A Quantum Variational Classifier for Predictive Maintenance and Monitoring of Battery Health in Electric Vehicles

In order to ensure the dependability and durability of Electric Vehicles (EVs), there is an increasing requirement for improved maintenance systems. This research combines optical and quantum approaches to present a unique methodology for the predictive maintenance of EV components. High-resolution, real-time data on vital components including the battery, electric motor, and power electronics are captured by the Fiber Bragg Grating (FBG) sensors. These sensors are useful because of their high sensitivity, ability to multiplex, and resilience to electromagnetic interference. A Quantum Variational Classifier (QVC) with quantum computing is proposed to manage massive datasets and improve prediction accuracy. The AI model is trained to anticipate possible failures and identify early indicators of component degradation, allowing for proactive maintenance and reducing downtime. The experimental results prove the efficacy of proposed method in providing accurate, appropriate predictions and improving the durability and performance of EV components. The proposed method achieved 0.0035 of MAE and 0.0097 of RMSE when compared with the existing methods.