Guoqiang Li

Optimal Control of Vehicles with Advanced Powertrain System in terms of Energy Efficiency


Front	Back

ISBN:

978-3-8440-7014-9

Series:

Forschungsberichte aus dem Fachgebiet für Elektromobilität
Herausgeber: apl. Prof. Dr.-Ing. Daniel Görges
Kaiserslautern

Volume:

Keywords:

Optimal control; powertrain control; hybrid electric vehicles; adaptive cruise control; machine learning

Type of publication:

Thesis

Language:

English

Pages:

172 pages

Figures:

10 figures

Weight:

254 g

Format:

21 x 14,8 cm

Binding:

Paperback

Price:

48,80 € / 61,10 SFr

Published:

November 2019

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Abstract:

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.