Explainability-driven model improvement for SOH estimation of lithium-ion battery

Fujin Wang, Zhibin Zhao, Zhi Zhai, Zuogang Shang, Ruqiang Yan and Xuefeng Chen

Reliability Engineering and System Safety, 2023, vol. 232, issue C

Abstract: Deep neural networks have been widely used in battery health management, including state-of-health (SOH) estimation and remaining useful life (RUL) prediction, with great success. However, traditional neural networks still lack transparency in terms of explainability due to their “black-box†nature. Although a number of explanation methods have been reported, there is still a gap in research efforts towards improving the model benefiting from explanations. To bridge this gap, we propose an explainability-driven model improvement framework for lithium-ion battery SOH estimation. To be specific, the post-hoc explanation technique is used to explain the model. Beyond explaining, we feed the insights back to model to guide model training. Thus, the trained model is inherently explainable, and the performance of the model can be improved. The superiority and effectiveness of the proposed framework are validated on different datasets and different models. The experimental results show that the proposed framework can not only explain the decision of the model, but also improve the model’s performance. Our code is open source and available at: https://github.com/wang-fujin/Explainability-driven_SOH.

Keywords: Lithium-ion battery; State-of-health (SOH); Estimation; Explainability-driven; Layer-wise relevance propagation (LRP) (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832022006615
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006615

DOI: 10.1016/j.ress.2022.109046

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().