Introduction

Model development

Model validation

Parametric study

Conclusion

References

Abstract

Fuel cell vehicles offer significant sustainability benefits by eliminating tailpipe emissions, increasing powertrain efficiency, and utilizing hydrogen that can be supplied from various sources including renewables. A pressure regulator in the hydrogen storage system on a fuel cell vehicle is an important component to ensure that the hydrogen delivery to the fuel cell stack meets the pressure and temperature requirements. A validated model of the regulator can be used to support the product design and optimization of the operating strategy. In this work, a pressure regulator model has been developed to capture the hydrogen discharge behaviors from the compressed hydrogen tank to the fuel cell stack. The focus of the model is to develop the pressure and temperature relationship at the regulator outlet given the inlet conditions from the storage tank. Besides the ideal-gas based derivation for pressure response, the model has used a constant-enthalpy approach to capture the hydrogen temperature increase associated with the pressure drop due to the Joule–Thomson effect. The model was validated with various testing data including hysteresis and dynamic flow conditions, showing satisfactory agreement. The validated model was then used for parametric studies. The modeling results concluded that the regulator inlet temperature has the strongest influence on raising the outlet temperature, while the regulator inlet pressure is an important factor although secondary to the inlet temperature. The comprehensive regulator modeling developed in this work provides the foundation for assessing and optimizing a key dynamic component in the hydrogen storage system.

Introduction

A fuel cell vehicle is regarded as an important zero-emission alternative that features comparable driving range and refilling time with internal-combustion-engine vehicles [1]. There are many model-based studies to improve the fuel cell system and control in a vehicular application [2e6]. Bao et al. [2,3] developed a dynamic model of fuel cell system to control and optimize the transient behaviors of air supply and hydrogen recirculation considering the mixed effects of gas flow, pressure and humidity. Hatti and Tioursi [4] demonstrated an artificial intelligence technique to control a proton exchange membrane fuel cell system process using a dynamic neural network. The anode recirculation system has been studied with theoretical modeling by Dadvar and Afshari [5], focusing on the optimization of stack and ejector design parameters. The pressure control components play an important role for the desired system performance. Hong et al. [6] presented a control oriented dynamic model for the fuel delivery system with anode recirculation and anode bleeding. Based on the model, a multi-input-multi-output nonlinear state feedback controller along with an optimized output feedback controller is proposed to maintain adequate hydrogen supply and suitable anode hydrogen concentration. However, as a typical simplification, an ideal behavior of reaching desired pressure from hydrogen tank without fluctuation and delay was assumed in these studies. Realistically, the dynamic response of pressure regulators and valves upon load change could influence the performance of system components and stack. For example, the primary flow rate as well as the recirculation ratio of an anode ejector is dependent on the upstream pressure [7]. Insufficient hydrogen supply upon load change could also lead to anode reversal and durability concern such as carbon corrosion [8], besides impacting the stack performance [9] and the accuracy of the abovementioned control strategies. Some component optimization for anode pressuremanagement has been performed.