Learning model predictive control for efficient energy management of electric vehicles under car following and road slopes

被引：2
|
作者：
Yeom, Kiwon [1 ]
机构：
[1] Sangmyung Unviers, Dept Human Intelligence & Robot Engn, Cheonan Si, South Korea
来源：
ENERGY REPORTS | 2022年 / 8卷
基金：
新加坡国家研究基金会;
关键词：
Eco-driving; Electric vehicles; Model Predictive Control; Deep Reinforcement Learning; Optimal speed control; Energy consumption; DESIGN;
D O I：
10.1016/j.egyr.2022.10.267
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In this paper, a novel control algorithm is proposed using Model Predictive Control for improving energy consumption of fully electric vehicles (FEVs) and Deep Reinforcement Learning (DRL) for understanding the driving environment in real-time, respectively. The dynamics of the FEVs and the brushless DC motor (BLDC) based powertrain system is utilized for the DRL process, and a Model Predictive Control (MPC) based optimal speed strategy for minimizing battery power consumption is developed by updating the approximation model through the repeated experience. Using the high fidelity car simulator (CarSim), the proposed algorithm is evaluated under freeway cruising scenarios with road slopes as well as preceding car following. The simulation results demonstrate in overall potential energy saving of 3.2%. (C) 2022 The Author(s). Published by Elsevier Ltd.
引用
收藏
页码：599 / 604
页数：6
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