ABSTRACT:
Hybrid electric vehicles (HEV) have become an increasing topic of research in recent years. Compared to traditional Internal Combustion Engine (ICE) driven automobiles, HEV’s have the potential to consume less fuel and pollute less. Results go as high as 50 % of the fuel consumption of a conventional vehicle of the same size.
A Hybrid Electric Vehicle (HEV) is an automobile in which the propulsion comprises both an Internal Combustion Engine (ICE) and an Electric Motor (EM). The most common type of HEV is the parallel type, in which both ICE and EM are directly connected to the wheels. The ICE is known to have good efficiency at certain operating curves (on a speed-torque diagram) and poor efficiency in the rest. During transients efficiency drops considerably and pollution increases.
When properly controlled, an electric motor can have far better efficiency both in transients and at different operating conditions. Therefore, in a parallel HEV the ICE is kept at steady state and the electric motor is responsible in supplying the difference in torque between the torque command and the torque supplied by the ICE. In a series HEV, the entire torque is produced by the electric motor while the ICE only drives a generator to charge the batteries and supply the EM. The induction motor is the electric propulsion solution of choice for most HEV, since it is relatively low cost, robust and virtually maintenance free.
In high performance applications, the induction motor is controlled through field orientation techniques. Since these techniques require the knowledge of the motor model parameters, a mismatch in parameters is prone to create control errors. It is therefore important to accurately model the induction motor. The induction motor parameters vary with the operating conditions, as is the case with all electric motors. The inductances tend to saturate at high flux levels and the resistances tend to increase as an effect of heating and skin effect. There are other effects that contribute to the parameter variation, which make the dependency between operating conditions and parameters even more complicated. Most of previous research in motor control uses a single set of parameters for all operating condition or uses on-line adaptive procedures for the estimation of only one parameter, namely the rotor resistance. The present research develops a methodology for parameter estimation that can be easily applied on site (the motor does not need to be tested separately); also, the parameters are mapped to the operating conditions. Furthermore, for parameters that vary as a function of unmeasurable quantities (for example, the rotor resistance varies as function of rotor temperature) or that can modify in time due to aging, an on-line parameter estimator is developed.
Field orientation techniques also require knowledge of the rotor speed. Since speed sensors decrease the reliability of a drive system (and increase its price), a common trend in motor control is to eliminate them and use a rotor speed observer to calculate the speed. However, all known speed estimators (open loop, MRAS, Kalman filter, Sliding mode etc) depend on the induction motor model. This work corrected this problem by developing a speed observer that has parameters adapting to operating conditions. All known speed estimation techniques behave poorly at low speed and loading levels. An intelligent controller is developed to correct speed estimation at low speed.
The objectives of this work has the following main components:
Modeling and parameter estimation as a function of operating conditions
Many methods for parameter estimation exist. Most off-site and offline methods require special testing procedures. Furthermore, most procedures test the motor with other inputs (pure sinusoidal, step etc) than the ones used in motor control (PWM), neglecting the effects of the later. On-site and off-line methods (self-commissioning) methods also exist, but they are usually limited to constant parameters or only allow for a simplified version of the variation with operating conditions. This work uses some of the existent estimation algorithms, but improves in the use of more realistic conditions (use the power converter to generate input signals) and relates the parameters of the motor to operating conditions.
Development of an on-line observer for rotor resistance and rotor time constant
For parameters that vary as a function of unmeasurable quantities (e.g. rotor temperature), mapping is hardly possible. An on-line estimation method is preferred. On-line estimation methods also exist, but most are limited to certain operating conditions or at steady-state conditions. A sliding mode flux observer is developed in this work. The observer simultaneously estimates rotor fluxes and rotor parameters (rotor resistance and time constant) continuously and at all operating conditions, both at steady state and in transient
Development and analysis of field oriented control algorithms
Field oriented control was implemented almost two decades ago and considerable work has been done in the area. However, a discrete time sliding mode controller for induction motor has not been attempted by any researcher so far. The only implementations that exist for sliding mode controllers are for continuous time sliding mode controllers.
A field-oriented control is implemented on DSP, using measurements of input voltage and current and speed. Three controllers are implemented and tested, both in simulation and in experiment: a classical PI control with decoupling, a continuous time sliding mode and a discrete time sliding mode controller. The variation of parameters with operating conditions is included in the algorithm. Critical analysis of the three controllers is performed.
Development and analysis of a sliding mode sensorless control algorithm
Many speed sensorless control algorithms exist in research. All of these algorithms have serious speed estimation problems at low speed and/or when the parameters of the motor vary considerably. This work focuses on sliding mode speed observer algorithms since they are more robust to uncertainties and have fast dynamic response. An adaptive sliding mode speed-flux 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.
Development and analysis of an intelligent sliding mode sensorless control algorithm
Speed estimation errors occur in all experimental setups and at any speed range. However, except for low speed, the effect of errors can be easily neglected since their impact on overall control is minimal. An attempt to compensate the observer errors by analytically computing them would be futile due to the uncertainties in the error sources. In this work an intelligent controller is developed; the controller adapts the sliding mode speed observer to improve speed estimation.
This technical report is organized as follows. The background and literature review for this work are summarized in Chapter 2. Chapter 3 presents the modeling, parameter estimation and mapping as a function of operating conditions of the induction motor used in this research. Chapter 4 presents the development of a sliding mode flux observer with online estimation of the rotor parameters. Chapter 5 contains the development and analysis of field oriented control algorithms. In Chapter 6 a sliding mode sensorless control algorithm is developed and analyzed. In Chapter 7, the sensorless algorithm developed in Chapter 6 is enhanced to correct speed estimation at low speed by using a fuzzy logic controller. Overall conclusions and future work are presented in Chapter 8.
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