Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines

Kaibo Li; Shumei Cui; Shouliang Han; Zhenyuan Zhang

doi:10.1109/ITEC-AP.2017.8080838

Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines

Kaibo Li
, Shumei Cui^*
, Shouliang Han
, Zhenyuan Zhang

^*Corresponding author for this work

School of Electrical Engineering and Automation

Harbin Institute of Technology

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

Abstract

Modular Cascade Machines (MCM) is a system where integrating several modular designed machines to replace the conventional single machine, and gradually attracted worldwide attentions in recent years. It may have perspectives in the applications of Electrical Vehicles (EVs). This paper developed a possible MCM torque distribution strategy, by the means of Adaptive Network based Fuzzy Interface System (ANFIS). A description tool, Energetic Macroscopic Representation (EMR) is then adopted for the description of system modelling, as well as system control. An optimal efficiency torque distribution table is developed as an expert library to train and test the ANFIS network. The simulation proves that ANFIS-based strategy can realize MCM high efficiency operation. Meanwhile, machines working conditions are improved comparing to the optimal efficiency strategy.

Original language	English
Title of host publication	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538628942
DOIs	https://doi.org/10.1109/ITEC-AP.2017.8080838
State	Published - 23 Oct 2017
Event	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 - Harbin, China Duration: 7 Aug 2017 → 10 Aug 2017

Publication series

Name	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

Conference

Conference	2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017
Country/Territory	China
City	Harbin
Period	7/08/17 → 10/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Adaptive Network based Fuzzy Interface System
Electric drive
Electric traction
Electrical Vehicles
Energetic Macroscopic Representation
Energy Management Strategy
Modular Cascade Machines

Access to Document

10.1109/ITEC-AP.2017.8080838

Cite this

Li, K., Cui, S., Han, S., & Zhang, Z. (2017). Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines. In 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 Article 8080838 (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITEC-AP.2017.8080838

Li, Kaibo ; Cui, Shumei ; Han, Shouliang et al. / Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines. 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017).

@inproceedings{65244c9fa401438d87bdbff0ce71fc09,

title = "Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines",

abstract = "Modular Cascade Machines (MCM) is a system where integrating several modular designed machines to replace the conventional single machine, and gradually attracted worldwide attentions in recent years. It may have perspectives in the applications of Electrical Vehicles (EVs). This paper developed a possible MCM torque distribution strategy, by the means of Adaptive Network based Fuzzy Interface System (ANFIS). A description tool, Energetic Macroscopic Representation (EMR) is then adopted for the description of system modelling, as well as system control. An optimal efficiency torque distribution table is developed as an expert library to train and test the ANFIS network. The simulation proves that ANFIS-based strategy can realize MCM high efficiency operation. Meanwhile, machines working conditions are improved comparing to the optimal efficiency strategy.",

keywords = "Adaptive Network based Fuzzy Interface System, Electric drive, Electric traction, Electrical Vehicles, Energetic Macroscopic Representation, Energy Management Strategy, Modular Cascade Machines",

author = "Kaibo Li and Shumei Cui and Shouliang Han and Zhenyuan Zhang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017 ; Conference date: 07-08-2017 Through 10-08-2017",

year = "2017",

month = oct,

day = "23",

doi = "10.1109/ITEC-AP.2017.8080838",

language = "英语",

series = "2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017",

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

booktitle = "2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017",

address = "美国",

}

Li, K, Cui, S, Han, S & Zhang, Z 2017, Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines. in 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017., 8080838, 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017, Harbin, China, 7/08/17. https://doi.org/10.1109/ITEC-AP.2017.8080838

Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines. / Li, Kaibo; Cui, Shumei; Han, Shouliang et al.
2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8080838 (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017).

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

TY - GEN

T1 - Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines

AU - Li, Kaibo

AU - Cui, Shumei

AU - Han, Shouliang

AU - Zhang, Zhenyuan

PY - 2017/10/23

Y1 - 2017/10/23

N2 - Modular Cascade Machines (MCM) is a system where integrating several modular designed machines to replace the conventional single machine, and gradually attracted worldwide attentions in recent years. It may have perspectives in the applications of Electrical Vehicles (EVs). This paper developed a possible MCM torque distribution strategy, by the means of Adaptive Network based Fuzzy Interface System (ANFIS). A description tool, Energetic Macroscopic Representation (EMR) is then adopted for the description of system modelling, as well as system control. An optimal efficiency torque distribution table is developed as an expert library to train and test the ANFIS network. The simulation proves that ANFIS-based strategy can realize MCM high efficiency operation. Meanwhile, machines working conditions are improved comparing to the optimal efficiency strategy.

AB - Modular Cascade Machines (MCM) is a system where integrating several modular designed machines to replace the conventional single machine, and gradually attracted worldwide attentions in recent years. It may have perspectives in the applications of Electrical Vehicles (EVs). This paper developed a possible MCM torque distribution strategy, by the means of Adaptive Network based Fuzzy Interface System (ANFIS). A description tool, Energetic Macroscopic Representation (EMR) is then adopted for the description of system modelling, as well as system control. An optimal efficiency torque distribution table is developed as an expert library to train and test the ANFIS network. The simulation proves that ANFIS-based strategy can realize MCM high efficiency operation. Meanwhile, machines working conditions are improved comparing to the optimal efficiency strategy.

KW - Adaptive Network based Fuzzy Interface System

KW - Electric drive

KW - Electric traction

KW - Electrical Vehicles

KW - Energetic Macroscopic Representation

KW - Energy Management Strategy

KW - Modular Cascade Machines

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U2 - 10.1109/ITEC-AP.2017.8080838

DO - 10.1109/ITEC-AP.2017.8080838

M3 - 会议稿件

AN - SCOPUS:85040128322

T3 - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

BT - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017

Y2 - 7 August 2017 through 10 August 2017

ER -

Li K, Cui S, Han S, Zhang Z. Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines. In 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8080838. (2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2017). doi: 10.1109/ITEC-AP.2017.8080838

Torque distribution strategy based on adaptive network based fuzzy interface system for an electrical vehicle using modular cascade machines

Abstract

Publication series

Conference

UN SDGs

Keywords

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