Improving Fuel Efficiency of Commercial Vehicles through Optimal Control of Energy Buffers

Sammanfattning: Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption.Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption.The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications.The contribution of the thesis can be summarized as follows:• Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust.• Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs.• Developing a one-step prediction control method for improving the gearchanging decision.• Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus.• Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod.• Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck.