Machine Learning-Based State-of-Charge Prediction for Electric Bus Fleet: A Critical Analysis

The transportation sector is undergoing a rapid energy transition. Electric Vehicles (EVs) are gradually replacing conventional ones in many different sectors, but battery management still represents a critical limitation of this process. Consequently, research in this area is expanding, aiming to develop solutions that enhance performance while minimizing environmental impact. This study addresses the application of Machine Learning (ML) techniques to estimate the battery State of Charge (SoC) for a full-electric bus fleet operating public service. The methodology is built based on the available driving data disclosed from the fleet monitoring system. The ML methods are assessed starting from model-based (MB) observers assumed as reference and performances are compared upon this basis. The datasets are retrieved from a public repository or derived from real cases, particularly referring to an electric bus fleet operating for an urban public service. The most critical limitation is the absence of the electrical input data coming from the battery, typically required by model-based approaches. Despite this, the proposed ML model achieved sufficient accuracy levels (RMSE < 0.3%) comparable to those of traditional observers. These outcomes demonstrate the potential of data-driven approaches to provide scalable and more straightforward tools for battery monitoring.