Enhancing Lithium-Ion Battery Health Predictions by Hybrid-Grained Graph Modeling

Author:

Xing Chuang1,Liu Hangyu2,Zhang Zekun3,Wang Jun1ORCID,Wang Jiyao4ORCID

Affiliation:

1. College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China

2. School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China

3. College of Biomedical Engineering, Sichuan University, Chengdu 610065, China

4. Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China

Abstract

Predicting the health status of lithium-ion batteries is crucial for ensuring safety. The prediction process typically requires inputting multiple time series, which exhibit temporal dependencies. Existing methods for health status prediction fail to uncover both coarse-grained and fine-grained temporal dependencies between these series. Coarse-grained analysis often overlooks minor fluctuations in the data, while fine-grained analysis can be overly complex and prone to overfitting, negatively impacting the accuracy of battery health predictions. To address these issues, this study developed a Hybrid-grained Evolving Aware Graph (HEAG) model for enhanced prediction of lithium-ion battery health. In this approach, the Fine-grained Dependency Graph (FDG) helps us model the dependencies between different sequences at individual time points, and the Coarse-grained Dependency Graph (CDG) is used for capturing the patterns and magnitudes of changes across time series. The effectiveness of the proposed method was evaluated using two datasets. Experimental results demonstrate that our approach outperforms all baseline methods, and the efficacy of each component within the HEAG model is validated through the ablation study.

Publisher

MDPI AG

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