SoC Planner for Predictive Energy Management of Fuel Cell Vehicles

Qingyun Min; Xiaodong Wei; Chao Sun; Qiang Ren; Biao Liang; Bo Liu

doi:10.1109/VPPC53923.2021.9699249

SoC Planner for Predictive Energy Management of Fuel Cell Vehicles

Qingyun Min, Xiaodong Wei, Chao Sun, Qiang Ren, Biao Liang, Bo Liu

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Global state of charge (SoC) trajectory planning is of great significance for the fuel economy improvement of plug in fuel cell vehicles (PFCV), which is equipped with a relatively larger battery pack. In this paper, we provide a comprehensive analysis of three SoC planners, applied within a model predictive control (MPC) framework. The basic principles of rule-based, dynamic programing based and neural network based SoC planners are described systematically. Their planning accuracy and computation efficiency are compared. The generated reference SoC profile is used for global guidance of real-time MPC energy management. The fuel performance of guided MPC under different reference SoC is further analyzed. The simulation results demonstrate that the fuel economy of real-time algorithm can be significantly improved by introducing a reference SoC (3.31∼4.55%), while the impact of reference SoC precision is not obvious (0.25∼1.24%).

Original language	English
Title of host publication	2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665405287
DOIs	https://doi.org/10.1109/VPPC53923.2021.9699249
Publication status	Published - 2021
Event	18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - Virtual, Gijon, Spain Duration: 25 Oct 2021 → 28 Oct 2021

Publication series

Name	2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

Conference

Conference	18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021
Country/Territory	Spain
City	Virtual, Gijon
Period	25/10/21 → 28/10/21

Keywords

Energy management
Model predictive control
Plug-in fuel cell vehicle
SoC trajectory planner

Access to Document

10.1109/VPPC53923.2021.9699249

Cite this

Min, Q., Wei, X., Sun, C., Ren, Q., Liang, B., & Liu, B. (2021). SoC Planner for Predictive Energy Management of Fuel Cell Vehicles. In 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS (2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC53923.2021.9699249

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title = "SoC Planner for Predictive Energy Management of Fuel Cell Vehicles",

abstract = "Global state of charge (SoC) trajectory planning is of great significance for the fuel economy improvement of plug in fuel cell vehicles (PFCV), which is equipped with a relatively larger battery pack. In this paper, we provide a comprehensive analysis of three SoC planners, applied within a model predictive control (MPC) framework. The basic principles of rule-based, dynamic programing based and neural network based SoC planners are described systematically. Their planning accuracy and computation efficiency are compared. The generated reference SoC profile is used for global guidance of real-time MPC energy management. The fuel performance of guided MPC under different reference SoC is further analyzed. The simulation results demonstrate that the fuel economy of real-time algorithm can be significantly improved by introducing a reference SoC (3.31∼4.55%), while the impact of reference SoC precision is not obvious (0.25∼1.24%).",

keywords = "Energy management, Model predictive control, Plug-in fuel cell vehicle, SoC trajectory planner",

author = "Qingyun Min and Xiaodong Wei and Chao Sun and Qiang Ren and Biao Liang and Bo Liu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021 ; Conference date: 25-10-2021 Through 28-10-2021",

year = "2021",

doi = "10.1109/VPPC53923.2021.9699249",

language = "English",

series = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",

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

booktitle = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",

address = "United States",

}

Min, Q, Wei, X, Sun, C, Ren, Q, Liang, B & Liu, B 2021, SoC Planner for Predictive Energy Management of Fuel Cell Vehicles. in 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS. 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS, Institute of Electrical and Electronics Engineers Inc., 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021, Virtual, Gijon, Spain, 25/10/21. https://doi.org/10.1109/VPPC53923.2021.9699249

SoC Planner for Predictive Energy Management of Fuel Cell Vehicles. / Min, Qingyun; Wei, Xiaodong; Sun, Chao et al.
2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SoC Planner for Predictive Energy Management of Fuel Cell Vehicles

AU - Min, Qingyun

AU - Wei, Xiaodong

AU - Sun, Chao

AU - Ren, Qiang

AU - Liang, Biao

AU - Liu, Bo

PY - 2021

Y1 - 2021

N2 - Global state of charge (SoC) trajectory planning is of great significance for the fuel economy improvement of plug in fuel cell vehicles (PFCV), which is equipped with a relatively larger battery pack. In this paper, we provide a comprehensive analysis of three SoC planners, applied within a model predictive control (MPC) framework. The basic principles of rule-based, dynamic programing based and neural network based SoC planners are described systematically. Their planning accuracy and computation efficiency are compared. The generated reference SoC profile is used for global guidance of real-time MPC energy management. The fuel performance of guided MPC under different reference SoC is further analyzed. The simulation results demonstrate that the fuel economy of real-time algorithm can be significantly improved by introducing a reference SoC (3.31∼4.55%), while the impact of reference SoC precision is not obvious (0.25∼1.24%).

AB - Global state of charge (SoC) trajectory planning is of great significance for the fuel economy improvement of plug in fuel cell vehicles (PFCV), which is equipped with a relatively larger battery pack. In this paper, we provide a comprehensive analysis of three SoC planners, applied within a model predictive control (MPC) framework. The basic principles of rule-based, dynamic programing based and neural network based SoC planners are described systematically. Their planning accuracy and computation efficiency are compared. The generated reference SoC profile is used for global guidance of real-time MPC energy management. The fuel performance of guided MPC under different reference SoC is further analyzed. The simulation results demonstrate that the fuel economy of real-time algorithm can be significantly improved by introducing a reference SoC (3.31∼4.55%), while the impact of reference SoC precision is not obvious (0.25∼1.24%).

KW - Energy management

KW - Model predictive control

KW - Plug-in fuel cell vehicle

KW - SoC trajectory planner

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U2 - 10.1109/VPPC53923.2021.9699249

DO - 10.1109/VPPC53923.2021.9699249

M3 - Conference contribution

AN - SCOPUS:85126217912

T3 - 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

BT - 2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021

Y2 - 25 October 2021 through 28 October 2021

ER -

SoC Planner for Predictive Energy Management of Fuel Cell Vehicles

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