Integrated artificial intelligence and predictive maintenance of electric vehicle components with optical and quantum enhancements

The growing demand for electric vehicles (EVs) has necessitated the development of advanced maintenance systems to ensure their reliability, longevity, and cost-effectiveness. This paper presents an innovative approach for the predictive maintenance of EV components by integrating optical and quantum-enhanced artificial intelligence (AI) techniques. The proposed system employs fiber Bragg grating (FBG) sensors to capture high-resolution, real-time data on critical EV components such as the battery, electric motor, and power electronics. These sensors offer numerous advantages, including immunity to electromagnetic interference, high sensitivity, and multiplexing capabilities. To process the acquired data, we employ a quantum-enhanced machine learning algorithm, harnessing the power of quantum computing to handle large-scale data sets and improve prediction accuracy. Our AI model is trained to detect early signs of component degradation and predict potential failures, allowing for proactive maintenance and minimal downtime. The experimental results demonstrate the effectiveness of our approach in achieving accurate, timely predictions, thereby enhancing the overall performance and durability of electric vehicle components. This research paves the way for the development of advanced, efficient, and environmentally friendly transportation systems.