Year: 2020
Type: article
Abstract: With the development of autonomous vehicles, research on energy-efficient eco-driving is becoming increasingly important. The optimal control problem of determining the speed profile of the vehicle for minimizing energy consumption is a challenging problem that necessitates the consideration of various aspects, such as the vehicle energy consumption, slope of the road, and driving environment, e.g., the traffic and other vehicles on the road. In this study, an approach using reinforcement learning was applied to the eco-driving problem for electric vehicles considering road slopes. A novel model-based reinforcement learning algorithm for eco-driving was developed, which separates the vehicle's energy consumption approximation model and driving environment model. Thus, the domain knowledge of vehicle dynamics and the powertrain system is utilized for the reinforcement learning process, while model-free characteristics are maintained by updating the approximation model using experience replay. The proposed algorithm was tested via a vehicle simulation and compared with a solution obtained using dynamic programming (DP), and as well as conventional cruise control driving with constant speed. The simulation results indicated that the speed profile optimized using model-based reinforcement learning had similar behavior to the global solution obtained via DP and energy saving performance compared with cruise control.
Source: IEEE Access
Authors: Heeyun Lee, Namwook Kim, Suk Won Cha
Institutions: Seoul National University, Hanyang University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
OpenAlex ID: W3105511950