Application of dynamic programming to optimal energy management of grid-independent hybrid railcars

In this paper, a dynamic programming-based optimization analysis is performed to quantitatively assess the energy-saving potential associated to the battery hybridization of railcars, which is nowadays regarded as a high-potential solution to reduce the greenhouse-gas impact of regional trains. A computationally effective longitudinal model is first developed, starting from an available energetic macroscopic representation modeling tool. The resulting simplified railcar model was first integrated with a battery state of charge simulator and then embedded within a fast constrained optimization algorithm. The optimization outcomes, reachable within reasonable timeframe and considering cost-effective hybridization scenarios, are highly informative for train manufacturers and operators interested in substantial hybridization of railcars. Particularly, fuel savings as high as 18% can be achieved with respect to the current diesel-powered trains. As a consequence, useful design and control guidelines are made available, considering both retrofitting or replacing the existing railway systems (particularly focusing on regional trains), while coping with capital and operating costs, safety, and traffic constraints.