Research Article
A neural regulator for efficient control of electric vehicle motors
@ARTICLE{10.4108/eai.13-7-2018.162804, author={O. Nepomnyashchiy and F. Kazakov and D. Ostroverkhov and A. Tarasov and N. Sirotinina}, title={A neural regulator for efficient control of electric vehicle motors}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={28}, publisher={EAI}, journal_a={EW}, year={2020}, month={1}, keywords={Energy optimization, nature-inspired computing techniques, neural network electric vehicle, PI-regulator, neural observer, embedded systems, adaptive control, method, model}, doi={10.4108/eai.13-7-2018.162804} }- O. Nepomnyashchiy
F. Kazakov
D. Ostroverkhov
A. Tarasov
N. Sirotinina
Year: 2020
A neural regulator for efficient control of electric vehicle motors
EW
EAI
DOI: 10.4108/eai.13-7-2018.162804
Abstract
INTRODUCTION: A number of promising designs of electric vehicles use separate wheeled motors. In this case, an important task of designing a power supply system is to provide effective control of electric motors and battery charge / discharge modes.
OBJECTIVES: The paper considers the problem of determining optimal coefficients of the electric motor proportionalintegral (PI) controller and their influence on the power distribution in the electric vehicle on-board power supply system.
METHODS: It is proposed to implement separate adaptive control of electric motors, taking into account conditions of operating, road surface, and other factors. There are introduced two options for the motor controller implementation: an adaptive PI-controller and an intelligent PI-controller with an adaptive observer based on a neural network.
RESULTS: The simulation results show that the adaptive PI-controller provides a reduction in the transient duration, but insufficient energy efficiency. Intelligent PI controller on the base of neuroregulator provides 2 times reduction of transition time, reduction of energy losses and engine overshoot.
CONCLUSION: The use of the neuroregulator makes it possible to automatically select and adjust PI controller coefficients. In addition, the proposed control method reduces inrush currents and torque spikes, that prolongs the service life of mechanical components. During motor operation, the neural network can continue learning and adjusting PIcontroller coefficients to changes in operating conditions (for example, seasonal) and motor parameters. Assumed outcomes of this solution will be improving electric vehicle characteristics, increasing mileage and battery life time, and prospective transition to an electronic differential.
Copyright © 2020 Oleg V. Nepomnyashchiy et al, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
