Multi-Agent DRL-Based Resource Scheduling and Energy Management for Electric Vehicles

Author:

Zhang Zhewei1,Yu Chengbo1,Tian Bingxin2

Affiliation:

1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China

2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

With the emergence of vehicular edge computing (VEC) and electric vehicles (EVs), integrating computation and charging tasks presents challenges due to limited resources and dynamic vehicular networks. This research focuses on the joint optimization of computation offloading and charging scheduling in VEC networks. Specifically, we optimize the offloading factor, charging association variable, and charging rates to minimize the system delay and energy consumption by leveraging the multi-attributes of EVs in both information and energy networks. Considering the dynamic environment, we model the problem as a Markov Decision Process, and use the Multi-Agent Reinforcement Learning (MARL) algorithm MADDPG, with its centralized training and distributed execution mechanisms. Simulation results demonstrate that this approach significantly improves utility while reducing energy consumption and latency.

Funder

Research and Innovation Team of Chongqing University of Technology

Chongqing Natural Science Foundation Innovation Development Joint Fund

High-End Foreign Experts Project

Publisher

MDPI AG

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