Deep Learning-Based Energy Management of an All-Electric City Bus with Wireless Power Transfer

Abstract

Fuel cell-based hybrid electric vehicles are one of the most promising options to achieve zero-emission city buses. Efficient Energy Management (EM) plays a critical role to make such buses more efficient and practical. In this research, an available all-electric bus consisting of fuel cell (FC) and battery is considered and the efficiency of adding a Wireless Power Transfer (WPT) system to it is assessed. The proposed WPT system is only capable to receive energy in bus stations and use it to supply loads or charge the battery. To this end, the actual data of a city bus, its route and load profile were collected and utilized to ensure a realistic assessment. A full mathematical model of the energy system as well as the constraints governing the management issue is extracted and a Deep Deterministic Policy Gradient (DDPG) method is used to optimally manage the energy flows for the entire journey. All models are implemented in MATLAB software and the efficiency of the proposed system is investigated from economic and technical aspects. The results illustrate a high efficiency for the proposed WPT technique to be used in actual all-electric city buses.