Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle

Jiangtao Gai; Yue Ma; Xuzhao Hou; Gen Zeng; Shumin Ruan

doi:10.1007/978-981-19-6226-4_57

Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle

Jiangtao Gai, Yue Ma, Xuzhao Hou^*, Gen Zeng, Shumin Ruan

^*Corresponding author for this work

School of Mechanical Engineering

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

Abstract

Electric tracked vehicles have better maneuverability than the tracked vehicles driven by conventional powertrain. Tracked vehicles may slide laterally when turning at high speed and with large curvature. This study develops sideslip angle estimators and predictors with potential for online application based on long-short-term memory (LSTM) neural network. The estimator and the predictors with 0.1 s and 0.2 s prediction horizons can achieve good accuracy. Other predictors with longer prediction horizons have significant errors, and the root mean square and mean errors increase almost linearly with the prediction horizon. The estimated and predicted sideslip angle can be used in vehicle lateral stability control.

Original language	English
Title of host publication	Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II
Editors	Yingmin Jia, Weicun Zhang, Yongling Fu, Shoujun Zhao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	576-583
Number of pages	8
ISBN (Print)	9789811962257
DOIs	https://doi.org/10.1007/978-981-19-6226-4_57
Publication status	Published - 2022
Event	18th Chinese Intelligent Systems Conference, CISC 2022 - Beijing, China Duration: 15 Oct 2022 → 16 Oct 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	951 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	18th Chinese Intelligent Systems Conference, CISC 2022
Country/Territory	China
City	Beijing
Period	15/10/22 → 16/10/22

Keywords

Estimator
LSTM
Sideslip angle
Tracked vehicle

Access to Document

10.1007/978-981-19-6226-4_57

Cite this

Gai, J., Ma, Y., Hou, X., Zeng, G., & Ruan, S. (2022). Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle. In Y. Jia, W. Zhang, Y. Fu, & S. Zhao (Eds.), Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II (pp. 576-583). (Lecture Notes in Electrical Engineering; Vol. 951 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-6226-4_57

Gai, Jiangtao ; Ma, Yue ; Hou, Xuzhao et al. / Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle. Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II. editor / Yingmin Jia ; Weicun Zhang ; Yongling Fu ; Shoujun Zhao. Springer Science and Business Media Deutschland GmbH, 2022. pp. 576-583 (Lecture Notes in Electrical Engineering).

@inproceedings{6300f09316194cfb9c53879b06cea1b3,

title = "Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle",

abstract = "Electric tracked vehicles have better maneuverability than the tracked vehicles driven by conventional powertrain. Tracked vehicles may slide laterally when turning at high speed and with large curvature. This study develops sideslip angle estimators and predictors with potential for online application based on long-short-term memory (LSTM) neural network. The estimator and the predictors with 0.1 s and 0.2 s prediction horizons can achieve good accuracy. Other predictors with longer prediction horizons have significant errors, and the root mean square and mean errors increase almost linearly with the prediction horizon. The estimated and predicted sideslip angle can be used in vehicle lateral stability control.",

keywords = "Estimator, LSTM, Sideslip angle, Tracked vehicle",

author = "Jiangtao Gai and Yue Ma and Xuzhao Hou and Gen Zeng and Shumin Ruan",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 18th Chinese Intelligent Systems Conference, CISC 2022 ; Conference date: 15-10-2022 Through 16-10-2022",

year = "2022",

doi = "10.1007/978-981-19-6226-4_57",

language = "English",

isbn = "9789811962257",

series = "Lecture Notes in Electrical Engineering",

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

pages = "576--583",

editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Shoujun Zhao",

booktitle = "Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II",

address = "Germany",

}

Gai, J, Ma, Y, Hou, X, Zeng, G & Ruan, S 2022, Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle. in Y Jia, W Zhang, Y Fu & S Zhao (eds), Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II. Lecture Notes in Electrical Engineering, vol. 951 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 576-583, 18th Chinese Intelligent Systems Conference, CISC 2022, Beijing, China, 15/10/22. https://doi.org/10.1007/978-981-19-6226-4_57

Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle. / Gai, Jiangtao; Ma, Yue; Hou, Xuzhao et al.
Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II. ed. / Yingmin Jia; Weicun Zhang; Yongling Fu; Shoujun Zhao. Springer Science and Business Media Deutschland GmbH, 2022. p. 576-583 (Lecture Notes in Electrical Engineering; Vol. 951 LNEE).

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

TY - GEN

T1 - Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle

AU - Gai, Jiangtao

AU - Ma, Yue

AU - Hou, Xuzhao

AU - Zeng, Gen

AU - Ruan, Shumin

PY - 2022

Y1 - 2022

N2 - Electric tracked vehicles have better maneuverability than the tracked vehicles driven by conventional powertrain. Tracked vehicles may slide laterally when turning at high speed and with large curvature. This study develops sideslip angle estimators and predictors with potential for online application based on long-short-term memory (LSTM) neural network. The estimator and the predictors with 0.1 s and 0.2 s prediction horizons can achieve good accuracy. Other predictors with longer prediction horizons have significant errors, and the root mean square and mean errors increase almost linearly with the prediction horizon. The estimated and predicted sideslip angle can be used in vehicle lateral stability control.

AB - Electric tracked vehicles have better maneuverability than the tracked vehicles driven by conventional powertrain. Tracked vehicles may slide laterally when turning at high speed and with large curvature. This study develops sideslip angle estimators and predictors with potential for online application based on long-short-term memory (LSTM) neural network. The estimator and the predictors with 0.1 s and 0.2 s prediction horizons can achieve good accuracy. Other predictors with longer prediction horizons have significant errors, and the root mean square and mean errors increase almost linearly with the prediction horizon. The estimated and predicted sideslip angle can be used in vehicle lateral stability control.

KW - Estimator

KW - LSTM

KW - Sideslip angle

KW - Tracked vehicle

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U2 - 10.1007/978-981-19-6226-4_57

DO - 10.1007/978-981-19-6226-4_57

M3 - Conference contribution

AN - SCOPUS:85140441103

SN - 9789811962257

T3 - Lecture Notes in Electrical Engineering

SP - 576

EP - 583

BT - Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II

A2 - Jia, Yingmin

A2 - Zhang, Weicun

A2 - Fu, Yongling

A2 - Zhao, Shoujun

PB - Springer Science and Business Media Deutschland GmbH

T2 - 18th Chinese Intelligent Systems Conference, CISC 2022

Y2 - 15 October 2022 through 16 October 2022

ER -

Gai J, Ma Y, Hou X, Zeng G, Ruan S. Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle. In Jia Y, Zhang W, Fu Y, Zhao S, editors, Proceedings of 2022 Chinese Intelligent Systems Conference - Volume II. Springer Science and Business Media Deutschland GmbH. 2022. p. 576-583. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-19-6226-4_57

Research on Sideslip Angle Estimation and Prediction for Electric Tracked Vehicle

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