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Fault detection and diagnosis of electric vehicles using artificial intelligence

International Journal of Applied Power Engineering (IJAPE) · Vol. 13 No. 3 (2024) · hlm. 653–660

DOI
10.11591/ijape.v13.i3.pp653-660
ISSN
2722-2624, 2252-8792
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Electric vehicle (EV) performance is greatly influenced by the motor drive system's stability, efficiency, and safety. With the increased usage of electric vehicles, fault detection and diagnostics (FDD) of the motor drive system has become an important topic of research. In recent years, there has been a lot of interest in artificial intelligence (AI) approaches employed in FDD. This paper provides an overview of the application of AI in defect detection for electric vehicles. The FDD method is divided into two steps: feature extraction and fault classification. Feature extraction involves identifying relevant parameters or characteristics from the EV's sensors and signals, enabling the AI system to capture meaningful patterns. Subsequently, fault classification employs AI algorithms to categorize and identify specific faults based on the extracted features, facilitating efficient diagnosis and maintenance of EVs. In the realm of EVs, the combination of AI techniques and FDD has the potential to improve performance, reliability, and safety while enabling proactive maintenance and reducing downtime. Using machine learning and deep learning, we can detect the fault in the system before it starts damaging our EV.

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  • Woosong University
  • Veer Surendra Sai University of Technology
  • Biju Patnaik University of Technology
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Rudranarayan SenapatiAfiliasi tidak diketahui10
Sivkumar MishraBiju Patnaik University of Technology6
Seong-Cheol KimWoosong University4
Papia RayVeer Surendra Sai University of Technology4
Sanchita SahayAfiliasi tidak diketahui4
Ayush KumarAfiliasi tidak diketahui4
Arun Kumar SahooAfiliasi tidak diketahui4
S. NarasimhaAfiliasi tidak diketahui3
Aditya Prasad MahapatraAfiliasi tidak diketahui2
Bishweashwar SuklaAfiliasi tidak diketahui2
Harikrishnan K. M.Afiliasi tidak diketahui2
Padarabinda PalaiAfiliasi tidak diketahui2
Nisha KediaAfiliasi tidak diketahui2
Raj Alpha SwainAfiliasi tidak diketahui2
Sandip Ranjan BeheraAfiliasi tidak diketahui2
Arul Kumar DashAfiliasi tidak diketahui2
Prajna Jeet OjhaAfiliasi tidak diketahui2
Lingam YashwanthAfiliasi tidak diketahui2
Peesodi UdayAfiliasi tidak diketahui2
Bhabesh RoutrayAfiliasi tidak diketahui2
Nitesh PattanayakAfiliasi tidak diketahui2
Priyansu ShekharAfiliasi tidak diketahui2
Pratyus Ranjan BeheraAfiliasi tidak diketahui2
Priyanka raiAfiliasi tidak diketahui2
Prince KumarAfiliasi tidak diketahui2
Prabin BiswalAfiliasi tidak diketahui2
Swayamjyoti SatapathyAfiliasi tidak diketahui2
Smruti Susmita SahuAfiliasi tidak diketahui2
Chan-Mook JungWoosong University1
Pankaj SharmaWoosong University1
Monalisa PandaAfiliasi tidak diketahui1
Debani Prashad MishraAfiliasi tidak diketahui1
Sopa Mousumi PatroAfiliasi tidak diketahui1
Kalpa Ranjan BeheraAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Fault detection and diagnosis of electric vehicles using artificial intelligence</dc:title>
	<dc:creator>Mishra, Debani Prasad</dc:creator>
	<dc:creator>Padhy, Somya Siddharth</dc:creator>
	<dc:creator>Pradhan, Partha Sarathi</dc:creator>
	<dc:creator>Gupta, Shubh</dc:creator>
	<dc:creator>Senapati, Asutosh</dc:creator>
	<dc:creator>Salkuti, Surender Reddy</dc:creator>
	<dc:subject xml:lang="en-US">artificial intelligence; artificial neural network; deep learning; fault detection and diagnostics; electric vehicles; machine learning; random forest</dc:subject>
	<dc:description xml:lang="en-US">Electric vehicle (EV) performance is greatly influenced by the motor drive system's stability, efficiency, and safety. With the increased usage of electric vehicles, fault detection and diagnostics (FDD) of the motor drive system has become an important topic of research. In recent years, there has been a lot of interest in artificial intelligence (AI) approaches employed in FDD. This paper provides an overview of the application of AI in defect detection for electric vehicles. The FDD method is divided into two steps: feature extraction and fault classification. Feature extraction involves identifying relevant parameters or characteristics from the EV's sensors and signals, enabling the AI system to capture meaningful patterns. Subsequently, fault classification employs AI algorithms to categorize and identify specific faults based on the extracted features, facilitating efficient diagnosis and maintenance of EVs. In the realm of EVs, the combination of AI techniques and FDD has the potential to improve performance, reliability, and safety while enabling proactive maintenance and reducing downtime. Using machine learning and deep learning, we can detect the fault in the system before it starts damaging our EV.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/20830</dc:identifier>
	<dc:identifier>10.11591/ijape.v13.i3.pp653-660</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 13, No 3: September 2024; 653-660</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/20830/13234</dc:relation>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>