Co-optimization of total running time, timetables, driving strategies and energy management strategies for fuel cell hybrid trains

A co-optimization of the total running time, timetables, driving strategies and energy management is implemented for the world’s first commercial fuel cell train Coradia iLint in this contribution. Thereby, the forward dynamic programming algorithm is applied to co-optimize the driving strategies between two stations, the running time distribution in various railway sections and the entire journey’s running time. Through parallelization of the algorithm, the computational time is reduced. For energy management, a rule-based strategy utilizing the convexity of the fuel cell system’s consumption curve is introduced. The co-optimization of train control and energy management is realized using a sequential algorithm to achieve decoupling. As a result, the number of state variables while using dynamic programming for the co-optimization is maintained at two. Through the co-optimization of the running time, timetables and driving strategies, the optimal running time is determined, which is about 8 min less than the existing time table while consumes 1.8 % less energy. Furthermore, through the co-optimization of the speed profiles and the energy management, evident energy consumption decreases if a short running time is required. Thereby, the hydrogen consumption decreases by 3.8 % after the co-optimization compared to that before co-optimization under the minimal drive time of 4615 s for a total distance of 82.6 km. •Co-optimization of running time, train control and timetabling for fuel cell trains.•Forward dynamic programming algorithm utilized to realize flexible arrival time.•Parallelization of dynamic programming to reduce computation time.•Co-optimization of driving cycles and energy management for fuel cell trains.•Reduction of energy consumption with the travel time decreased at the same time.