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A Real-Time Intelligent Energy Management Strategy for Hybrid Electric Vehicles Using Reinforcement Learning

Title
A Real-Time Intelligent Energy Management Strategy for Hybrid Electric Vehicles Using Reinforcement Learning
Author
이웅
Keywords
Energy management strategy; adaptive ECMS; machine learning; reinforcement learning; hybrid electric vehicles; deep Q-learning; Electrical engineering. Electronics. Nuclear engineering; TK1-9971
Issue Date
2021-05
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v. 9, Page. 72759-72768
Abstract
Equivalent Consumption Management Strategy (ECMS), a representative energy management strategy for hybrid electric vehicles (HEVs) derived from Pontryagin's minimum principle, is known to produce a near-optimal solution if the costate or equivalent factor of electric use is appropriately determined according to the driving conditions. One problem when applying the control concept to real-world scenarios is that it is difficult to precisely evaluate the performance of the control parameter before driving is complete, so the costate cannot be determined properly. To address this issue, this study proposes a practical method for estimating an appropriate costate based on Deep Q-Networks (DQNs), which is a reinforcement learning algorithm that uses a Deep Neural Network to evaluate the performances and determine the best control parameter or costate. The control concept benefits vehicle energy management by selecting the control parameter most related to stochastic conditions or future driving information based on artificial intelligence (AI), while optimal control is deterministically conducted by ECMS if the control parameter is given. Simply, only the implicit part of the optimal controller is solved via artificial intelligence. In the simulation results, not only does the proposed control concept outperform an existing ECMS that uses an adaptive technique for determining the costate, but the concept is also very feasible, in that it does not need a model for evaluating the performances.
URI
https://doaj.org/article/6519dd60c3f54393b665ad18b95f41f4https://repository.hanyang.ac.kr/handle/20.500.11754/171714
ISSN
2169-3536
DOI
10.1109/ACCESS.2021.3079903
Appears in Collections:
RESEARCH INSTITUTE[E](부설연구소) > RESEARCH INSTITUTE OF ENGINEERING & TECHNOLOGY(공학기술연구소) > Articles
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