Energy Optimization of Hybrid electric Vehicles Using Deep Q-Network

Takashi Yokoyama, Hiromitsu Ohmori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hybrid electric vehicles are positioned as an intermediate form between gasoline and electric vehicles, contributing to lower fuel consumption and emissions. Map control is used for conventional engine control. This method maps optimal values from experimental data, and it has been pointed out that the capacity of the map is increasing and that it is difficult to respond to increasingly sophisticated control objectives. In this paper, we first present a model of a series-parallel hybrid electric vehicle and propose a method using Deep Q-Network, a typical reinforcement learning technique. Through numerical simulation, we verify that the SOC is within an acceptable range throughout the entire run and that energy efficiency can be improved compared to existing map control.

Original languageEnglish
Title of host publication2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages827-832
Number of pages6
ISBN (Electronic)9784907764784
DOIs
Publication statusPublished - 2022
Event61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022 - Kumamoto, Japan
Duration: 2022 Sept 62022 Sept 9

Publication series

Name2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022

Conference

Conference61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
Country/TerritoryJapan
CityKumamoto
Period22/9/622/9/9

Keywords

  • Deep Q-Learning
  • Hybrid electric Vehicle
  • Optimization problem

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Control and Optimization
  • Instrumentation

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