Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation

Yue Ma; Jiaxin Liu; Xuzhao Hou

doi:10.1007/978-981-16-6320-8_37

Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation

Yue Ma^*, Jiaxin Liu, Xuzhao Hou

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

Abstract

Under complex driving conditions, there are higher requirements for the performance of the electromechanical transmission system to satisfy the vehicle’s random power consumption. This paper proposes a new method for modeling the stochastic road load of a series hybrid electric tracked vehicle using Ito stochastic differential equation. Firstly, the random road resistance data is obtained through dynamic simulation and calculation. Secondly, through the statistical analysis of the historical data, the appropriate stochastic equation model is selected and its parameters are estimated. Finally, the simulation results of the stochastic model are compared with the initial model. The MATLAB simulation results show that the bus voltage error of the stochastic model is between 0.97% and 1.04%, which verifies the effectiveness and accuracy of the model.

Original language	English
Title of host publication	Proceedings of 2021 Chinese Intelligent Systems Conference
Editors	Yingmin Jia, Weicun Zhang, Yongling Fu, Zhiyuan Yu, Song Zheng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	349-361
Number of pages	13
ISBN (Print)	9789811663192
DOIs	https://doi.org/10.1007/978-981-16-6320-8_37
Publication status	Published - 2022
Event	17th Chinese Intelligent Systems Conference, CISC 2021 - Fuzhou, China Duration: 16 Oct 2021 → 17 Oct 2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	805 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	17th Chinese Intelligent Systems Conference, CISC 2021
Country/Territory	China
City	Fuzhou
Period	16/10/21 → 17/10/21

Keywords

Hybrid electric vehicle
Ito differential equation
Stochastic dynamics modelling

Access to Document

10.1007/978-981-16-6320-8_37

Cite this

Ma, Y., Liu, J., & Hou, X. (2022). Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation. In Y. Jia, W. Zhang, Y. Fu, Z. Yu, & S. Zheng (Eds.), Proceedings of 2021 Chinese Intelligent Systems Conference (pp. 349-361). (Lecture Notes in Electrical Engineering; Vol. 805 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-6320-8_37

Ma, Yue ; Liu, Jiaxin ; Hou, Xuzhao. / Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation. Proceedings of 2021 Chinese Intelligent Systems Conference. editor / Yingmin Jia ; Weicun Zhang ; Yongling Fu ; Zhiyuan Yu ; Song Zheng. Springer Science and Business Media Deutschland GmbH, 2022. pp. 349-361 (Lecture Notes in Electrical Engineering).

@inproceedings{965c5c0ad01f46249d45492d241b311e,

title = "Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation",

abstract = "Under complex driving conditions, there are higher requirements for the performance of the electromechanical transmission system to satisfy the vehicle{\textquoteright}s random power consumption. This paper proposes a new method for modeling the stochastic road load of a series hybrid electric tracked vehicle using Ito stochastic differential equation. Firstly, the random road resistance data is obtained through dynamic simulation and calculation. Secondly, through the statistical analysis of the historical data, the appropriate stochastic equation model is selected and its parameters are estimated. Finally, the simulation results of the stochastic model are compared with the initial model. The MATLAB simulation results show that the bus voltage error of the stochastic model is between 0.97% and 1.04%, which verifies the effectiveness and accuracy of the model.",

keywords = "Hybrid electric vehicle, Ito differential equation, Stochastic dynamics modelling",

author = "Yue Ma and Jiaxin Liu and Xuzhao Hou",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",

year = "2022",

doi = "10.1007/978-981-16-6320-8_37",

language = "English",

isbn = "9789811663192",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "349--361",

editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",

booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference",

address = "Germany",

}

Ma, Y, Liu, J & Hou, X 2022, Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation. in Y Jia, W Zhang, Y Fu, Z Yu & S Zheng (eds), Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol. 805 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 349-361, 17th Chinese Intelligent Systems Conference, CISC 2021, Fuzhou, China, 16/10/21. https://doi.org/10.1007/978-981-16-6320-8_37

Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation. / Ma, Yue; Liu, Jiaxin; Hou, Xuzhao.
Proceedings of 2021 Chinese Intelligent Systems Conference. ed. / Yingmin Jia; Weicun Zhang; Yongling Fu; Zhiyuan Yu; Song Zheng. Springer Science and Business Media Deutschland GmbH, 2022. p. 349-361 (Lecture Notes in Electrical Engineering; Vol. 805 LNEE).

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

TY - GEN

T1 - Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation

AU - Ma, Yue

AU - Liu, Jiaxin

AU - Hou, Xuzhao

PY - 2022

Y1 - 2022

N2 - Under complex driving conditions, there are higher requirements for the performance of the electromechanical transmission system to satisfy the vehicle’s random power consumption. This paper proposes a new method for modeling the stochastic road load of a series hybrid electric tracked vehicle using Ito stochastic differential equation. Firstly, the random road resistance data is obtained through dynamic simulation and calculation. Secondly, through the statistical analysis of the historical data, the appropriate stochastic equation model is selected and its parameters are estimated. Finally, the simulation results of the stochastic model are compared with the initial model. The MATLAB simulation results show that the bus voltage error of the stochastic model is between 0.97% and 1.04%, which verifies the effectiveness and accuracy of the model.

AB - Under complex driving conditions, there are higher requirements for the performance of the electromechanical transmission system to satisfy the vehicle’s random power consumption. This paper proposes a new method for modeling the stochastic road load of a series hybrid electric tracked vehicle using Ito stochastic differential equation. Firstly, the random road resistance data is obtained through dynamic simulation and calculation. Secondly, through the statistical analysis of the historical data, the appropriate stochastic equation model is selected and its parameters are estimated. Finally, the simulation results of the stochastic model are compared with the initial model. The MATLAB simulation results show that the bus voltage error of the stochastic model is between 0.97% and 1.04%, which verifies the effectiveness and accuracy of the model.

KW - Hybrid electric vehicle

KW - Ito differential equation

KW - Stochastic dynamics modelling

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U2 - 10.1007/978-981-16-6320-8_37

DO - 10.1007/978-981-16-6320-8_37

M3 - Conference contribution

AN - SCOPUS:85117955002

SN - 9789811663192

T3 - Lecture Notes in Electrical Engineering

SP - 349

EP - 361

BT - Proceedings of 2021 Chinese Intelligent Systems Conference

A2 - Jia, Yingmin

A2 - Zhang, Weicun

A2 - Fu, Yongling

A2 - Yu, Zhiyuan

A2 - Zheng, Song

PB - Springer Science and Business Media Deutschland GmbH

T2 - 17th Chinese Intelligent Systems Conference, CISC 2021

Y2 - 16 October 2021 through 17 October 2021

ER -

Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation

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