Instantaneous Energy Consumption Estimation for Electric Buses With a Multi-Model Fusion Method

The accurate instantaneous energy consumption estimation for electric buses (EBs) is helpful for drivers to schedule and use EBs more reasonably, which is also essential for the realization of electrification, intelligence, and cyber-physical integration of public transportation systems. In this paper, real-world driving data collected from ten electric buses in Beijing city are obtained, and the influencing factors of energy consumption from multiple sources such as driving-related, vehicle status, and external environment are extracted. With the extracted key influencing factors, we propose a new multi-model fusion method to estimate the 1 Hz energy consumption of electric buses. The results demonstrate that the proposed fusion model has a mean absolute percentage error of 6.21%, which is superior to the single model. Finally, we use the control variable method for feature importance analysis, which provides valuable insights into the factors that affect the energy consumption of EBs in real-world scenarios.