Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles

Mihir Trivedi, Riya Kakkar, Rajesh Gupta, Smita Agrawal (), Sudeep Tanwar (), Violeta-Carolina Niculescu (), Maria Simona Raboaca, Fayez Alqahtani, Aldosary Saad and Amr Tolba
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Mihir Trivedi: Department of Electrical Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Riya Kakkar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Rajesh Gupta: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Smita Agrawal: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Violeta-Carolina Niculescu: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uzinei Street, No. 4, P.O. Box 7 Râureni, 240050 Vâlcea, Romania
Maria Simona Raboaca: National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uzinei Street, No. 4, P.O. Box 7 Râureni, 240050 Vâlcea, Romania
Fayez Alqahtani: Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Aldosary Saad: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia

Mathematics, 2022, vol. 10, issue 19, 1-22

Abstract: The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R 2 scores of 0.874 and 0.9375.

Keywords: electric vehicle; convolutional neural network; long-short term memory; fault detection; blockchain; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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