Optimal Eco-driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension
Artikel i vetenskaplig tidskrift, 2022
This paper develops a model to plan energy-efficient speed trajectories of electric trucks in real time by taking into account the information of topography and traffic ahead of the vehicle. In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. An energy minimization problem is then formulated and solved by an alternating direction method of multipliers (ADMM) that exploits the structure of the problem. A model predictive control (MPC) framework is further employed to deal with topographic and traffic uncertainties in real-time. An empirical study is finally conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The simulation results show that the energy consumption by using the developed method is reduced by up to 5.05%, and the battery life extended by more than 100% compared to benchmarking solutions.
Optimization
Batteries
heavy duty truck
MPC
battery life extension
speed control
Minimization
Roads
ADMM
Autonomous and electric trucks
Velocity control
State of charge
energy minimization
Energy consumption
Författare
Yongzhi Zhang
Chongqing University
Xiaobo Qu
Transportgruppen
Lang Tong
Cornell University
IEEE Transactions on Transportation Electrification
2332-7782 (eISSN)
Vol. 8 2 2149-2163Ämneskategorier
Transportteknik och logistik
Farkostteknik
Reglerteknik
DOI
10.1109/TTE.2022.3147214