Wild Gibbon Optimized Sparse Attentive Convolutional Transformer Network for Fault Diagnosis in Electric Vehicles
Main Article Content
Abstract
Fault Detection and Diagnosis (FDD) is critical for maintaining the security and dependability of Electric Vehicles (EVs). The electric motor drive and battery system, along with the EV’s powertrain and energy storage, are essential parts that are susceptible to a variety of malfunctions. Henceforth, this paper presents a fault diagnosis framework dependent on deep learning (DL) and nature-inspired optimization to classify faults with high accuracy. The application uses the NEV Fault Testing Dataset, which contains critical operational signals, including voltage, current, motor speed, temperature,
vibration, and humidity signals. Data normalization is applied for ensuring uniformity across the dataset while improving learning capability of model. Exploratory Data Analysis (EDA) is employed for identifying hidden patterns in the dataset and examining the contribution of each variable to the features’ distribution. Feature engineering is used for extracting meaningful variables that influence fault-related behavior. The proposed novel model, Wild Gibbon Optimized Sparse Attentive Convolutional Transformer Network (WG-Sparse ACTNet), integrates sparse convolutional methods and attention mechanisms for effective and accurate fault classification while the Wild Gibbon Optimization Algorithm (WGOA) is employed for hyper-parameter tuning to further
enhance model accuracy. This model is implemented in Python and evaluated using standard performance metrics, which achieved an accuracy of 99% and a precision, recall, and F1-score of 98%, respectively.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
M. Z. Khaneghah, M. Alzayed and H. Chaoui, “Fault detection and diagnosis of the electric motor drive and battery system of electric vehicles,” Machines, vol. 11, no. 7, pp. 713, Jul. 2023.
C. Wu, R. Sehab, A. Akrad and C. Morel, “Fault diagnosis methods and fault tolerant control strategies for the electric vehicle powertrains,” Energies, vol. 15, no. 13, pp. 4840, Jul. 2022.
K. S. Kavin, P. S. Karuvelam, A. Pathak, T. R. Premila, R. Hemalatha and T. Kumar, “Modelling and analysis of hybrid fuzzy tuned PI controller based PMBLDC motor for electric vehicle applications,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 2, pp. 8-18, Feb. 2023.
S. Sandhiya and I. Abinaya, “Experimental analysis on different effects of PV module with respect to panel angle using machine learning algorithms,” information technology in industry, vol. 9, no. 3, pp. 367-374, Apr. 2021.
D. Li, Z. Zhang, P. Liu, Z. Wang and L. Zhang, “Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model,” IEEE transactions on power electronics, vol. 36, no. 2, pp. 1303-1315, Jul. 2020.
N. Kumarasabapathy, K. S. Kavin, K. Sakthidhasan, G. W. Martin, S. Gr and T. Nadu, “Optimized Speed and Current Controller Based High Speed Switched Reluctance Motor for EV Applications”, Futuristic Trends in Electrical Engineering, IIP Series, vol. 3, Book 1, Part2, Chapter 3, 2024.
V. Vaishnavi, and A. Inbamani, “Perturb and Observe Algorithm Based MPPT Controller for a Stand-Alone PV System Using Iot and Machine Learning Algorithms”, International Research Journal of Modernization in Engineering Technology and Science, 2021.
K. M. Sundaram, A. Hussain, P. Sanjeevikumar, J. B. Holm-Nielsen, V. K. Kaliappan and B. K. Santhoshi, “Deep learning for fault diagnostics in bearings, insulators, PV panels, power lines, and electric vehicle applications—the state-of-the-art approaches,” IEEE Access, vol. 9, pp. 41246-41260, Mar. 2021.
V. Veerasamy, N. I. Abdul Wahab, A. Vinayagam, M. L. Othman, R. Ramachandran, A. Inbamani and H. Hizam, “A novel discrete wavelet transforms based graphical language classifier for identification of high impedance fault in distribution power system,” International Transactions on Electrical Energy Systems, vol. 30, no. 6, pp. e12378, Jun. 2020.
C. S. Wang, I. H. Kao and J. W. Perng, “Fault diagnosis and fault frequency determination of permanent magnet synchronous motor based on deep learning,” Sensors, vol. 21, no. 11, pp. 3608, May 2021.
D. P. Mishra, S. S. Padhy, P. S. Pradhan, S. Gupta, A. Senapati and S. R. Salkuti, “Fault detection and diagnosis of electric vehicles using artificial intelligence,” International Journal of Applied, vol. 13, no. 3, pp. 653-660, Sep. 2024.
L. Yao, S. Xu, Y. Xiao, J. Hou, X. Gong, Z. Fu and A. Tang, “Fault identification of lithium-ion battery pack for electric vehicle based on GA optimized ELM neural network,” IEEE Access, vol. 10, pp. 15007-15022, Jan. 2022.
M. U. I. Khan, M. I. H. Pathan, M. M. Rahman, M. M. Islam, M. A. R. Chowdhury, M. S. Anower, M. M. Rana, M. S. Alam, M. Hasan, M. S. I. Sobuj and M. B. Islam, “Securing electric vehicle performance: Machine learning-driven fault detection and classification,” IEEE Access, vol. 12, pp. 71566-71584, May 2024.
M. Aishwarya and R. M. Brisilla, “Design and fault diagnosis of induction motor using ML-based algorithms for EV application,” IEEE Access, vol. 11, pp. 34186-34197, Mar. 2023.
H. Kaplan, K. Tehrani and M. Jamshidi, “A fault diagnosis design based on deep learning approach for electric vehicle applications,” Energies, vol. 14, no. 20, pp. 6599, Oct. 2021.
K. S. K. Chu, K. W. Chew, Y. C. Chang and S. Morris, “An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three- Phase Permanent Magnet Synchronous Motor (PMSM),” World Electric Vehicle Journal, vol. 15, no. 2, pp. 71, Feb. 2024.
J. Yang, F. Cheng, M. Duodu, M. Li and C. Han, “High-precision Fault Detection for electric vehicle battery system based on bayesian optimization SVDD,” Energies, vol. 15, no. 22, pp. 8331, Nov. 2022.
R. N. Toma, A. E. Prosvirin and J. M. Kim, “Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers,” Sensors, vol. 20, no. 7, pp. 1884, Mar. 2020.
W. Zheng and T. Wang, “Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm,” Archives of Electrical Engineering, pp. 481-498, 2024.
L. L. Li, J. Q. Liu, W. B. Zhao and L. Dong, “Fault diagnosis of high-speed brushless permanentmagnet DC motor based on support vector machine optimized by modified grey wolf optimization algorithm,” Symmetry, vol. 13, no. 2, pp. 163, Jan. 2021.
V. S. R. Kosuru and A. Kavasseri Venkitaraman, “A smart battery management system for electric vehicles using deep learning-based sensor fault detection,” World Electric Vehicle Journal, vol. 14, no. 4, pp. 101, Apr. 2023.
