EFFICIENT BATTERY FAULT MONITORING IN ELECTRIC VEHICLES

Abstract

The accelerating global adoption of electric vehicles (EVs) demands sophisticated battery health monitoring solutions to address critical safety and reliability challenges. This paper presents a novel multi-modal fault detection system for lithium-ion batteries that combines advanced sensor fusion with hierarchical machine learning architecture. Our framework employs distributed sensing of 14 critical parameters including individual cell voltage (±0.1% accuracy), temperature gradients (0.5°C resolution), and electrochemical impedance spectra (1kHz-10mHz range). The system implements a hybrid AI approach featuring: (1) convolutional neural networks for spatial feature extraction from sensor arrays, (2) long short-term memory networks for temporal pattern recognition, and (3) random forest classifiers for real-time fault categorization. Experimental validation across 1,200 charge-discharge cycles demonstrated superior performance metrics: 98.2% detection accuracy for overvoltage events (response time = 42ms), 95.7% for thermal anomalies (detectable at 2°C above baseline), and 90.3% for nascent internal shorts (identified 8.5±2.1 minutes before critical failure). The system reduces false positives by 32.7% compared to conventional threshold-based methods while maintaining computational efficiency (<75MB memory footprint) suitable for edge deployment. Key innovations include adaptive fault thresholds that evolve with battery aging and a federated learning architecture that improves model accuracy across diverse operating conditions. Practical implementation challenges are analyzed, including sensor drift compensation techniques and optimization strategies for embedded hardware. This research establishes a new benchmark for intelligent battery monitoring, providing both immediate safety benefits and enabling predictive maintenance capabilities that could extend pack lifespan by 18-22%.

Author

Dr.Cyril Mathew O, Mohamed Kalith M, Ashik J, Bharath D, Mohamed Aspar S