Traffic scenario frozen callback and adaptive neuro-fuzzy inference system based energy management strategy for connected fuel cell buses

Exploring the full potential of energy savings for new energy vehicles in a future connected transportation system is a challenging task. To address how connected buses can leverage surrounding traffic information to improve their energy efficiency, an intelligent fuel cell bus energy management method based on traffic scenario frozen callback is proposed， which enables high real-time performance in online energy management. To tackle the issue of inconsistent data dimensions caused by random fluctuations in the number of vehicles in a fixed traffic flow, a traffic flow representation based on grid grayscale images is designed. Building upon this representation, a speed trajectory prediction model based on traffic scenario frozen callback is developed. Subsequently, offline historical global optimal data are used to construct a training dataset that links speed trajectories to optimal control sequences. An end-to-end energy management framework based on the adaptive neuro-fuzzy inference system (ANFIS) is presented and validated in scenarios that before entering bus station and after exiting bus station. Simulation results demonstrate that, the proposed energy management strategy (EMS) approaches the overall energy consumption of dynamic programming (DP), reaching 97.76 % and 98.82 % in the two kinds of scenarios of its performance, outperforms the other two comparative EMSs. In terms of timeliness, the computational time spent by the proposed EMS is only 0.2076 times and 0.1952 times that of traditional model predictive control (MPC)-based EMS in the separate scenario.