Optimizing BLDC Motor Control Through System Simulation

Author:
Siddharth Mohan, Sr. Design Engineer

Date
06/30/2025

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Figure 1: How to run the ACT72350 BLDC motor example

Why BLDC motors?

Electric motors are responsible for roughly 50% of the world’s total electrical energy consumption, according to the European Commission1. Improving motor efficiency not only reduces operational costs but also plays a vital role in lowering global CO₂ emissions. Among available motor technologies, brushless DC (BLDC) motors stand out for their high efficiency, typically between 80% and 90%, compared to 75% to 80% for traditional brushed motors.

This efficiency, along with their durability and low maintenance requirements, has made BLDC motors a popular choice in both battery-powered and mains-powered applications. Driven by an inverter that regulates their speed and torque, BLDC motors are widely used in everything from power tools and household appliances to electric vehicles and industrial automation. Their popularity has also been helped by advances in microcontroller algorithms, which have made it easier to implement the precise control these motors require.

Unlike brushed motors, which use mechanical contacts to switch current, BLDC motors operate electronically. This eliminates the sparking and wear that contribute to maintenance issues and performance degradation in brushed designs. BLDC motors also produce less electrical and audible noise.

In terms of construction, a typical BLDC motor features permanent magnets mounted on the rotor shaft, with three field windings arranged around the stator. Rotation is achieved by sequentially energizing these windings. Fine control over motor behavior—such as speed, torque, and acceleration—is accomplished by adjusting the pulse width and frequency of the current applied to each coil.

To achieve such dynamic and precise control, feedback on the rotor’s position is essential. This can be done using position sensors (encoder or Hall sensors), or employing a sensorless method by measuring the back electromotive force (EMF) induced in the stator windings by the rotating magnets. This method is central to field-oriented control (FOC).

Extracting the best performance from a BLDC motor while maintaining efficiency requires careful tuning and dynamic control across a wide range of operating conditions. This is where system simulation becomes invaluable. By simulating the motor, control algorithms, and load conditions together, engineers can optimize the entire system—balancing performance, efficiency, and robustness long before hardware is built.

Qorvo’s ACT72350 BLDC integrated driver

To streamline the design of BLDC motor-based applications, Qorvo introduced the ACT72350, a 160 V standalone three-phase BLDC motor driver with integrated power manager and Configurable Analog Front-End (CAFE).

By integrating bootstrap diodes and all the analog blocks needed to measure current and protect the device, the ACT72350 allows for smaller solutions and replaces up to 40 discrete components in a BLDC motor control system, significantly reducing the BOM.

The CAFE allows customers to precisely configure their own sensing and position requirements, while the wide input voltage range from 25 V to 160 V allows the same design to be reused for a variety of battery-powered motor control applications.

The ability to drive up to 20S battery power makes the ACT72350 ideal for battery-powered applications between 48 V and 120 V, including:

  • Power tools
  • Garden tools
  • Motor controllers
  • Drone/RC
  • E-bike
  • E-vehicle
  • Ped-electric bikes
  • Light HEV

QSPICE® simulator

QSPICE®, introduced in 2023 by Qorvo®, marks a new era in circuit simulation software—offering power and analog designers a substantial boost in productivity through faster simulation speeds, enhanced functionality, and improved reliability.

Beyond pushing the boundaries of traditional analog simulation, QSPICE empowers engineers to model complex digital circuits and algorithms with ease. Its innovative blend of modern schematic capture and high-speed mixed-mode simulation makes it the go-to solution for addressing the growing hardware and software challenges system designers face today.

Offered at no cost2, Qorvo’s QSPICE includes a wide range of advancements over legacy analog modeling tools, such as:

  • Comprehensive support for advanced analog and digital system simulations, including those used in AI and machine learning applications.
  • A next-generation simulation engine that leverages cutting-edge numerical methods and is optimized for today’s computing platforms—featuring a GPU-rendered user interface and SSD-aware memory management—to deliver significant improvements in speed and accuracy.
  • Dramatically reduced runtimes and a 100% completion rate based on Qorvo's benchmarks using a suite of challenging test circuits—compared to up to a 15% failure rate with other popular SPICE simulators.
  • Access to a continuously updated QSPICE model library, including Qorvo’s advanced power management solutions, enabling easy evaluation and design with Qorvo’s power portfolio.

QSPICE is available now at www.qspice.com, with active support from Qorvo and a thriving user community through the QSPICE forum at forum.qorvo.com.

Model generator

Qorvo® has introduced a powerful new enhancement to its QSPICE circuit simulation software. This latest update streamlines the process of generating highly accurate models for semiconductor components—enabling electronic designers to complete tasks in just minutes rather than hours, all through a new tool (‘model generator’) now included in the free QSPICE software package.

This newly integrated feature supports the creation of simulation models for discrete components such as Junction Field Effect Transistors (JFETs), Metal Oxide Semiconductor Field Effect Transistors (MOSFETs), and diodes—using standard parameter data commonly found in datasheets. According to Mike Engelhardt3, the creator of QSPICE, this addition represents the most significant advancement in circuit simulation since the platform’s debut in 2023. This feature enables designers to create models for the power MOSFETs they want to use in their BLDC motor circuit simulations.

A full BLDC motor simulation in QSPICE

Krismon Budiono, an electronic circuit simulation enthusiast, recently developed a BLDC motor model in QSPICE4. To achieve this important result, Krismon made use of the concepts provided by Professor Marcos Alonso, who has created and posted nearly 100 power electronics design lessons on YouTube (https://www.youtube.com/@MarcosAlonsoElectronics/videos). Professor Alonso switched from LTspice to QSPICE to illustrate these lessons, after developing a BLDC motor model in LTspice a couple of years ago5,6.

To run and test the behavior of the BLDC motor, Krismon used a six-step commutation control he created in Cblock, using a hall sensor model as a practical sensor. Six-step commutation, also referred to as trapezoidal commutation, is a switching technique used to control three-phase BLDC motors. It consists of switching energy between the three phases in sequence, creating a rotating field that rotates the motor.

QSPICE now features a full BLDC motor example that includes the BLDC motor model developed by Krismon (together with the six-step commutation control) and the simulation of the ACT72350 BLDC motor driver IC7. As shown in Figure 1, the example can be found by right-clicking on ACT72350 and selecting “Open example schematic”. All files required to run this schematic are included in the latest version of QSPICE.

Trapezoidal waveforms are used to drive a BLDC motor because this type of signal allows for higher system efficiency and because it maximizes the motor torque. The trapezoidal technique employs Hall-effect sensors to determine the position of the rotor; this information is essential for the drive, as it allows the speed and torque of the motor itself to be controlled. Figure 2 shows the waveforms of three trapezoidal back EMF of the BLDC motor taken from the “ea”, “eb, and “ec” nodes of the motor simulation.

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Figure 2: Visualization of trapezoidal back EMF waveformsin QSPICE

 

It is also interesting to look at the response of the motor inductors, characterized by high-frequency elements at the “OUT A”, “OUT B” and “OUT C” nodes. As shown in Figure 3, the model perfectly simulates the real conditions of the system.

Click image to enlarge

Figure 3: Waveforms corresponding to the motor inductors’ response

 

The signals involved in the BLDC motor model, six-step commutation control, and inverter, are shown in Figure 2 and Figure 33. As can be seen, the motor model also provides the angular velocity (“w_rpm”) and can simulate variable load conditions through the “T_load” terminal as well.

While the BLDC motor example included in QSPICE uses the ha, hb, and hc signals (Hall sensors) to detect the rotor’s position, sensorless applications can be developed with the ACT72350 motor driver simulation using techniques such as FOC.

When using a half-bridge configuration, the ACT72350 inserts a 100 ns dead time, or break-before-make (BBM), between each of the half-bridge drivers. The BBM timing can be programmed by accessing the SOC.CFGDRV1, SOC.CFGDRV2, and SOC.CFGDRV3 registers via the SPI interface. These registers are fully implemented in the model.

In addition to break-before-make, many motors also require control over the dV/dt of switching transitions, as high displacement currents associated with rapid voltage changes can degrade magnet wire insulation. Therefore, users will likely want to add gate resistors to slow down these transitions. The BBM behavior can also be adjusted using diodes and resistors to apply different gate resistor values for turn-on and turn-off events. However, inverter-grade motors may not require such dV/dt control.

Among the advanced features offered by the ACT72350 is gate driver fault protection, which involves sensing the source current of the bottom FET. The trip threshold and the blanking time used to filter out the leading-edge current spike are programmable via SPI-controlled registers. These features, including the thresholds and blanking times, are fully implemented in QSPICE.

While the proposed BLDC motor example uses a sensored control technique, sensorless motor control can also be simulated. In that case, the original schematic shown in Figure 1 should be modified by replacing the six-step block with an alternative control logic.

Krismon Budiono developed a working example that uses a block with two inputs: one for amplitude (Amp) and another for speed (dPhi_dt). This approach functions similarly to a Class D audio amplifier, where the outputs are switched at a high frequency and the motor serves as a low-pass filter.

Conclusion

QSPICE and ACT72350 are a perfect example of Qorvo's integrated approach to reducing power consumption in BLDC drives. Additionally, the controller can be paired with a wide variety of commonly used microcontrollers.

Qorvo

References

1 European Commission, Ecodesign Regulation

2 QSPICE simulator

3 Mike Engelhardt’s video, Learn How to Create QSPICE Models in Minutes

4 Krismon Budiono, BLDC Motor in Qspice Part 1 - Motor Modeling

5 Marcos Alonso, LTspice #15: How to Create a Brushless DC Motor Model (Part I)

6 Marcos Alonso, LTspice #16: How to Create a Brushless DC Motor Model (Part II)

7 Tim McCune (mccunets), QSPICE Forum Schematic Capture “ACT72350 BLDC Motor Driver”

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