Journal of Microcontroller Engineering and Applications Review Article
Real Time Ai Based Fault Detection in EV
Abstract
Electric vehicles (EVs) are rapidly increasing in usage, but motor faults can affect their performance and safety. This paper presents a real- time AI-based fault detection system using sensor data and embedded processing. The system utilizes an ESP32 microcontroller along with current, vibration, and temperature sensors to continuously monitor motor conditions. An intelligent algorithm analyzes real-time data to detect abnormalities such as overheating, overcurrent, and mechanical faults. When a fault is detected, the system alerts the user through an LCD display and activates a relay to stop the motor. The proposed system enhances safety, improves reliability, and reduces maintenance costs, with experimental results showing accurate and fast fault detection. Additionally, thorough monitoring of mechanical and electrical characteristics is made possible by the integration of several sensors, which supports the diagnosis of various fault circumstances during motor operation. Without the need for sophisticated external computer hardware, the ESP32 offers an effective platform for sensor data collecting and processing, enabling prompt replies. While the relay-based protection mechanism aids in preventing continuous operation under potentially hazardous situations, the LCD provides clear visual indicators of the operational state and identified defects. Additionally, the suggested method encourages ongoing condition monitoring, which can help spot emerging issues before they cause significant component damage. All things considered, this technology provides a workable and flexible way to enhance motor protection for electric vehicles. Future development possibilities offered by its integrated architecture include wireless monitoring, predictive maintenance, and integration with cutting-edge diagnostic methods for improved problem categorization and performance assessment.
Keywords
References (10)
- Kowsalya M. Fault Detection in Electric Vehicles Using Machine Learning Techniques. In2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI) 2025 Jul 9 (pp. 826-831). IEEE.
- Lee J, Bagheri B, Kao HA. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing letters. 2015 Jan 1;3:18-23.
- Awadallah MA, Morcos MM. Application of AI tools in fault diagnosis of electrical machines and drives-an overview. IEEE Transactions on energy conversion. 2003 Jun 30;18(2):245-51.
- Borré A, Seman LO, Camponogara E, Stefenon SF, Mariani VC, Coelho LD. Machine fault detection using a hybrid CNN-LSTM attention-based model. Sensors. 2023 May 5;23(9):4512.
- Zachariades C, Xavier V. A review of artificial intelligence techniques in fault diagnosis of electric machines. Sensors (Basel, Switzerland). 2025 Aug 18;25(16):5128.
- Mishra KM, Huhtala KJ. Fault detection of elevator systems using multilayer perceptron neural network. In2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 2019 Sep 10 (pp. 904-909). IEEE.
- Gültekin Ö, Cinar E, Özkan K, Yazıcı A. Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle. Expert Systems with Applications. 2022 Aug 15;200:117055.
- Mohammadi F, Rashidzadeh R. An overview of IoT-enabled monitoring and control systems for electric vehicles. IEEE instrumentation & measurement magazine. 2021 May 19;24(3):91-7.
- Zhang X, Hu Y, Gong C, Deng J, Wang G. Artificial intelligence technique-based EV powertrain condition monitoring and fault diagnosis: A review. IEEE Sensors Journal. 2023 Jun 23;23(15):16481-500.
- Madan R, Ogaili AA. Intelligent fault diagnosis of EV induction motors using multi-sensor ANN and explainable AI for sustainable transportation. Terra Joule Journal. 2026;2(1):3.