This thesis mainly focuses on parallel hybrid electric vehicle energy management problems considering fueling control and air path dynamics of a diesel engine. It aims to explore the concealed fuel-saving potentials in conventional energy management strategies, by employing detailed engine models. The contributions of this study lie on the following aspects: 1) Fueling control consists of fuel injection mass and timing control. By properly selecting combinations of fueling control variables and torque split ratio, engine efficiency is increased and the HEV fuel consumption is further reduced. 2) A transient engine model considering air path dynamics is applied to more accurately predict engine torque. A model predictive control based energy management strategy is developed and solved by dynamic programming. The fuel efficiency is improved, comparing the proposed strategy to those that ignore the engine transients. 3) A novel adaptive control-step learning model predictive control scheme is proposed and implemented in HEV energy management design. It reveals a trade-off between control accuracy and computational efficiency for the MPC based strategies, and demonstrates a good adaptability to the variation of driving cycle while maintaining low computational burden. 4) Two methods are presented to deal with the conjunction between consecutive functions in the piece-wise linearization for the energy management problem. One of them shows a fairly close performance with the original nonlinear method, but much less computing time. / Thesis / Doctor of Philosophy (PhD)

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