Predictive Energy Management Strategy for Fuel Cell Vehicles Combining Long-Term and Short-Term Forecasts

Abstract

Fuel cell electric vehicles are usually hybrid vehicles requiring an energy management strategy (EMS) to determine the power split between the fuel cell system and a battery. The performance of an EMS can be improved by taking into account forecasts of the vehicle velocity. Simple estimates derived from static route information, e.g., speed limits, can already provide a significant performance increase because they are available before departure and for the entire driving mission. However, such long-term predictions can deviate considerably from the actual velocity because of dynamic influences, such as traffic, roadworks, or weather. Here, short-term predictions from vehicular communication systems provide more accurate real-time information and allow the EMS to react better to the actual driving conditions. This article proposes a predictive EMS that optimally combines the information of long-term and short-term forecasts. Before departure, a dynamic programming algorithm optimizes the energy management based on static route information yielding a distance-based map describing the optimal cost-to-go. While driving, a model predictive controller (MPC) optimizes the energy management in real time considering the short-term prediction and including the optimal cost-to-go representing the long-term information as terminal cost. A computationally efficient linear MPC implementation is proposed, and the significant performance benefit over an MPC that tracks an optimized battery state of charge reference is demonstrated in a numerical study.