Energy management strategies can improve fuel cell hybrid electric vehicles’ dynamic and fuel economy, and the strategies based on model prediction control show great advantages in optimizing the power split effect and in real time. In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short-term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller’s real-time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real-time performance, and the prediction horizon of around 20 s was appropriate for this strategy.
This paper presents a methodology for the sizing of a heavy-duty fuel cell commercial vehicle. The parameters scanning model and the long-term stochastic drive cycle are adopted for this proposed sizing framework. The dynamic programming algorithm is employed as the energy management strategy to assess the performance of sizing. The efficacy of this framework is evaluated, and a detailed analysis for the hydrogen consumption is given in the results. In addition, a prediction analysis based on the price performance of the next decade is also given in this work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.