Efficient Multidimensional Dynamic Programming-Based Energy Management Strategy for Global Composite Operating Cost Minimization for Fuel Cell Trams

Abstract

Energy management is key to improve the performance of hybrid system. At present, the global optimization-based energy management strategy (EMS) is considered to have the best optimization effect under a known operating condition. However, the traditional global optimization-based EMS is often very time-consuming, which brings great inconvenience to debug and obtain the optimal result. Besides, in the literature, most works still introduce only the fuel consumption into the objective function while failing to consider the durability of fuel cells. In order to make up for these shortcomings, based on the modeling of hybrid system, this article first proposes a global composite operating cost minimization strategy (CMS) both considering fuel consumption and depreciation of purchase cost of fuel cells. Then, this article proposes computationally efficient multidimensional dynamic programming (EMDP) to resolve the optimal control trajectory of the studied issue. Finally, based on the hardware-in-the-loop platform, this article carries out comparative tests of the proposed EMDP algorithm and CMS strategy with traditional ones. Results show that the EMDP algorithm can effectively speed up the computation compared with the traditional dynamic programming. Besides, the proposed CMS strategy would lead to lower operating cost mainly by reducing the degradation cost of fuel cells so that the lifetime of fuel cells could also be extended.