Model reference adaptive system speed estimator based on type-1 and type-2 fuzzy logic sensorless control of electrical vehicle with electrical differential

• Дата: 2023
• Автори: Khemis, A., Boutabba, T., Drid, S.
• Формат: Стаття
• Мова: English
• Опубліковано: National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” 2023
• Теми: електромобіль; асинхронні машини; еталонна адаптивна система; полеорієнтоване керування; електричний диференціал; регулятор з нечіткою логікою; electrical vehicle; induction machines; model reference adaptive system; field oriented control; electric differential; fuzzy logic controller
• Онлайн доступ: http://eie.khpi.edu.ua/article/view/279753
• Назва журналу: Electrical Engineering & Electromechanics

Introduction. In this paper, a new approach for estimating the speed of in-wheel electric vehicles with two independent rear drives is presented. Currently, the variable-speed induction motor replaces the DC motor drive in a wide range of applications, including electric vehicles where quick dynamic response is required. This is now possible as a result of significant improvements in the dynamic performance of electrical drives brought about by technological advancements and development in the fields of power commutation devices, digital signal processing, and, more recently, intelligent control systems. The system’s reliability and robustness are improved, and the cost, size, and upkeep requirements of the induction motor drive are reduced through control strategies without a speed sensor. Successful uses of the induction motor without a sensor have been made for medium- and high-speed operations. However, low speed instability and instability under various charge perturbation conditions continue to be serious issues in this field of study and have not yet been effectively resolved. Some application such as traction drives and cranes are required to maintain the desired level of torque down to low speed levels with uncertain load torque disturbance conditions. Speed and torque control is more important particularly in motor-in-wheel traction drive train configuration where vehicle wheel rim is directly connected to the motor shaft to control the speed and torque. Novelty of the proposed work is to improve the dynamic performance of conventional controller used of model reference adaptive system speed observer using both type-1 and type-2 fuzzy logic controllers. Purpose. In proposed scheme, the performance of the engine is being controlled, fuzzy logic controller is controlling the estimate rotor speed, and results are then compared using type-1 and type-2. Method. For a two-wheeled motorized electric vehicle, a high-performance sensorless wheel motor drive based on both type-2 and type-1 fuzzy logic controllers of the model reference adaptive control system is developed. Results. Proved that, using fuzzy logic type-2 controller the sensorless speed control of the electrical differential of electric vehicle EV observer, much better results are achieved. Practical value. The main possibility of realizing reliable and efficient electric propulsion systems based on intelligent observers (type-2 fuzzy logic) is demonstrated. The research methodology has been designed to facilitate the future experimental implementation on a digital signal processor.