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48,80 €ISBN 978-3-8440-7014-9Softcover172 pages10 figures254 g21 x 14,8 cmEnglishThesis
November 2019
Guoqiang Li
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 like advanced vehicle powertrain systems and intelligent transportation systems
during the last years. 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, energy management strategies for parallel hybrid electric vehicles (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. Finally, ecological adaptive cruise controllers are designed for HEVs and conventional vehicles separately to improve the fuel economy and the driving safety.
Keywords: Optimal control; powertrain control; hybrid electric vehicles; adaptive cruise control; machine learning
Forschungsberichte aus dem Fachgebiet für Elektromobilität
Edited by apl. Prof. Dr.-Ing. Daniel Görges, Kaiserslautern
Volume 1
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