Power Management for a Plug-in Hybrid Electric Vehicle Based on Reinforcement Learning with Continuous State and Action Spaces

Li, Yuecheng; Huang, He; Peng, Jiankun; Zhang, Hailong

doi:10.1016/j.egypro.2017.12.629

Cited by 48 publications

(19 citation statements)

References 8 publications

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“…In the case of supervisory control strategies for an HEV, certain learning based strategies are shown to be comparable to the commonly used control strategies [31]. For continuous-spaces, the actor-critic method was used for the power management in a PHEV [36]. A qualitative study on RL techniques on HEVs and PHEVs shows potential for RL controllers to replace rule based controllers [37].…”

Section: Torque Split Controlmentioning

confidence: 98%

Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning

Harold¹,

Prakash²,

Hofman³

2020

Vehicles

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This paper presents a novel framework to enable automatic re-training of the supervisory powertrain control strategy for hybrid electric vehicles using supervised machine learning. The aim of re-training is to customize the control strategy to a user-specific driving behavior without human intervention. The framework is designed to update the control strategy at the end of a driving task. A combination of dynamic programming and supervised machine learning is used to train the control strategy. The trained control strategy denoted as SML is compared to an online-implementable strategy based on the combination of the optimal operation line and Pontryagin’s minimum principle denoted as OOL-PMP, on the basis of fuel consumption. SML consistently performed better than OOL-PMP, evaluated over five standard drive cycles. The EUDC performance was almost identical while on FTP75 the OOL-PMP consumed 14.7% more fuel than SML. Moreover, the deviation from the global benchmark obtained from dynamic programming was between 1.8% and 5.4% for SML and between 5.8% and 16.8% for OOL-PMP. Furthermore, a test-case was conducted to emulate a real-world driving scenario wherein a trained controller is exposed to a new drive cycle. It is found that the performance on the new drive cycle deviates significantly from the optimal policy; however, this performance gap is bridged with a single re-training episode for the respective test-case.

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Section: Torque Split Controlmentioning

confidence: 98%

Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning

Harold¹,

Prakash²,

Hofman³

2020

Vehicles

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“…Due to the low computational complexity of neural network, Li et al [60] presented a power management strategy based on reinforcement learning without worrying curse of dimensionality in complex environments, where stochastic gradient descent and experience replay were adopted to guarantee the accuracy and stability of the method.…”

Section: 2mentioning

confidence: 99%

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

Jiang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed.

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“…Next, they validated this strategy by considering the battery and ultracapacitor in the loop, where a hardware-in-loop platform was established to execute the simulations [29]. To exploit the trained value function provided by RL methods, He et al leveraged an actor-critic approach to handle the continuous action space and used stochastic gradient descent to train the state-value function [30], [31].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

200

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E nergy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field.

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Power Management for a Plug-in Hybrid Electric Vehicle Based on Reinforcement Learning with Continuous State and Action Spaces

Cited by 48 publications

References 8 publications

Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning

Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

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