An MHO Approach for Electric Bus Charging Scheme Optimization Based on Energy Consumption Estimation

In recent years, the increasing concerns on environmental issues have promoted the development of alternatives for urban mobility and public transportation. Specifically, the electric buses (EBs) have attracted significant attention due to the flexibility, sustainability, and excellent performance in terms of reducing greenhouse gas emissions. Because of the capital cost of the EB batteries, how to optimize the charging scheme of EBs in order to extend the battery lifetime is a major challenge faced by the public transit service providers. This challenge is complicated due to the highly dynamic and random energy consumption in the driving process, which is caused by engine torque, friction force, aerodynamic drag force, road condition, real-time speed, acceleration, and other related physical parameters. In order to address this challenge, a moving horizon optimization (MHO) approach is developed in this paper to optimize the charging scheme and extend the battery lifetime. Based on a detailed physical model involving the aforementioned stochastic variables, an energy consumption estimation model is proposed as a significant step in the optimization process. The performance of the proposed approach is evaluated via extensive simulations based on the actual Google transit data from Edmonton Transit Service (ETS).