Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain

Jianwei Li; Hanxiao Wang; Hongwen He; Zhongbao Wei; Qingqing Yang; Petar Igic

doi:10.1109/TTE.2021.3074792

Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain

Jianwei Li
, Hanxiao Wang
, Hongwen He
, Zhongbao Wei
, Qingqing Yang
, Petar Igic

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

103 Citations (Scopus)

513 Downloads (Pure)

Abstract

This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multiobjectives that minimize hydrogen consumption and suppress system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0%, compared with the conventional FC configuration.

Original language	English
Pages (from-to)	36-47
Number of pages	12
Journal	IEEE Transactions on Transportation Electrification
Volume	8
Issue number	1
Early online date	21 Apr 2021
DOIs	https://doi.org/10.1109/TTE.2021.3074792
Publication status	Published - Mar 2022

Bibliographical note

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

Batteries
deep reinforcement learning
Degradation
fuel cell hybrid electric vehicle
Fuel cells
Heuristic algorithms
hybrid energy storage system
Load modeling
power management
Power system management
sizing study
Vehicle dynamics
Deep reinforcement learning (DRL)
hybrid energy storage system (HESS)
fuel cell hybrid electric vehicle (FC-HEV)

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Transportation
Automotive Engineering

Access to Document

10.1109/TTE.2021.3074792

Li2021AAMAccepted author manuscript, 3.15 MB

Cite this

@article{eca99aadd6fa4dc89b6fd2635f41148c,

title = "Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain",

abstract = "This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multiobjectives that minimize hydrogen consumption and suppress system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0\%, compared with the conventional FC configuration.",

keywords = "Batteries, deep reinforcement learning, Degradation, fuel cell hybrid electric vehicle, Fuel cells, Heuristic algorithms, hybrid energy storage system, Load modeling, power management, Power system management, sizing study, Vehicle dynamics, Deep reinforcement learning (DRL), hybrid energy storage system (HESS), fuel cell hybrid electric vehicle (FC-HEV)",

author = "Jianwei Li and Hanxiao Wang and Hongwen He and Zhongbao Wei and Qingqing Yang and Petar Igic",

note = "{\textcopyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright {\textcopyright} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author{\textquoteright}s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.",

year = "2022",

month = mar,

doi = "10.1109/TTE.2021.3074792",

language = "English",

volume = "8",

pages = "36--47",

journal = "IEEE Transactions on Transportation Electrification",

issn = "2332-7782",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "1",

}

TY - JOUR

T1 - Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain

AU - Li, Jianwei

AU - Wang, Hanxiao

AU - He, Hongwen

AU - Wei, Zhongbao

AU - Yang, Qingqing

AU - Igic, Petar

N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

PY - 2022/3

Y1 - 2022/3

N2 - This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multiobjectives that minimize hydrogen consumption and suppress system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0%, compared with the conventional FC configuration.

AB - This article proposes a synergistic approach that traverses the battery optimal size simultaneously against the optimal power management based on deep reinforcement learning (DRL). A fuel cell hybrid electric vehicle (FC-HEV) with the FC/battery hybrid powertrain is used as the study case. The battery plays a key role in current transportation electrification, and the optimal sizing of the battery is critical for both system technical performances and economical revenues, especially in the hybrid design. The optimal battery design should coordinate the static sizing study against the dynamic power distribution for a given system, but few works provided the synergistic consideration of the two parts. In this study, the interaction happens in each sizing point with the optimal power sharing between the battery and the FC, aiming at minimizing the summation of hydrogen consumption, FC degradation, and battery degradation. Under the proposed framework, the power management is developed with deep Q network (DQN) algorithm, considering multiobjectives that minimize hydrogen consumption and suppress system degradation. In the case study, optimal sizing parameters with lowest cost are determined. Leveraged by the optimal size, the hybrid system economy with synergistic approach is improved by 16.0%, compared with the conventional FC configuration.

KW - Batteries

KW - deep reinforcement learning

KW - Degradation

KW - fuel cell hybrid electric vehicle

KW - Fuel cells

KW - Heuristic algorithms

KW - hybrid energy storage system

KW - Load modeling

KW - power management

KW - Power system management

KW - sizing study

KW - Vehicle dynamics

KW - Deep reinforcement learning (DRL)

KW - hybrid energy storage system (HESS)

KW - fuel cell hybrid electric vehicle (FC-HEV)

UR - https://www.scopus.com/pages/publications/85104617357

U2 - 10.1109/TTE.2021.3074792

DO - 10.1109/TTE.2021.3074792

M3 - Article

AN - SCOPUS:85104617357

SN - 2332-7782

VL - 8

SP - 36

EP - 47

JO - IEEE Transactions on Transportation Electrification

JF - IEEE Transactions on Transportation Electrification

IS - 1

ER -

Battery Optimal Sizing under a Synergistic Framework with DQN Based Power Managements for the Fuel Cell Hybrid Powertrain

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this