State-of-the-Art Technologies in Fault Diagnosis of Electric Vehicles: A Component-Based Review

Choudhary, Anurag; Fatima, Shahab; Panigrahi, Bijaya Ketan

doi:10.1109/tte.2022.3209166

Cited by 20 publications

(3 citation statements)

References 257 publications

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“…This facilitates the early detection of deteriorating vehicle conditions, enabling proactive solutions and minimizing the occurrence of sudden component failures. Reference [105] provides a comprehensive review of various faults and diagnostic methods for EV components, including motors, batteries, inverters/converters, and charging systems. The research into FD across different EV components establishes a conceptual framework for the FD of EVs.…”

Section: Fault Diagnosis Technology and Smart Network Technologymentioning

confidence: 99%

A Review of Modern Electric Vehicle Innovations for Energy Transition

Jiang,

Hsu,

et al. 2024

Energies

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As climate change worsens and the importance of energy security grows, numerous countries have adopted energy transition as a key policy objective. Electric vehicles (EVs) play a pivotal role in this transition by diminishing reliance on fossil fuels and reducing emissions of carbon dioxide and other harmful gases. While EVs are poised to be a crucial technology for achieving energy transition, substantial advancements in technology, infrastructure development, and policy support are essential for their full potential to be realized. This review examines the latest advancements in EV technology and market trends, and also addresses the challenges facing EVs and the potential opportunities for future development.

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Section: Fault Diagnosis Technology and Smart Network Technologymentioning

confidence: 99%

A Review of Modern Electric Vehicle Innovations for Energy Transition

Jiang,

Hsu,

et al. 2024

Energies

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“…Rigorous testing and related endeavors are imperative to ensure meticulous adherence to system design and vehicle safety standards. As elucidated in the referenced literature [27], motors constitute the pivotal components within electric vehicles, serving the purpose of converting electrical energy into mechanical power. However, as operational time accumulates, the susceptibility to motor failures across diverse operational scenarios amplifies, thereby impinging upon the reliability and safety of electric vehicles.…”

Section: Principles Of Establishing An Experimental Platform For Auto...mentioning

confidence: 99%

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Xie,

Li,

Fan

et al. 2023

Processes

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Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.

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“…They used a CNN network to classify the fault combinations. A. Choudhary et al [ 25 ] reviewed SOTA Condition Monitoring and Fault Diagnosis approaches for early identification of failure in electric vehicles and reducing the chance of abrupt failure. They also discussed difficulties for future work on emerging technologies for research activities.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

Misra

Kumar

Sayyad

et al. 2022

Sensors

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The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

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State-of-the-Art Technologies in Fault Diagnosis of Electric Vehicles: A Component-Based Review

Cited by 20 publications

References 257 publications

A Review of Modern Electric Vehicle Innovations for Energy Transition

A Review of Modern Electric Vehicle Innovations for Energy Transition

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data

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