Neural network applications in control of electric vehicle induction machine drives

In this paper two artificial neural networks (ANN) are used for speed sensorless control of an electric vehicle induction machine drive. The first neural network (ANN1) is trained to estimate speed and torque of the machine to use in the feedback loop of the control system. The second neural network (ANN2) is used for commanding optimum voltage and frequency that maximises the drive efficiency. The first neural network ANN1 has been trained with the data obtained using the machine model operating under different speed, load, temperature and flux density. To provide the required data to train and test the ANN2 a simulation program was written to calculate commanded voltage and frequency that would constrain the induction machine to operate with maximum efficiency at different load, speed, and temperature conditions. The controller is able to control the induction machine over a wide speed range from standstill to high speeds in the flux weakening region. The trained neural networks are employed for the control of a 7.5 kW induction machine set up. It has been found that the neural network control system could work reliably without using a mechanical speed sensor and is an appropriate technique for speed sensorless control of an induction machine to drive an electric vehicle (EV). The performance of this control system has been found to be as good as those controllers which use the induction machine model. The description of the control system and training procedure of the neural network are given in this paper. The test results obtained for a torque control scheme suitable for the control of an EV are also presented.