ELECTRIC VEHICLE SHIFT STRATEGY BASED ON MODEL PREDICTIVE CONTROL

Hang Qin; Hongwen He; Jiankun Peng; Mo Han; Haonan Li

doi:10.46855/energy-proceedings-2613

ELECTRIC VEHICLE SHIFT STRATEGY BASED ON MODEL PREDICTIVE CONTROL

Hang Qin, Hongwen He^*, Jiankun Peng, Mo Han, Haonan Li

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

In order to satisfy high torque output and high speed driving demand, electric vehicles need a gearbox to adjust the gear ratio. The shift schedule is popular in gear shift research. The most widely used schedule, the two-parameter shift schedule, ignores the influence of dynamic conditions, resulting in that it is hard to suit the road and it causes energy waste. In this paper, a strategy based on model predictive control is proposed. A Recurrent neural network is used to predict velocity sequences in the 5-second horizon. Dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of the MPC shift schedule. Simulation results show that this shift strategy can reduce the shift frequency while saving energy consumption.

Original language	English
Journal	Energy Proceedings
Volume	3
DOIs	https://doi.org/10.46855/energy-proceedings-2613
Publication status	Published - 2019
Event	11th International Conference on Applied Energy, ICAE 2019 - Västerås, Sweden Duration: 12 Aug 2019 → 15 Aug 2019

Keywords

model predict control
pure electrical vehicle
recurrent neural network
shift schedule

UN SDGs

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

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10.46855/energy-proceedings-2613

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@article{94062489bd864995928c60a02196770a,

title = "ELECTRIC VEHICLE SHIFT STRATEGY BASED ON MODEL PREDICTIVE CONTROL",

abstract = "In order to satisfy high torque output and high speed driving demand, electric vehicles need a gearbox to adjust the gear ratio. The shift schedule is popular in gear shift research. The most widely used schedule, the two-parameter shift schedule, ignores the influence of dynamic conditions, resulting in that it is hard to suit the road and it causes energy waste. In this paper, a strategy based on model predictive control is proposed. A Recurrent neural network is used to predict velocity sequences in the 5-second horizon. Dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of the MPC shift schedule. Simulation results show that this shift strategy can reduce the shift frequency while saving energy consumption.",

keywords = "model predict control, pure electrical vehicle, recurrent neural network, shift schedule",

author = "Hang Qin and Hongwen He and Jiankun Peng and Mo Han and Haonan Li",

note = "Publisher Copyright: {\textcopyright} 2019 ICAE.; 11th International Conference on Applied Energy, ICAE 2019 ; Conference date: 12-08-2019 Through 15-08-2019",

year = "2019",

doi = "10.46855/energy-proceedings-2613",

language = "English",

volume = "3",

journal = "Energy Proceedings",

issn = "2004-2965",

publisher = "Scanditale AB",

}

TY - JOUR

T1 - ELECTRIC VEHICLE SHIFT STRATEGY BASED ON MODEL PREDICTIVE CONTROL

AU - Qin, Hang

AU - He, Hongwen

AU - Peng, Jiankun

AU - Han, Mo

AU - Li, Haonan

PY - 2019

Y1 - 2019

N2 - In order to satisfy high torque output and high speed driving demand, electric vehicles need a gearbox to adjust the gear ratio. The shift schedule is popular in gear shift research. The most widely used schedule, the two-parameter shift schedule, ignores the influence of dynamic conditions, resulting in that it is hard to suit the road and it causes energy waste. In this paper, a strategy based on model predictive control is proposed. A Recurrent neural network is used to predict velocity sequences in the 5-second horizon. Dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of the MPC shift schedule. Simulation results show that this shift strategy can reduce the shift frequency while saving energy consumption.

AB - In order to satisfy high torque output and high speed driving demand, electric vehicles need a gearbox to adjust the gear ratio. The shift schedule is popular in gear shift research. The most widely used schedule, the two-parameter shift schedule, ignores the influence of dynamic conditions, resulting in that it is hard to suit the road and it causes energy waste. In this paper, a strategy based on model predictive control is proposed. A Recurrent neural network is used to predict velocity sequences in the 5-second horizon. Dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of the MPC shift schedule. Simulation results show that this shift strategy can reduce the shift frequency while saving energy consumption.

KW - model predict control

KW - pure electrical vehicle

KW - recurrent neural network

KW - shift schedule

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U2 - 10.46855/energy-proceedings-2613

DO - 10.46855/energy-proceedings-2613

M3 - Conference article

AN - SCOPUS:85202625398

SN - 2004-2965

VL - 3

JO - Energy Proceedings

JF - Energy Proceedings

T2 - 11th International Conference on Applied Energy, ICAE 2019

Y2 - 12 August 2019 through 15 August 2019

ER -

ELECTRIC VEHICLE SHIFT STRATEGY BASED ON MODEL PREDICTIVE CONTROL

Abstract

Keywords

UN SDGs

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