Тип публикации: статья из журнала
Год издания: 2020
Идентификатор DOI: 10.4108/EAI.13-7-2018.162804
Ключевые слова: adaptive control, embedded systems, energy optimization, method, model, nature-inspired computing techniques, neural network electric vehicle, neural observer, pi-regulator
Аннотация: 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. © 2020 Oleg V. Nepomnyashchiy et al, licensed to EAI.
Журнал: EAI Endorsed Transactions on Energy Web
Выпуск журнала: Vol. 7, Is. 28
Номера страниц: 3
ISSN журнала: 16155548
Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.