Optimal control of vehicles with advanced powertrain system in terms of energy efficiency

Depleting energy resources and growing environmental problems necessitate the reduction of the fuel consumption of road vehicles. This requirement has strongly promoted novel technologies in the automotive industry during the last years. Advanced vehicle transmissions and optimal shift control logic can lead to high transmission efficiency and energy saving. Hybrid electric vehicles (HEVs) make use of the electricity energy to reduce the fuel consumption. In order to improve the driving comfort, reduce the fuel consumption and emissions, model-based and learning-based optimal control methods for road vehicles with advanced powertrain systems are studied in three different dimensions. First, to improve the shift process with fast and smooth operation for dual clutch transmissions, a linear quadratic regulator based control method is proposed to optimize trajectories of the clutch torque and the input shaft torque. Furthermore, an energy management strategies for parallel HEVs including the gear shift and power split are optimized for fuel consumption minimization with dynamic programming (DP) and adaptive dynamic programming (ADP) respectively. The model-based method utilizes a varying weighting factor within DP and model predictive control for multi-objective optimization. The online learning for the energy management strategy is realized by the ADP-based approaches. The controller is adaptive, robust and realizes near fuel-optimality. Finally, ecological adaptive cruise controllers are designed for HEVs and conventional vehicles separately to improve the fuel economy and driving safety. The proposed methods are model-free and able to adapt to various driving behaviors as well as parameter change during the vehicle lifetime.