Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features

Abstract

Lithium-ion batteries (LIBs) are widely used in many fields, such as electric vehicles and energy storage, and directly impact the device performance and safety. Therefore, the state of health (SOH) assessment is critical for LIB usage. However, most of the existing data-driven SOH modeling methods overlook the inherent uncertainty in battery health prediction, which decreases the reliability of the model. To address this issue, this paper proposes a novel SOH assessment model based on the deep learning framework. The SOH results are derived from the quantile distribution of deep features, giving the SOH values with associated confidence intervals. This enhances the reliability and generalization of SOH assessment results. Additionally, to complete the optimization of the deep model, a Wasserstein distance-based quantile Huber (QH) loss function is developed. This function integrates Huber loss and quantile regression loss, enabling the model to be optimized based on a distribution output. The proposed method is validated using the NASA dataset, and the results confirm that the proposed method can effectively estimate the SOH of LIB while accounting for uncertainty. The incorporation of SOH distribution enhances the reliability and generalization ability of the SOH assessment model.

Publication DOI: https://doi.org/10.1016/j.ress.2024.110002
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences
Funding Information: This work is supported by National Natural Science Foundation of China (52275080).
Additional Information: © 2024 The Authors. CC BY NC 4.0
Uncontrolled Keywords: Lithium-ion battery,Model reliability,Quantile distribution,State of health,Uncertainty,Wasserstein distance,Safety, Risk, Reliability and Quality,Industrial and Manufacturing Engineering
Publication ISSN: 0951-8320
Last Modified: 18 Jun 2024 07:47
Date Deposited: 09 Feb 2024 10:06
Full Text Link:
Related URLs: https://www.sci ... 0772?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-05
Published Online Date: 2024-02-08
Accepted Date: 2024-02-07
Authors: Zhang, Ying
Zhang, Ming (ORCID Profile 0000-0001-5202-5574)
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Feng, Zhipeng
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)

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