Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism

Walid Mchara; Lazhar Manai; Mohamed Abdellatif Khalfa; Monia Raissi

doi:10.1093/ce/zkaf019

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Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism

更新时间：2026-02-06

- Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism
- Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism
- 清洁能源（英文） 2025年第4期
- 作者机构：
  
  1. Laboratory of Robotics, Informatics, and Complex Systems, ENIT
  2. Higher Institute of Information and Communication Technologies, ISTIC
  3. LaNSER, CRTEn
  4. Department of Mathematics, Faculty of Science, University of Monastir
- 作者简介：
- 基金信息：
  
  This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
- DOI：10.1093/ce/zkaf019
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, 等. Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism[J]. 清洁能源（英文）, 2025,(4).

Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism, Clean Energy, Volume 9, Issue 4, August 2025, Pages 64–79, https://doi.org/10.1093/ce/zkaf019
Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, 等. Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism[J]. 清洁能源（英文）, 2025,(4). DOI： 10.1093/ce/zkaf019.

Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Intelligent health state diagnosis of lithium-ion batteries for electric vehicles using wavelet-enhanced hybrid deep learning integrated with an attention mechanism, Clean Energy, Volume 9, Issue 4, August 2025, Pages 64–79, https://doi.org/10.1093/ce/zkaf019 DOI：

摘要

Abstract

The increasing integration of lithium-ion batteries in electric vehicles has spurred extensive research aimed at enhancing the safety and efficiency of battery management systems. A fundamental component of battery management system functionality is the accurate estimation of the state of health (SOH)

which is critical for ensuring the dependable and safe operation of electric vehicles. To address this challenge

we introduce FCM-CNN-WNN-WBILSTM-AM

a time-series prediction framework specifically designed to improve SOH estimation accuracy for Li-ion batteries. The proposed framework starts with a preprocessing phase using fuzzy c-means (FCM) clustering to group batteries with similar characteristics

enabling more precise and customized predictions. It then employs a convolutional neural network (CNN) for initial feature extraction

followed by a wavelet neural network (WNN) layer to handle the non-stationary nature of battery degradation. A wavelet bidirectional long short-term memory (WBILSTM) layer further enhances time-series analysis by capturing both past and future dependencies. To refine feature selection and improve predictive accuracy

an attention mechanism (AM) is integrated

ensuring the model focuses on the most relevant information. To improve computational efficiency and ensure global optimization

the FCM-CNN-WNN-WBILSTM-AM framework employs the RMSprop optimizer

replacing the commonly used Adagrad optimizer. The experimental validation

utilizing multi-battery datasets from the National Aeronautics and Space Administration and the Center for Advanced Life Cycle Engineering repositories

demonstrates the model’s effectiveness in accurately capturing SOH trends and degradation patterns. The results indicate a significant enhancement in SOH estimation accuracy

achieving a root-mean-squared error of 0.0013 and a mean absolute percentage error of 0.0015. These outcomes highlight the model’s superior predictive performance and reduced error rates compared to existing methodologies. Ultimately

the proposed advancements contribute to improving the reliability and longevity of electric vehicle batteries

promoting the widespread adoption and sustainability of electric mobility solutions.

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