MODEL PREDICTIVE CONTROL AND NEURAL-FUZZY  FOR ELECTRIC DRIVE SYSTEMS OF ELECTRIC VEHICLE

Trần Ngọc Sơn; Lại Khắc Lãi; Lê Thị Thu Hà

doi:10.34238/tnu-jst.8407

MODEL PREDICTIVE CONTROL AND NEURAL-FUZZY FOR ELECTRIC DRIVE SYSTEMS OF ELECTRIC VEHICLE

About this article

Received: 25/07/23 Revised: 03/10/23 Published: 03/10/23

Authors

1. Tran Ngoc Son , University of Economics – Technology for Industries
2. Lai Khac Lai, TNU – University of Technology
3. Le Thi Thu Ha, TNU – University of Technology

Abstract

Electric driver systems for electric vehicles provide the traction needed to move the vehicle at the driver's command. They have the following characteristics: Must have a wide speed control range; high torque when starting and climbing; convenient control; stable working in all environmental conditions; have the ability to regenerate energy when braking and when going downhill. This paper proposes the application of an adaptive fuzzy neural system and model-based predictive control for electric vehicle transmission using 3-phase asynchronous motors. Model predictive control is used for torque control loops and adaptive fuzzy neural control is used for speed loops. The results are checked through simulation on Matlab - Simulink software when the vehicle is working in the conditions of unchanged speed and torque and when the vehicle is working in the conditions of changed speed and torque showing that the system is suitable for the vehicle's operating conditions.

Keywords

Batter Electric Vehicle; Model Predictive Control; Adaptive Neuro-Fuzzy; Electric Vehicle; Asynchronous Motor

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DOI: https://doi.org/10.34238/tnu-jst.8407

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