Integrating Thermodynamic Simulation and Surrogate Modeling to Find Optimal Drive Cycle Strategies for Hydrogen-Powered Trucks

Hydrogen-powered heavy-duty trucks have a high potential to significantly reduce CO2 emissions in the transportation sector. Therefore, efficient hydrogen storage onboard vehicles is a key enabler for sustainable transportation, as achieving high storage densities and extended driving ranges is essential for the competitiveness of hydrogen-powered trucks. Cryo-compressed hydrogen (CcH2), stored at cryogenic temperatures and high pressures, emerges as a promising solution. This study presents a comprehensive dynamic thermodynamic model that is capable of simulating the tank system across all operating conditions and, therefore, enables thermodynamic analysis of drive cycles. The core of the model is a differential-algebraic equation system that describes the thermodynamic state of the hydrogen in the tank. Additionally, surrogate models based on artificial neural networks are applied to efficiently describe quasi-steady-state heat exchangers integrated into the tank system. Several use cases are explored to demonstrate the model's ability to simulate the thermodynamic behavior and to find optimal operating strategies. The optimal hydrogen density, when to stop driving and refuel the tank to maximize overall driving ranges, is investigated both in ideal and real operation, taking into account the limited availability of refueling stations in early market applications. Further, driving range and venting losses are considered for longer periods of dormancy. These results provide insights into how operational strategies can be tailored to maximize driving range, minimize hydrogen losses, and improve overall system efficiency, ultimately supporting the adoption of hydrogen in long-haul transportation.