Electric bus energy prediction and factors interactions using explainable machine learning models

Battery electric buses (BEBs) are pivotal for sustainable urban transportation, yet their energy consumption is influenced by complex, interrelated factors that challenge accurate estimation and optimization. While machine learning models excel in energy prediction, their "black-box" nature limits practical deployment. This study addresses this gap by developing an interpretable machine learning framework integrating several machine learning models with SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP). Using a high-fidelity simulation dataset of 169,344 scenarios generated via a validated MATrix LABoratory (MATLAB) Simulink model, we systematically analyze energy consumption under diverse driving conditions: covering extreme gradients (−8 % to 8 %), passenger loads (0–75), and Heating Ventilation and Air Condition (HVAC) usage (1.25–22.3 kW) as a proxy for temperature effects. The eXtreme Gradient Boosting (XGBoost) was selected as the best-performing machine learning model. SHAP analysis identified road gradient, initial state of charge (SoC), and Heating Ventilation and Air Condition (HVAC) usage as dominant factors, with nonlinear interactions between average speed and stop density ratio significantly impacting energy use. A human-machine interface (HMI) was developed to translate these insights into actionable recommendations for route optimization and driver training, enabling energy savings with minimal data acquisition costs. This study bridges the gap between theoretical energy models and practical decision-making, offering a robust framework for BEB fleet management while highlighting future directions for integrating real-world environmental data. • We fill the gap in the BEB energy consumption model interpretation. • We developed an interpretable Machine learning model for BEB energy consumption. • SHAP and PDP methods are combined to explain machine learning-based models. • Influence relationships and interactions of energy consumption factors are parsed. • Decision support tools and remarks are offered to fleet managers to reduce energy use.