Machine learning prediction models for battery-electric bus energy consumption in transit

Abstract

Road gradient (g), initial state of charge (SoCi), drag coefficient (Cd), road condition (RC), HVAC, driver aggressiveness (Dagg.), passenger loading (PL), stop density (SD), average speed (Va), Multiple Regression Analysis (MLR), Radial Basis Function (RBF), Decision Tree Model (DT), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Multilayer Perception Neural Network (MLP-NN), and Radial Basis Neural Network (RBNN) • We developed and validated a simulation model to estimate the E C of BEBs. • A factorial experimental design generated BEB operation scenarios. • Seven data-driven models are developed to predict BEB energy consumption. • The models accommodate vehicular, operational, topological, and external parameters. • Multiple Regression and support vector machine models are deemed appropriate to predict BEB’s E C . The energy consumption (E C ) of battery-electric buses (BEB) varies significantly due to the intertwined relationships of vehicular, operational, topological, and external parameters. This variation is posing several challenges to predict BEB’s energy consumption. Several studies are calling for the development of data-driven models to address this challenge. This study develops and compares seven data-driven modelling techniques that cover both machine learning and statistical models. The models are based on a full-factorial experimental design ( n = 907,199 ) of a validated Simulink energy simulation model. The models are then used to predict E C using a testing dataset ( n = 169,344 ). The results show some minor discrepancies between the developed models. All models explained more than 90% of the energy consumption variance. Further, the results indicate that road gradient and the battery state of charge are the most influential factors on E C , while driver behaviour and drag coefficient have the lowest impact.