Induction motors are more attractive to car manufacturers because they are more robust and more cost effective to maintain in comparison with other types of electric machines. The evolution of their control makes them more efficient and less expensive. However, a new control technique known as sensorless control is being used to simplify the implementation of electric machines in electric vehicles. This technique involves replacing the flux and speed sensors with an observer. The estimation of these elements is based on the measurement of currents and voltages. The main purpose of the present study is to design a novel robust structure of the sensorless vector control for an urban electric vehicle. The proposed structure aims to improve the accuracy of dynamics at low speeds, eliminate sensitivity to the machine's parameters, and maintain the stability of the system even if the variation reaches high values. The speed estimation is ensured by an enhanced PI adaptation mechanism based on the full order Luenberger observer. The proof of this stability is based on the Lyapunov theorem. Moreover, a GA-based adaptive control is used for self-tuning of the stator resistance. By combining these techniques, we can enhance the efficiency and stability of the whole system.
Electric vehicle, induction machine, sensorless control, vector control, genetic algorithm, Lyapunov theorem, Luenberger observer
BOULMANE, ASMA; ZIDANI, YOUSSEF; BELKHAYAT, DRISS; and BOUCHOUIRBAT, MAROUANE
"A GA-based adaptive mechanism for sensorless vector control of induction motor drives for urban electric vehicles,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 28:
3, Article 36.
Available at: https://journals.tubitak.gov.tr/elektrik/vol28/iss3/36