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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Development and Validation of a CNN-LSTM Fusion Model for Multi-Fault Diagnosis in Hybrid Electric Vehicle Power Systems

by
Bo-Siang Chen
1,
Tzu-Hsin Chu
2,3,
Wei-Lun Huang
4,5,6 and
Wei-Sho Ho
2,7,8,*
1
Department of Vehicle Engineering, Nan Kai University of Technology, No. 568, Zhongzheng Road, Caotun Township, Nantou City 542020, Taiwan
2
Department of Electrical and Mechanical Technology, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Road, Changhua City 500208, Taiwan
3
Department and Graduate Institute of Information Management, Yu Da University of Science and Technology, No. 168, Hsueh-fu Road, Tanwen Village, Chaochiao Township, Miaoli County 361027, Taiwan
4
Medical Affairs Office, National Taiwan University Hospital, No. 7, Zhongshan S. Road, Zhongzheng District, Taipei City 100225, Taiwan
5
Department of Health Services Adminstration, China Medical University, No. 100, Sec. 1, Jingmao Road, Beitun District, Taichung City 406040, Taiwan
6
Department of Health Care Management, National Taipei University of Nursing and Health Sciences, No. 365, Mingde Road, Beitou District, Taipei City 112303, Taiwan
7
Graduate Institute of Technological and Vocational Education, National Changhua University of Education, Bao-Shan Campus, No. 2, Shi-Da Road, Changhua City 500208, Taiwan
8
NCUE Alumni Association, National Changhua University of Education, Jin-De Campus, No. 1, Jinde Road, Changhua City 500207, Taiwan
*
Author to whom correspondence should be addressed.
Eng 2026, 7(1), 51; https://doi.org/10.3390/eng7010051 (registering DOI)
Submission received: 26 September 2025 / Revised: 26 December 2025 / Accepted: 8 January 2026 / Published: 17 January 2026
(This article belongs to the Section Electrical and Electronic Engineering)
Versions Notes

Abstract

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications.
Keywords: Hybrid Electric Vehicles (HEV); CNN-LSTM fusion model; power system fault diagnosis; deep learning; predictive maintenance Hybrid Electric Vehicles (HEV); CNN-LSTM fusion model; power system fault diagnosis; deep learning; predictive maintenance

Share and Cite

MDPI and ACS Style

Chen, B.-S.; Chu, T.-H.; Huang, W.-L.; Ho, W.-S. Development and Validation of a CNN-LSTM Fusion Model for Multi-Fault Diagnosis in Hybrid Electric Vehicle Power Systems. Eng 2026, 7, 51. https://doi.org/10.3390/eng7010051

AMA Style

Chen B-S, Chu T-H, Huang W-L, Ho W-S. Development and Validation of a CNN-LSTM Fusion Model for Multi-Fault Diagnosis in Hybrid Electric Vehicle Power Systems. Eng. 2026; 7(1):51. https://doi.org/10.3390/eng7010051

Chicago/Turabian Style

Chen, Bo-Siang, Tzu-Hsin Chu, Wei-Lun Huang, and Wei-Sho Ho. 2026. "Development and Validation of a CNN-LSTM Fusion Model for Multi-Fault Diagnosis in Hybrid Electric Vehicle Power Systems" Eng 7, no. 1: 51. https://doi.org/10.3390/eng7010051

APA Style

Chen, B.-S., Chu, T.-H., Huang, W.-L., & Ho, W.-S. (2026). Development and Validation of a CNN-LSTM Fusion Model for Multi-Fault Diagnosis in Hybrid Electric Vehicle Power Systems. Eng, 7(1), 51. https://doi.org/10.3390/eng7010051

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