Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Ahn, Kyoungho; Rakha, Hesham A. · 2024 · DOAJ

DOI: 10.3390/en17246360

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Summary

This paper addresses the lack of comprehensive, simple energy models for Hydrogen Fuel Cell Vehicles (HFCVs), which are critical for real-time applications such as smartphone apps, in-vehicle systems, and transportation simulations. While HFCVs offer significant environmental benefits and efficiency advantages over internal combustion engines, existing modeling tools like FASTSim and Autonomie are often too complex or computationally intensive for real-time use. The authors aim to develop a simplified HFCV energy consumption model that leverages easily accessible operational data—specifically real-time vehicle speed, acceleration, and roadway grade—to estimate instantaneous hydrogen fuel consumption, battery energy usage, and overall energy consumption. The study utilizes a disaggregate energy consumption dataset from a 2017 Toyota Mirai, collected by the Argonne National Laboratory. The dataset includes 10 Hz energy consumption data and vehicle operational information under standard temperature conditions (22.2 °C). The model was tested against four driving cycles: the Urban Dynamometer Driving Schedule (UDDS), the Highway Fuel Economy Test (HWFET), the New European Driving Cycle (NEDC), and a constant speed cycle. The proposed model incorporates a novel method for estimating fuel cell driveline efficiency based on the ratio of vehicle power at the wheels to maximum vehicle power. It also accounts for four distinct operational modes: idling, regenerative braking, fuel cell mode, and battery mode. Validation results demonstrate that the model’s forecasts align closely with real-world data. Across all four driving cycles, the average error rates were 0.0% for fuel cell energy and −0.1% for total energy consumption. However, the UDDS cycle exhibited a higher error rate of 13.1%. When validated against independent empirical data, the model achieved an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation. Comparisons with the NREL’s FASTSim model showed a difference of approximately 2.5%. Additionally, the model accurately predicted instantaneous battery state of charge (SOC), closely matching observed measurements. The study further applied the model to assess the energy impact of various intersection controls, confirming its applicability in traffic simulation contexts. The significance of this work lies in providing a practical, versatile alternative to complex simulation tools. By reducing computational requirements while maintaining high accuracy, the model enables real-time energy consumption analysis for HFCVs. This capability supports the integration of HFCV energy modeling into connected and automated vehicle (CAV) applications, traffic simulation programs, and consumer-facing technologies. The study confirms that simplicity in model structure does not compromise accuracy, offering a robust tool for evaluating the energy efficiency and operational impacts of hydrogen fuel cell vehicles in diverse driving scenarios.

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• simulator validity fidelity