Adaptive optimal control strategy of fuel economy for fuel cell battery storage system using in HEV applications

Fuel cell stack (FCS) is a practical power source for new energy vehicle applications, and fuel economy is a problem that many researchers are concerned about. In this paper, an adaptive real-time control strategy aiming at improving fuel efficiency is proposed; the control purpose is to distribute the power requirement between the FCS and the battery to achieve good fuel economy. First, the FCS model is built according to experiment data, and in order to reflect the affection of the temperature to the proposed control strategy, the thermal model of the battery is established. Then the future power requirement is predicted via Bayes inference analysis. Based on the FCS model, the battery model, and the predicted power requirement, the real-time control strategy is designed and solved with minimization principle optimization over the receding horizon. The proposed control strategy is validated both through simulation and hardware-in-loop (Hil) experiments on a 40 kW FCS. The results compared with the rule-based (RB) strategy and the loss minimum strategy (LMS) show that the proposed control strategy can effectively reduce fuel consumption by 4%, and at the same time, it can help extend the life span of the battery by considering the temperature affection.