The heavy-duty freight sector finds itself in a state of change. Legislation and customer demand pushes the industry towards electrification, where the pure battery and fuel cell electric hybrid have emerged as the top contending technologies to replace the conventional diesel powertrain, and although pure battery powertrains dominate the light-duty sector, projections indicate that fuel cell hybrids will show superior performance in long-range missions with heavy cargo. Particularly so in the context of future autonomous vehicles, considering the potential for continuous, non-stop driving. Regardless, several techno-economic challenges remain before widespread adoption of either pure battery powertrains or fuel cell hybrids in the heavy-duty sector. These challenges include but are not limited to the weight of lithium cells, elevated hydrogen prices, insufficient recharging/refuelling infrastructure, durability, as well as thermal management related issues.

A model-based approach can be used to target these challenges, which motivated the development of the Electrochemical Commercial Vehicle (ECCV) platform; a model library tailored for controls algorithm development and rapid virtual prototyping of electrified trucks. While auto-manufacturers use in-house and proprietary software to solve similar tasks, the open-source nature of the ECCV-platform allows for collaboration within academia and industry without issues relating to intellectual property rights, a type of collaboration which was demonstrated in a benchmark competition held at the IFAC World Congress 2023, where six teams from universities all over the world contributed their solutions to the fuel cell hybrid energy management problem.

Although the development of the ECCV-platform constitutes a major part of the thesis, further work was done to improve its capacity. First, the platform was extended with the capability to model state-of-the-art thermal systems, specifically through the inclusion of refrigerant models. This extension allowed for an investigation into the effectiveness of various heat pump systems for pure battery trucks. Secondly, the platform was extended with a fuel cell model validated in the intermediate temperature range, which enabled an investigation of how elevated stack temperatures may benefit overall system efficiency. Thirdly, the model library was used to develop a real-time energy management algorithm for fuel cell trucks, demonstrating the value of the platform also in the context of optimal control.