Using Algorithms to Increase Motor Efficiency

In the face of economic uncertainties and increasing environmental concerns, many businesses are trying to make their operations more lean, efficient, and environmentally friendly. Examining your electricity bill is a good place to start. The top consumers of electricity are HVAC systems, water heating, lighting, office equipment, and machinery.

By Christian Fritz, National Instruments October 1, 2009
For more information, visit:
Download Field Oriented Control IP for LabView FPGA at
DOE whitepaper, Buying an Energy Efficient Motor at
DOE MotorMaster+ sizing tool for AC induction motors at
Copperhill Media VisualSizer for DC motors

In the face of economic uncertainties and increasing environmental concerns, many businesses are trying to make their operations more lean, efficient, and environmentally friendly. Examining your electricity bill is a good place to start. The top consumers of electricity are HVAC systems, water heating, lighting, office equipment, and machinery. More specifically, the motors within these machines are responsible for approximately two-thirds of the total electrical energy consumption in a typical industrial facility. To improve the efficiency and lower operating costs of motors in your enterprise, consider the following factors.

High efficiency motors

A motor running at 50% efficiency is converting only half of the electrical power into useful mechanical work. The rest is wasted. This makes the extra investment in efficient motors prudent since electricity costs make up 96% of the total life cycle costs of a motor. According to the U.S. Department of Energy (DOE), switching to a motor with a 4-6% higher efficiency rating can pay for itself in two years if the motor runs more than 4,000 hours a year.

Unfortunately, simply replacing existing equipment is a luxury. Many facilities host motors that are very large and costly to replace. Hence, users are always looking for ways to squeeze more efficiency out of existing assets. The key to reaping savings could lay in the drive control algorithms and implementation of commercial-off-the-shelf (COTS) hardware. Essentially, when you cannot replace the motor, replace the algorithm and controller to achieve better efficiency. With high computational power silicon devices, such as the Virtex or Spartan FPGAs from Xilinx, along with available commercial off the shelf (COTS) hardware like National Instruments’ CompactRIO, one can rapidly prototype and realize precise custom control systems to increase motor efficiency significantly.

Right size motors

A second fundamental component is proper motor sizing. The DOE estimates that 80% of all motors are oversized, causing businesses to pay a high price in wasted energy. As shown in the graph, efficiency drops dramatically when the load is below approximately 40% of the full-rated load. A number of sizing tools are available online to assist you in the process, such as MotorMaster+ for AC induction motors and VisualSizer for DC servo motors. When sizing, a good rule of thumb is to choose a motor with a peak and RMS torque rating approximately 25% higher than the application requires. Similar to advances in FPGA technologies which reduce complexities in design, new virtual prototyping tools are just around the corner to help provide more accurate torque and velocity data by linking motion control programming software, such as NI LabView, with 3D mechanical CAD environments for simulation and rapid design prototyping.

Motors running at leass than full load can lose much of their efficiency, this is particularly true of smaller size motors.

Appropriate motor technology

The type of motor you choose for an application has a big impact on energy efficiency. Induction motors, also known as asynchronous ac motors, are one of the oldest and most well established types of motor. With their low cost and ability to operate without sophisticated controls, ac induction motors are the workhorse for most household goods. They are usually operated in constant speed applications but can also be augmented with more sophisticated controls for use in applications requiring variable speed and torque.

For low-power applications, inexpensive stepper motors and brushed dc motors are popular due to the simple control circuitry necessary. However, they provide somewhat lower energy efficiency and therefore higher operating costs. Stepper motors are particularly inefficient, because they draw power even when stopped and they must be significantly oversized due to poor torque output at high speeds.

Brushless dc motors and permanent magnet synchronous ac motors (PMSM) are both commonly referred to as brushless dc (BLDC) motors but they do differ in the way their stator is wound. When rotated, the stator of the BLDC is wound in such a way as to produce a trapezoidally shaped back emf voltage, while the PMSM produces a sinusoidally shaped voltage. Brushless dc motors are more costly but provide better energy efficiency and performance when controlled using advanced algorithms compared to the ac induction motors explained above, and they can scale up to serve very high power and high speed applications.

BLDC motors are a type of synchronous motor. This means the magnetic field generated by the stator and the magnetic field off the rotor rotate at the same frequency. Usually BLDCs are equipped with three phases. Most BLDC motors have three stator windings connected in star fashion. The internal structure is like an induction motor containing pairs of permanent magnets on the rotor rather than windings. Since there are no brushes, commutation must now be provided electronically. To rotate the BLDC motor, the stator windings are energized in a sequence. To calculate which winding to energize at a time it is necessary to know the rotor position typically measured by three Hall Effect sensors embedded into the stator of the motor. Based on the triple combination of these sensor signals, the exact sequence of commutation can be determined by the control electronics. Because brushless motors use permanent magnets in their rotor rather than passive windings, they natively provide higher power for their size and weight compared to induction motors. The key to high efficiency operation, however, lies in the control system.

Control algorithms for motors

The use of microprocessing technologies in motor control has increased in recent years. Their purpose is to control algorithm execution in order to deliver better efficiency. For example, when using brushless motors, a wide range of control system algorithms is available, including trapezoidal, sinusoidal, and field-oriented control.

Trapezoidal control: Also known as six-step control, trapezoidal control is the simplest but lowest performance method. For each of the six commutation steps, the motor drive provides a current path between two windings while leaving the third motor phase disconnected. This method has significant performance limitations in the form of torque ripple which causes vibration, noise, mechanical wear, and greatly reduced servo performance.

Sinusoidal control: Also known as voltage-over-frequency commutation, sinusoidal control addresses many of these issues. A sinusoidal controller drives the three motor windings with currents that vary smoothly. This eliminates the torque ripple issues and offers smooth rotation. The fundamental weakness of sinusoidal commutation is that it attempts to control time-varying motor currents using a basic proportional-integral (PI) control algorithm and doesn’t account for interactions between the phases. As a result, performance suffers at high speeds.

Field-oriented control (FOC): Also known as vector control, FOC improves upon sinusoidal control by providing high efficiency at faster motor speeds. It delivers the highest torque-per-watt of power input compared to other control techniques, and allows precise and responsive speed control when the load changes. FOC also guarantees optimized efficiency even during transient operation by perfectly maintaining the stator and rotor fluxes.

Understanding FOC

One way to understand how FOC works is to form an image of the coordinate reference transformation process. If you picture an ac motor operation from the perspective of the stator, you see a sinusoidal input current applied to the stator. This time variant signal generates a rotating magnetic flux. The speed of the rotor is a function of the rotating flux vector. From a stationary perspective, the stator currents and the rotating flux vector look like ac quantities.

Now, imagine being inside the motor and running alongside the spinning rotor at the same speed as the rotating flux vector generated by the stator currents. Looking at the motor from this perspective during steady state conditions, the stator currents look like constant values, and the rotating flux vector is stationary. Ultimately, you want to control the stator currents to obtain the desired rotor currents. Using coordinate reference transformation, the stator currents can be controlled like dc values using simple PI-control loops. Under the hood, the FOC algorithm works by removing time and speed dependencies and enabling direct and independent control of both magnetic flux and torque. This is done by mathematically transforming the electrical state of the motor into a two-coordinate time-invariant rotating frame using mathematical formulas known as Clarke and Park transformations.

An efficient method to control the power electronics is called space vector pulse width modulation (PWM). It simultaneously maximizes usage of motor supply voltage and minimizes harmonic losses. Harmonics can significantly reduce motor efficiency by inducing energy-sucking eddy currents in the iron core of the motor. Best of all, field-oriented control can be utilized for both ac induction and brushless dc machines to improve efficiency and performance, and FOC can be applied to existing motors by upgrading the control system. In fact, vector control techniques such as FOC, can be employed with ac induction motors to enable servo-motor-like performance.

FOC with FPGAs

To implement FOC, powerful computation devices are needed which makes FPGA advancements in lower cost-to-performance a natural fit for motor control. The vector control algorithm must be continuously recomputed, at a rate of 10 to 100 kHz. In parallel to the control algorithm, additional IP (intellectual property) blocks such as the high speed PWM outputs need to execute without affecting control algorithm timing. Capable to perform control algorithms with loop rates up to hundreds of KHz, combined with its inherent parallel execution and hardware reliability can make an FPGA perfect solution for this application. This approach leaves additional room to perform communication and provide data for user interface applications, and the reconfigurability of FPGAs allows users to adjust the control algorithm whenever necessary.

The NI LabView FPGA module delivers graphical development for FPGAs on Reconfigurable I/O (RIO) COTS hardware targets allowing users to create custom applications using built-in functions or existing HDL IP. LabView is well suited for FPGA programming because it clearly represents parallelism and data flow. IPNet ( ) is a companion site for LabView FPGA to search, download, and exchange additional IP algorithms. Field-oriented control algorithms for LabView FPGA can be downloaded free of charge through the NI intellectual property network (IPNet).

To connect the algorithm embedded in the FPGA to real world signals, the compact RIO and single board RIO offer a wide range of I/O connectivity and validated I/O drivers to read the Hall Effect sensors and control the power electronics driving the motor. NI Single-Board RIO is a low-cost OEM board-level embedded platform capable of executing the same code developed for the compact RIO modular platform. This combined solution allows design teams to prototype embedded systems rapidly with modular, flexible compact RIO then quickly deploy to low-cost single-board embedded hardware with 100% code reuse. Other key benefits of such a solution include shortened time to market and increased machine reliability with validated middleware.

One of the biggest challenges in embedded design is the effort required to create, debug, and validate driver-level software stacks to integrate all of the hardware components of the embedded system. Traditionally, this integration process is left to the user, which complicates and lengthens the embedded system design process. The RIO platform middleware drivers go beyond the basic drivers that traditional single-board computer and other embedded system providers offer to deliver increased productivity and performance and short time to market.

Driver software and additional configuration services software are included with every RIO-supported device. The built-in middleware driver tools contain built-in functions for interfacing between analog, digital, motion, and communication I/O and the FPGA, transfer functions for data communication between the FPGA and processor, methods for interfacing the FPGA/processor to memory, functions for interfacing the processor to peripherals (RS232 serial, Ethernet), and multi-threaded drivers for high performance.

Improving motor operating efficiency can produce significant energy and dollar savings, and provide a rapid return on investment. For example, a 5% efficiency increase on just one 500 horsepower motor operated 8,000 hours/year could save over $12,000 and 170 kWh of electricity each year. When evaluating control system upgrades, keep in mind that energy costs are typically orders of magnitude higher than hardware costs over the lifecycle of the motor.

Author Information
Christian Fritz is product manager for motion control and mechatronics for National Instruments. Reach him at