Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

Hybrid transmissions are characterized by their utilization of more than one form of energy storage. They have the potential to help reduce overall fuel consumption and vehicle emissions by providing the possibility of brake energy recuperation and prime mover operation management. Electric hybrids and electric vehicle drives are nowadays ubiquitous, and mechanical energy storage in flywheel has been investigated in the past. The use of fluid power technology with a combustion engine has also been investigated since the late 1970s, and is frequently revisited. Hydraulic hybridization is especially attractive for heavy vehicles with frequent braking and acceleration which benefit most from fluid power components’ high power density, typically busses, delivery or refuse vehicles, and vehicles with existing hydraulic circuits and transmissions, such as forest and construction machinery, but have been considered for smaller vehicles as well. Due to the characteristic discharge profile of hydraulic energy storage, special attention needs to be paid to control aspects in the design process to guarantee drivability of the vehicle. In this respect, simulation models can be of use in early design process stages for cheaper and faster evaluation of concepts and designs than physical experiments and prototyping, and to generate better understanding of the system studied. Engineering optimization aids in the systematic exploration of a given design space, to determine limits and potentials, evaluate trade-offs and potentially find unexpected solutions. In the optimization of a hydraulic hybrid transmission, the integration of component and controller design is of importance, and different strategies (sequential, iterative, bi-level and simultaneous approaches) are conceivable, with varying consequences for the implementation. This thesis establishes a simulation-based optimization framework for a hydraulic hybrid transmission with series architecture. Component and control parameter optimization are addressed simultaneously, using a rule-based supervisory control strategy. The forward-facing dynamic simulation model at the centre of the framework is built in Hopsan, a multi-disciplinary open-source tool developed at Linköping University. The optimization is set up and conducted for an example application of an on-road light-duty truck over standard drive