Article
DATA DRIVEN ENERGY ECONOMY PREDICTION FOR ELECTRIC CITY BUSES
The electrification of transportation systems is increasing rapidly, and city buses offer significant potential in this transition. A deep understanding of real-world driving data is essential for improving vehicle design and optimizing fleet operations. Several technological factors must be considered to operate alternative powertrain systems efficiently. Uncertainty in energy demand often leads to conservative vehicle design, which can result in inefficiencies and higher operational costs. Due to the complexity and interdependence of various parameters, both industry and academic researchers have faced challenges in developing analytical solutions for this problem. Accurate prediction of energy demand can enable cost reduction and improved operational planning. This paper focuses on increasing transparency in the energy consumption behavior of battery electric buses (BEB). A new set of explanatory variables is introduced to describe speed profiles, which are then applied within machine learning models for prediction. The study develops and evaluates five different algorithms in terms of prediction accuracy, robustness, and practical applicability. The proposed models achieved prediction accuracy of more than 94%, showing strong performance when combined with carefully selected features. The presented methodology offers valuable potential for manufacturers, fleet operators, and urban communities to support the transition toward efficient and sustainable public transportation systems.
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