In the field of sensorless control of permanent magnet synchronous motors (PMSMs), different techniques based on machine anisotropies have been studied and implemented successfully. Nevertheless, most proposed approaches extract the rotor position information from the measured machine currents, that, when applied to low-power machines, might require high-bandwidth current sensors. An interesting alternative is given by sensorless techniques that exploit the star-point voltage of PMSMs, such as the direct flux control technique. This work aims at analyzing the conditions of applicability of such technique by considering a more thorough description of the machine inductance matrix. After a comprehensive mathematical description of the technique and characterization of the machine anisotropy information that is extracted from the star-point voltage, simulation as well as experimental results conducted on a test machine are presented and discussed in order to validate the proposed theory.

In the field of sensorless drive of synchronous machines (SMs), many techniques have been proposed that can be applied successfully in most applications. Nevertheless, these techniques rely on the measurement of the phase currents to extract the rotor position information. In the particular case of low-power machines, the application of such techniques is challenging due to the limited bandwidth of the available current sensors. An alternative is offered by those techniques that exploit the star-point voltage rather than phase currents. This work aims at providing a model of the dynamic behavior of the star-point voltage and presenting a technique for extracting the rotor electrical position needed for sensorless operation of SMs. Two different circuitries for measuring the star-point voltage are also presented and then compared. The presented mathematical analysis and the measurement methods are validated both numerically and experimentally on a test machine.

The need for reducing the cost of and space in Electrically Assisted Bicycles (EABs) has led the research to the development of solutions able to sense the applied pedalling torque and to provide a suitable electrical assistance avoiding the installation of torque sensors. Among these approaches, this paper proposes a novel method for the estimation of the pedalling torque starting from an estimation of the motor load torque given by a Load Torque Observer (LTO) and evaluating the environmental disturbances that act on the vehicle longitudinal dynamics. Moreover, this work shows the robustness of this approach to rotor position estimation errors introduced when sensorless techniques are used to control the motor. Therefore, this method allows removing also position sensors leading to an additional cost and space reduction. After a mathematical description of the vehicle longitudinal dynamics, this work proposes a state observer capable of estimating the applied pedalling torque. The theory is validated by means of experimental results performed on a bicycle under different conditions and exploiting the Direct Flux Control (DFC) sensorless technique to obtain the rotor position information. Afterwards, the identification of the system parameters together with the tuning of the control system and of the LTO required for the validation of the proposed theory are thoroughly described. Finally, the capabilities of the state observer of estimating an applied pedalling torque and of recognizing the application of external disturbance torques to the motor is verified.

This study proposes an innovative unknown input observation approach based on Kalman filtering to estimate the cycling torque and provide assistance in electrically powered bicycles. Specifically, a constant and a sinusoidal pedaling torque model are compared, underlining the need for an enhanced mathematical description to improve system performance. Using a nonlinear model of the bicycle longitudinal dynamics, the cycling torque is reconstructed with an Extended Kalman Filter. Also, an online low-computational effort road slope estimation method based on Kalman filtering, that accounts for cornering effect errors, is proposed. The considered approaches, that utilize wheel speed, inertial, and motor current measurements, are tested in an outdoor setting with variable slopes and curves. Differently from the current state-of-the-art, the estimation performances are not only expressed in terms of pedaling torque estimation error minimization. This work presents a novel pedaling power and delivered energy analysis to evaluate the provided electrical assistance and the consequent pedaling effort decrease. The experimental results show that a cycling endeavor reduction, similar to what can be achieved when electrical assistance is provided employing a torque sensor, is possible, especially when relying on improved pedaling modeling.