Earticle

초록

영어

This study presents model-free reinforcement learn ing methods for economic and ecological adaptive cruise control (Eco-ACC) of connected and autonomous electric vehicles. For model-free optimal control of Eco-ACC, we applied two reinforcement learning methods, Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), in which deep neural networks of actors and critics were trained using IPG CarMaker simulations. For performance demonstrations, the HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front vehicle, and the energy consumptions of the host vehicle and front vehicle were compared. In high-fidelity IPG CarMaker simulations, the proposed reinforcement learning- based Eco-ACC methods demonstrated approximately 3–5% and 10–14% efficiency improvements in highway and city-highway driving scenarios, respectively, when compared with the front vehicle. A video of the CarMaker simulation is available at https://youtu.be/DIXzJxMVig8.

목차

Abstract
I. INTRODUCTION
II. OPTIMAL CONTROL PROBLEM
III. BACKGROUNDS IN SOLUTION METHODS
IV. SOLUTION METHODS
V. SIMULATION RESULTS
VI. CONCLUSIONS AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

• Sunwoo Kim [ Dept. of Electrical and Computer Engineering Inha University Incheon, Republic of Korea ]
• Kwang-Ki K. Kim [ Dept. of Electrical and Computer Engineering Inha University Incheon, Republic of Korea ]

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