| 제목 |
Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles |
| 분야 |
ADAS, AI, Autonomous Vehicles |
| 언어 |
English |
| 저자 |
Dohyun Park(Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center , Hanyang University), Woong Lee(Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center , Hanyang University), JONGRYEOL JEONG(Vehicle & Mobility Systems Department, Transportation and Power Systems Division , Argonne National Laboratory), Dominik Karbowski(Vehicle & Mobility Systems Department, Transportation and Power Systems Division , Argonne National Laboratory), Namwook Kim(Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center , Hanyang University) |
| Key Words |
Autonomous vehicles, Electric vehicles, Dynamic programming, Optimal control, Speed control, Automotive Engineering |
| 초록 |
Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes—acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. This study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption. |
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