AI-Based Fault Detection in EV Powertrain Using Sensor Data

Abstract

Electric vehicle (EV) powertrains are complex multi-domain systems whose safe and efficient operation demands continuous monitoring of electrical, thermal, and mechanical parameters. Conventional rule-based Battery Management Systems (BMS) rely on fixed voltage, current, and temperature thresholds that are inadequate for detecting subtle multivariate fault signatures and early-stage degradation. This paper presents an AI-based fault detection framework that processes multi-channel EV powertrain sensor data—battery voltage, DC bus current, three-phase motor currents, inverter temperature, rotor speed, torque demand, and State-of-Charge (SoC)—using a sliding-window statistical feature extraction approach coupled with an XGBoost gradient-boosted classifier. Six statistical features (mean, standard deviation, RMS, minimum, maximum, and least-squares slope) are computed per window per channel, yielding a 42-dimensional feature vector. The trained binary classifier achieves 97.4% test accuracy with macro-average F1-score of 0.970 and AUC-ROC exceeding 0.990 across all fault classes. A Flask-based web dashboard provides real-time visualization of sensor signals, predicted fault probability, and traffic-light risk indicators. Results confirm that the system detects incipient overcurrent, undervoltage, and thermal overload faults with a median latency of 0.43 s—significantly earlier than conventional threshold-based methods— offering a practical, deployable foundation for predictive maintenance in electric mobility.

Introduction

The text discusses the growing importance of intelligent fault detection systems in electric vehicles (EVs), whose powertrains consist of critical components such as lithium-ion batteries, inverters, permanent-magnet synchronous motors (PMSMs), and power electronics. These components are vulnerable to issues like overcurrent, thermal overload, insulation degradation, and battery imbalance, which can reduce performance or even lead to dangerous thermal runaway events. Traditional Battery Management Systems (BMS) rely on fixed threshold-based monitoring, which often fails to detect gradual or correlated faults and can generate excessive false alarms.

To overcome these limitations, the proposed system introduces an AI-based EV fault detection platform that uses machine learning for real-time monitoring and predictive fault analysis. The system employs XGBoost, a gradient-boosted tree ensemble model known for high accuracy, robustness to class imbalance, and fast inference suitable for embedded systems. The platform combines three key contributions: a seven-channel sliding-window feature extraction pipeline, an XGBoost classifier achieving 97.4% accuracy across four operating conditions, and a Flask-based real-time monitoring dashboard with probabilistic risk indicators and fault event logging.

The literature review highlights previous approaches to EV fault detection, including model-based systems using Kalman filters and SVMs, vibration analysis for motor diagnostics, battery state-of-charge estimation, and machine learning methods such as Random Forests and gradient-boosted classifiers. Earlier systems often focused on single components, depended heavily on accurate physical models, or lacked real-time deployment capability. The proposed system addresses these gaps by jointly analyzing multiple powertrain channels in a unified framework integrated with a live dashboard.

The methodology begins with sensor data acquisition from seven powertrain channels, including battery voltage, motor currents, inverter temperature, rotor speed, torque demand, and battery state of charge (SoC). Data is collected at 100 Hz and preprocessed using median filtering and z-score normalization to reduce noise and standardize measurements. A sliding-window approach extracts statistical features such as mean, standard deviation, RMS, minimum, maximum, and trend slope from each channel, producing a 42-dimensional feature vector.

The extracted features are then analyzed using the XGBoost classifier to estimate fault probabilities. Based on the predicted probability, the system categorizes the vehicle condition into Safe, Warning, or Fault states. Additional power quality indicators, such as Total Harmonic Distortion (THD) and Crest Factor (CF), are computed to detect inverter or motor winding faults. Battery SoC is estimated using a Coulomb-counting model with voltage correction to identify issues like cell imbalance or internal short circuits.

Finally, the system integrates all monitoring and prediction functions into a Flask-based web dashboard, enabling real-time visualization of EV health, fault probabilities, and event logs. Overall, the proposed platform provides a scalable, accurate, and deployable AI-driven solution for improving EV safety, reliability, and predictive maintenance.

Conclusion

This paper presented an end-to-end AI-based fault detection system for EV powertrains combining sliding-window statistical feature engineering with an XGBoost classifier and a Flask real-time monitoring dashboard. Processing seven powertrain sensor channels at 100 Hz, the system achieves 97.4% classification accuracy and a median detection latency of 0.43 s, demonstrating clear superiority over conventional threshold-based BMS. Feature importance analysis confirms that DC bus current slope, phase-current RMS, and inverter temperature mean are the dominant fault discriminators, providing physically interpretable diagnostic insight for maintenance engineers. The three-level probabilistic alert policy (Safe / Warning / Fault) enables proactive intervention before fault severity escalates to component damage or safety hazards. The modular system architecture—separating data acquisition, feature extraction, ML inference, and visualization layers—facilitates independent upgrade of individual components as deployment requirements evolve. Future work will pursue: (i) real EV test-bench validation via CAN bus hardware-in-the-loop (HIL) integration; (ii) deep learning architectures (1D-CNN, LSTM) for implicit temporal feature learning to reduce manual feature engineering; (iii) multi-class fault severity grading and remaining useful life (RUL) prognostics; (iv) model quantization and edge deployment on ARM-based microcontrollers for on-board sub-10 ms latency; and (v) federated learning across an EV fleet for cross-vehicle model robustness without centralizing raw sensor data.

References

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Copyright

Copyright © 2026 Mr. Baratam Murali, S. Dhanalakshmi , P. Shanmukh, M. Sharmila, R. Venkatesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.