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

Fuel consumption reduction is one of the main challenges in the automotive industry due to its economical and environmental impacts as well as legal regulations. While fuel consumption reduction is important for all vehicles, it has larger benefits for commercial ones due to their long operational times and much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves an effective approach for reducing energy consumption. Energy is temporarily stored in a buffer when its cost is small and released when it is relatively expensive. An example of an energy buffer is the vehicle body. Before going up a hill, the vehicle can accelerate to increase its kinetic energy, which can then be consumed on the uphill stretch to reduce the engine load. The simple strategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular components with distinct physical disciplines so that they share the same model structure reflecting energy flow. The thesis furthermore improves widely applied control 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 minimization strategy (ECMS) controller (which is one of the well-known optimal energy management methods in hybrid electric vehicles (HEVs)) more robust. • Developing an integrated controller to optimize torque split and gear number simultaneously for both reducing fuel consumption and improving drivability of HEVs. • Developing a one-step prediction control method for improving the gear changing decision. • Studying the potential fuel efficiency improvement of using electromechanical brake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electrically actuated engine cooling system through the off-line global optimization method. • Developing a linear time variant model predictive controller (LTV-MPC) for the real-time control of the electric engine cooling system of heavy trucks and implementing it on a real truck.