TY - GEN
T1 - Stochastic Dynamics Modelling of Hybrid Electrical Vehicle and Parameters Estimation
AU - Ma, Yue
AU - Liu, Jiaxin
AU - Hou, Xuzhao
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
UR - http://www.scopus.com/inward/record.url?scp=85117955002&partnerID=8YFLogxK
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 -