Study of optimal speed planning and energy management for eco-driving of fuel cell electric trucks

Abstract

The increase in greenhouse emissions from the road freight transport sector has encouraged the search for new, cleaner, and more sustainable alternatives in order to achieve the European goals for environmental pollution. One of the alternatives that is beginning to be establish is the use of hydrogen fuel cells and electric batteries as energy sources for electric motors. Fuel cell electric trucks have the great advantage of being able to make long-distance trips with one single recharge, with a similar range to conventional combustion cars, but without emitting polluting gases into the atmosphere.This master’s thesis focuses on the study of the optimal speed planning and energy management control of a fuel cell electric truck in realistic scenarios. The final goals are to minimize these terms depending on the road topography and the trip time and analyse their trade-off over driving time. Dynamic programming is the method adopted in this work to solve the optimal control problem and ensure the global optimality. However, one of the drawbacks of solving the optimization by dynamic programming is the curse of dimensionality when multiple state and control variables are added to the problem increasing the computational burden. For this reason, it is used a hierarchical strategy instead of a co-optimization of the two problems, decreasing the high complexity of the optimal control problem. Hierarchical optimization split the global control problem into two sub-problems: optimal speed-planning control and energy management control. Speed planning provides the optimal speed distribution along the route to minimize the total energy consumption of the truck, using the elevation profile as input. Energy management uses the speed and power profile data from the speed planning, and it calculates the optimal distribution of fuel cell and battery power to the powertrain. Speed planning control is optimized under motorway speed limits and power motor constraints and the energy management control under fuel cell and battery power limits and the degradation of the battery based on the state of charge. It is also analysed the behaviour of the battery based on the energy released by the internal ohmic resistance of the battery, called ohmic losses. High ohmic losses lead to high temperature which must be managed by the battery thermal management system to reduce the degradation of the components. The thesis shows simulations from different scenarios, varying the load of the truck, using different elevation profiles, as well as penalizing high power motor values and implementing different strategies in the optimal speed planning and energy management control.From the results, it is observed that the optimal speed planning avoids mechanical braking and increases the use of coasting to reduce energy consumption. In addition, it is shown that including ohmic terms in the objective function improve battery behaviour without a significant impact in energy or hydrogen consumption. Future works should model in detail the fuel cell system of the truck considering hydrogen consumption due to high gradients in the fuel cell power, curves of the road, and the design of an online control of the truck in real-time.