Optimizing electric bus performance via predictive maintenance: a combined experimental and modeling approach

This study explores the optimization of Electric Bus (EB) performance by integrating predictive maintenance strategies, utilizing real-time data and advanced modeling techniques.The study involves installing measurement sensors to capture dynamic behavior and energy consumption during actual road trips, analyzing the collected data to refine vehicle dynamics models and assess battery degradation under various operational conditions, and employing a multi-objective optimization framework to minimize battery degradation while ensuring efficient energy use and maintaining operational requirements.The study offers valuable insights into battery management strategies, revealing that battery degradation can be reduced by 25% through optimum driving behavior, which can be achieved in real driving conditions by avoiding aggressive driving. This research supports the broader goal of promoting sustainable public transportation solutions through the effective use of electric buses, enabling operators to extend battery longevity and enhance overall vehicle performance by implementing the identified strategies.