Predictive Maintenance Framework For Electric Bus Braking Systems Based On Regenerative Braking Data Analytics - Asanov Seyran (Emerging Frontiers Library for The American Journal of Engineering and Technology, 2025)
Electric buses use regenerative braking a lot to make them more energy-efficient and lower the amount of particles they release into the air. This means that friction braking parts are used less often. This change makes brake parts last longer, but it also changes the thermal cycles and wear patterns, which can cause corrosion, uneven wear, and problems with braking performance that regular maintenance schedules based on time or mileage can't fix. This paper suggests a way to use high-resolution telematics and machine-learning techniques to predict when friction brakes in electric buses will need maintenance. We process operational data like regenerative and hydraulic braking signals, deceleration behavior, thermal cycles, state-of-charge limits, and passenger load estimates to create a Brake Wear Index and train hybrid models that use both Random Forest and LSTM architectures with Weibull reliability estimation. Results show that wear prediction accuracy has improved and that there are up to 40% fewer unplanned maintenance events than with scheduled maintenance methods. The results show how important it is to use regenerative-aware diagnostic analytics to make sure that electric buses run safely, cheaply, and reliably in urban transport networks.