TY - GEN
T1 - Energy Optimization of Hybrid electric Vehicles Using Deep Q-Network
AU - Yokoyama, Takashi
AU - Ohmori, Hiromitsu
N1 - Funding Information:
This work is the result of a collaborative research program with the research association of Automotive Internal Combustion Engines (AICE). The authors gratefully acknowledge the concerned personnel.
Publisher Copyright:
© 2022 The Society of Instrument and Control Engineers - SICE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deep Q-Learning
KW - Hybrid electric Vehicle
KW - Optimization problem
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U2 - 10.23919/SICE56594.2022.9905847
DO - 10.23919/SICE56594.2022.9905847
M3 - Conference contribution
AN - SCOPUS:85141166384
T3 - 2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
SP - 827
EP - 832
BT - 2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2022
Y2 - 6 September 2022 through 9 September 2022
ER -