Induction motor control for hybrid electric vehicle applications

Hybrid Electric Vehicles (HEV) are becoming an increasingly popular alternative to conventional vehicles due to their potential for lower energy consumption and lower pollutant emissions. The power train of a HEV comprises both an Internal Combustion Engine (ICE) and an Electric Motor (EM). The ICE is kept at optimal steady state conditions while the electric motor is operated at various operating conditions and in transient. The motor of choice for the EM in a HEV is the induction motor, owing it to its robustness, its low maintenance and low price.; The overall goal of this research is the control of the induction motor for HEV applications; besides the implementation and evaluation of existent high performance control algorithms, enhancements and new solutions are proposed.; Accurate knowledge of the induction motor model and its parameters is critical for high performance algorithms. Since the operating conditions for a HEV vary considerably as a function of loading, driving cycle, ambient temperature etc, the induction motor parameters will vary considerably. An induction motor model with parameters that modify as a function of operating conditions is developed. The estimation uses transient data and constrained optimization algorithm. The parameters are mapped to the operating conditions. An online estimator for rotor parameters is also developed.; Based on the model, three field-oriented control strategies are implemented and compared: a classical PI, a continuous time sliding mode controller and a discrete time sliding mode controller.; Speed knowledge is critical in field-oriented algorithms. Speed sensors decrease the overall reliability of an IM drive; furthermore, it is desirable that an IM drive function even after a speed sensor failure (even at a lower performance). An adaptive sliding mode speed observer is developed. The observer adapts itself to the speed range and adapts its parameters as a function of operating conditions. Performance over the entire speed and loading range is analyzed. To compensate the speed estimation errors in the low speed range (common to all known speed sensorless algorithms) an intelligent (fuzzy logic) controller is designed.