly reduces the time and resource demands of traditional SOH estimation.
Publisher
Nature Communications
Published On
May 13, 2023
Authors
Jiahuan Lu, Rui Xiong, Jinpeng Tian, Chenxu Wang, Fengchun Sun
Tags
lithium-ion battery
state of health
deep-learning framework
domain adaptation
deep neural networks
estimation accuracy
commercial batteries

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