Quantifying the commutation error of a BLDC machine using sensorless load angle estimation

BLDC motors are often used for high speed applications, for example in pumps, ventilators and refrigerators. For commutation discrete position information is necessary. This feedback is often provided by Hall sensors instead of more expensive encoders. However, even small misalignment of the Hall sensors in low cost BLDC motors can lead to unwanted torque ripples or reduced performance of BLDC motors. This misplacement leads not only to noise and vibrations caused by the torque ripples but also to lower efficiency. In this paper, a self-sensing technique to assess the misalignment is introduced. The objective is to obtain knowledge of the quality of the commutation by quantifying the misalignment. The method used in this paper is based on the fundamental components of voltage and current measurements and only needs the available current and voltage signals and electrical parameters such as resistance and inductance to estimate the misalignment

A quantitative comparison between BLDC, PMSM, brushed DC and stepping motor technologies

Brushless DC machines (BLDC), Permanent Magnet Synchronous Machines (PMSM), Stepping Motors and Brushed DC machines (BDC) usage is ubiquitous in the power range below 1,5kW. There is a lot of common knowledge on these technologies. Stepping Motors are ideally suited for open loop positioning, BLDC machines are the most obvious candidate for high-speed applications, etc. However, literature lacks comprehensive research comparing these machines over a large range of applications. In this paper, more than 100 motors are considered. Their characteristics are compared and presented in a comprehensive way. These results support the common knowledge concerning the field of application of each technology and new insights follow from this quantitative comparison

Optimal load angle learning algorithm for sensorless closed-loop stepping motor control

Stepping motors are well suited for open-loop positioning tasks at low-power. The rotor position of the machine is simply controlled by the user. Every time the user sends a next pulse, the stepping motor driver excites the correct stator phases to rotate the rotor over a pre-defined discrete angular position. In this way, counting the step command pulses enables open-loop positioning. However, when the motor is overloaded or stuck, the relation between the expected rotor position based on the number of step command pulses and the actual rotor position is lost. To avoid this, the bulk of the widely used full-step open-loop stepping motor drive algorithms are driven at maximum current. This non-optimal way of control leads to low efficiency. To use stepping motors more optimally, closed-loop control is needed. A previously described sensorless load angle estimation algorithm, solely based on voltage and current measurements, is used to provide sensorless feedback. A closed-loop load angle controller adapts the current level to reach the setpoint load angle to obtain the optimal torque/current ratio. The difficulty is that the optimal load angle depends on the mechanical dynamics. To avoid the requirement of knowledge of the mechanical parameters, a practical learning algorithm to determine the optimal load angle is presented in this paper. Measurements validate the proposed approach

Modelling and Preliminary Design of a Variable-BVR Rotary Valve Expander with an Integrated Linear Generator

The Organic Rankine Cycle (ORC) is currently one of the most suitable technologies to convert waste heat into mechanical work or electricity. While large and medium scale systems are widely available on the market for various temperature and power ranges, small-scale ORCs below 50 kWe are still in a pre-commercial phase because of the relatively high specific cost per kW and the lack of technologically mature and high efficient expanders. Small-scale ORC installations for automotive applications operate at variable heat source profiles combined with the fluctuating power demand from a vehicle. The prediction of an optimum operating point is challenging. Exhaust gases are a limited heat source, therefore the more heat is recovered at an optimal cycle efficiency level, the more power is produced. By using advanced cycle architectures (e.g. trilateral ORCs, partial-evaporating ORCs, zeotropic mixture ORCs, etc.) and the right fluids, an optimum can be found. An expander with a variable built-in volume ratio (BVR) can allow to operate at optimal conditions within the whole range of pressures imposed by the variable heat source and heat sink. Adjustable expanders are known but mainly limited to large-scale applications. Neither a positive displacement expander, nor a turbine can provide an optimal expansion of a working fluid in a wide range of operation conditions. As a response to this challenge, the concept of a variable-BVR piston expander with an integrated linear generator is proposed in this paper. The internal part-load control is based on a rotary valve which controls the suction and discharge processes in the expander. An analytic model has been developed to relate the position of the valve with the motion of the piston. By means of a deterministic model, the influence of the main design parameters is investigated. A preliminary design based on the expander model results is described and the predicted performance over the operating range of interest is discussed

Proportional-integral state-feedback controller optimization for a full-car active suspension setup using a genetic algorithm

The use of active car suspensions to maximize driver comfort has been of growing interest in the last decades. Various active car suspension control technologies have been developed. In this work, an optimal control for a full-car electromechanical active suspension is presented. Therefore, a scaled-down lab setup model of this full-car active suspension is established, capable of emulating a car driving over a road surface with a much simpler approach in comparison with a classical full-car setup. A kinematic analysis is performed to assure system behaviour which matches typical full-car dynamics. A state-space model is deducted, in order to accurately simulate the behaviour of a car driving over an actual road prole, in agreement with the ISO 8608 norm. The active suspension control makes use of a Multiple-Input-Multiple- Output (MIMO) state-feedback controller with proportional and integral actions. The optimal controller tuning parameters are determined using a Genetic Algorithm, with respect to actuator constraints and without the need of any further manual fine-tuning

Sensorless load angle detection for brushless direct current and stepping motors

The possibility to accurately position without the need for a position sensor makes stepping motors very appealing for positioning applications. However, drawbacks of the open-loop control are the continuous risk of missing a step due to overload, the high torque ripple and low efficiency.   In addition, BLDC motors are particularly outspoken for speed varying applications. Literature shows that the control method can be improved by controlling a BLDC motor with sinusoidal currents instead of square-wave currents. Unfortunately, feedback techniques typically used in BLDC motors to determine the commutation moments are inadequate for sinusoidal current setpoint generation.   In this study, a sensorless feedback mechanism indicating the actual load and a controller preventing step loss, without noticeably increasing the cost, is proposed for stepping motor applications. In parallel, a computationally simple sensorless method that uses sinusoidal currents to increase the efficiency of BLDC motors is developed

Sensorless load angle control for energy optimal sinusoidal driven BLDC motor applications

Choosing sine-wave instead of square-wave shaped currents to drive a brushless DC (BLDC) motor can increase the energy-efficiency up to 9.5%. But unfortunately for sine-wave setpoint current generation, the typical electronic low-resolution commutation feedback techniques becomes unavailable. Techniques such as Hall sensors or sensorless algorithms based on back-electromotive force (back-EMF) sensing to detect rotor position information are inadequate for sinusoidal current generation. For broad industrial employability, other sensing techniques such as computationally complex observers or signal injection methods requiring access to the switching states of the power electronics are not preferred. This raises the need to develop a computationally sufficiently simple sensorless controller that optimizes the energy efficiency. Therefore, the authors propose a PID algorithm controlling the estimated load angle technique that enables sinusoidal current supply without the need for position feedback or user input. The aim is to implement a controller for dynamic speed-varying BLDC motor applications, which means that accurate speed trajectory tracking behaviour and high robustness against load changes should be guaranteed. The application-dependent PID-settings for setpoint and disturbance rejection control are estimated during an initialization speed trajectory based on only one stator winding current and voltage measurement. The proposed control algorithm is validated through experimental measurements on a BLDC motor with a nominal speed and power of 3000 RPM and 225 W respectively

Online impedance estimation for sensorless BLDC motor motion applications

Hall sensors embedded in Brushless DC system enable electronic commutation. However, even small misalignment of the Hall sensors can lead to unwanted torque ripples or reduced performance. Sensorless back-EMF feedback algorithms based on the stator winding dynamics seem a promising alternative for the Hall sensors. However, what remains to be done to arrive at an implementable solution is to assess the electrical parameters, i.e. resistance and inductance, as these parameters vary with the motor conditions and cannot be assumed constant. Therefore in this paper, an impedance estimator is presented. An disconnected approach is proposed, whereby the back-EMF estimation is seen separately from the real-time identification of stator impedance. Through this, the accuracy of the back-EMF estimator depends on the accuracy of the resistance and inductance estimator and not vice versa. The proposed estimation algorithm is validated through experimental measurements on a 225 W BLDC

Self-learning current optimizing control for conventional stepping motor drive technique based on step pulses

The essential advantage of the conventional stepping motor drive technique bases on step command pulses is the ability of open-loop positioning. By ruling out the cost of a position sensor, stepping motors are preferred in low power positioning applications. However, machine developers also want to obtain high dynamics with these small and cheap stepping motors. For that reason, stepping motors are used at its limits as much as possible. A drawback of the open-loop control is the continuous risk of missing a step due to overload. Due to this uncertainty, robustness is a major issue in stepping motor applications. Until today, to reduce the possibility of step loss, the motor is typically driven at maximum current level or is over-dimensioned with results in low-efficiency. Therefore in this paper, a self-learning [Formula: see text]-controller optimizing the current is presented. Moreover, to allow broad industrial applicability, this technique is computationally simple, needs no mechanical or electrical parameter knowledge and take into account the unique character of stepping motors and their conventional drive technique based on step command pulses. The proposed algorithm is validated through measurements on a hybrid stepping motor. </jats:p