{"id":9846,"date":"2026-08-12T14:30:33","date_gmt":"2026-08-12T06:30:33","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9846"},"modified":"2026-08-17T23:41:12","modified_gmt":"2026-08-17T15:41:12","slug":"jase-202611-34-034","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-034","title":{"rendered":"Fault diagnosis of new energy vehicle system based on deep learning"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Zhengqian Wu<\/i><\/a><\/p>\n\n\n\n

Hunan Mechanical & Electrical Polytechnic, Changsha, 410151 China<\/p>\n<\/div>\n\n\n\n

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Received: May 05, 2026
Accepted: June 26, 2026
Publication Date: August 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Architecture of the Adaptive Gated Long Short-Term Memory (AG-LSTM) Unit<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.034<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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New energy vehicles (NEVs), powered by sustainable energy, are emerging as a cleaner alternative to conventional vehicles. Their performance and safety depend heavily on the health of critical components, making accurate fault diagnosis essential. This study proposes an advanced fault detection framework for NEVs, focusing on fault identification and classification in the drivetrain using deep learning techniques. Sensor data from electric vehicles, including current, voltage, motor speed, and environmental conditions, are pre-processed through normalization and missing-value imputation to ensure consistency. Feature extraction is performed using Wavelet Transform (WT) and Fast Fourier Transform (FFT) to capture both transient and steady-state behaviours. The proposed Enriched Crow Search Optimizer-based Adaptive Gated Long Short-Term Memory (ECS-AG-LSTM) model enhances LSTM adaptability and fault classification accuracy through optimization. Experimental results demonstrate superior performance, achieving 98.5% accuracy, 98.31% recall, 98.42% precision, 98.51% F1-score, and an R2 value of 0.956 . The findings confirm that ECS-AG-LSTM provides a robust and reliable solution for improving NEV safety and operational dependability.<\/p>\n\n\n\n

Keywords: New Energy Vehicles (NEVs), Fault Diagnosis, Deep Learning (DL), Enriched Crow Search Optimizer-driven Adaptive Gated Long Short-Term Memory (ECS-AGLSTM), Sensor Data ion.<\/em><\/p>\n\n\n\n

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  1. [1] Y. Chen, J. Zhang, S. Zhai, and Z. Hu, (2024) \u201cData-Driven Modeling and Fault Diagnosis for Fuel Cell Vehicles Using Deep Learning\u201d Energy AI 16: 100345. DOI: 10.1016\/j.egyai.2024.100345.<\/li>\n
  2. [2] C. S. A. Gong, C. H. S. Su, and K. H. Tseng, (2020) \u201cImplementation of Machine Learning for Fault Classification on Vehicle Power Transmission System\u201d IEEE Sensors Journal 20(24): 15163\u201315176. DOI: 10.1109\/JSEN.2020.3010291.<\/li>\n
  3. [3] H. Kaplan, K. Tehrani, and M. Jamshidi, (2021) \u201cA Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications\u201d Energies 14(20): 6599. DOI: 10.3390\/en14206599.<\/li>\n
  4. [4] M. U. I. Khan, M. I. H. Pathan, M. M. Rahman, M. M. Islam, M. A. R. Chowdhury, M. S. Anower, et al., (2024) \u201cSecuring Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification\u201d IEEE Access: DOI: 10.1109\/ACCESS.2024.3400913.<\/li>\n
  5. [5] M. Z. Khaneghah, M. Alzayed, and H. Chaoui, (2023) \u201cFault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles\u201d Machines 11(7): 713. DOI: 10.3390\/machines11070713.<\/li>\n
  6. [6] X. Liang, P. Wang, X. Cao, X. Wan, P. Chao, X. Zhao, et al., (2025) \u201cResearch on Improving the Safety of New Energy Vehicles Exploits Vehicle Operating Data\u201d Safety Science 181: 106681. DOI: 10.1016\/j.ssci.2024.106681.<\/li>\n
  7. [7] H. Liu, X. Song, and F. Zhang, (2021) \u201cFault Diagnosis of New Energy Vehicles Based on Improved Machine Learning\u201d Soft Computing 25(18): 12091\u201312106. DOI: 10.1007\/s00500-021-05860-9.<\/li>\n
  8. [8] P. Wang, J. Chen, F. Lan, Y. Li, and Y. Feng, (2024) \u201cMultiscale Feature Fusion Approach to Early Fault Diagnosis in EV Power Battery Using Operational Data\u201d Journal of Energy Storage 98: 112812. DOI: 10.1016\/j.est.2024.112812.<\/li>\n
  9. [9] Y. Wang and W. Li, (2021) \u201cTransfer-Based Deep Neural Network for Fault Diagnosis of New Energy Vehicles\u201d Frontiers in Energy Research 9: 796528. DOI: 10.3389\/fenrg.2021.796528.<\/li>\n
  10. [10] Y. Zhang, C. Liao, M. Liu, and Z. She, (2024) \u201cPLC Technology Under the New Energy Vehicle Motor Drive System Fault Detection Research\u201d Journal of Electrical Systems 20(9s): 537\u2013545.<\/li>\n
  11. [11] J. Zhao, X. Feng, J. Wang, Y. Lian, M. Ouyang, and A. F. Burke, (2023) \u201cBattery Fault Diagnosis and Failure Prognosis for Electric Vehicles Using Spatiotemporal Transformer Networks\u201d Applied Energy 352: 121949. DOI: 10.1016\/j.apenergy.2023.121949.<\/li>\n
  12. [12] K. Zhao and H. Bai, (2024) \u201cSafety Management System of New Energy Vehicle Power Battery Based on Improved LSTM\u201d Energy Informatics 7(1): 101. DOI: 10.1186\/s42162-024-00411-6.<\/li>\n
  13. [13] C. Wu, R. Sehab, A. Akrad, and C. Morel, (2022) \u201cFault Diagnosis Methods and Fault Tolerant Control Strategies for the Electric Vehicle Powertrains\u201d Energies 15(13): 4840. DOI: 10.3390\/en15134840.<\/li>\n
  14. [14] X. Wang, S. Lu, K. Chen, Q. Wang, and S. Zhang, (2021) \u201cBearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multisensor Data Fusion\u201d IEEE Transactions on Industrial Informatics 18(4): 2452\u20132464. DOI: 10.1109\/TII.2021.3095086.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1682,6],"tags":[1716],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202611_34.034\u00a0\u00a0 Download PDF New energy vehicles (NEVs), powered by sustainable energy, are emerging as…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/9846"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9846"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9846"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9846"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}