Paper Info
Title
Energy-Saving Optimization and Control of Autonomous Electric Vehicles With Considering Multiconstraints
Abstract
The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle’s powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.
Year
DOI
Venue
2022
10.1109/TCYB.2021.3069674
IEEE Transactions on Cybernetics
Keywords
DocType
Volume
Autonomous driving,electric vehicles,energy optimization,intelligent transportation systems,vehicle motion control
Journal
52
Issue
ISSN
Citations 
10
2168-2267
0
PageRank 
References 
Authors
0.34
30
6
Name
Order
Citations
PageRank
Ying Zhang18720.05
Zhaoyang Ai231.52
jinchao chen372.52
Tao You400.68
Chenglie Du500.68
Deng Lei626228.79