This study presents an updated review of the application of electric tractors. A customised drive system for the conception of a novel low‐power electric tractor suitable for family farms is also introduced and discussed. The introduced system comprises several aspects regarding energy generation, transmission, conversion, storage, utilisation, conservation, and management, as well as sustainability issues. A 9 kW prototype composed of two three‐phase induction motors, two independent inverters, and a lead–acid battery bank is presented. Flexible and safe operation is ensured by using an electronic control unit specifically designed for this project, as a dedicated control algorithm is also developed to provide greater versatility under common rural activities. Also, a supervisory system is proposed for data storage and performance analysis. To verify the proper performance of the electric tractor, the methodology used for conducting drawbar tests has been based on document CODE 2 by Organisation for Economic Co‐operation and Development (OECD). Experimental results are presented and discussed; thus, demonstrating that the proposed electric tractor is technically feasible in terms of performance when compared with a similar internal combustion engine one.

A Revista Ciência Agronômica é voltada para estudos relacionados com as Ciências Rurais e Recursos Naturais

The model predictive control increased its prominence in the field of induction machine drives. However, the performance of this strategy depends on the accuracy of the machine model parameters. In order to overcome this deficiency, this paper proposes a robust model predictive current control employing indirect field-oriented control in stationary reference frame using the stator current and the rotor flux vectors as state and stator voltage vector as the input. The control algorithm combines the classical model predictive control with the deadbeat approach in order to calculate the applied stator voltage vector in two components: one element considers the stator current reference, and another employs the disturbances caused due to the parameter errors, which allows to compensate the parameter mismatches in the plant model. The minimized cost function employs the predicted stator voltage vector to select the voltage vector to be applied to the stator terminals of the motor. The control method performance was verified using an experimental test bench analyzing the system steady-state and dynamic actions. In this way, the results corroborate the proposed controller robustness against parametric variations.