In the last several years, many digital servo drive producers have developed features to prolong the life of both the rotary and linear motors they are paired with. At one time, a simple thermal switch or thermal-couple embedded in the motor’s winding was all that was required to protect the motor from overheating. Today, this is no longer the case as users are pushing motors closer to their theoretical torque and power limits. To protect motors under these rated conditions, accurate thermal models of the motor are needed. Basic thermal models typically monitor the motor’s average winding current. More advanced models monitor the motor’s rotor position, speed, ripple current, and average winding current. All of these models require thermal impedance information provided by the motor manufacture and knowledge of the ambient temperature. These models can then be programmed into a DSP and used in real time to predict both the motor’s winding and case temperatures.

The user must decide what the maximum allowable winding or case temperature can be. This is important as the higher the average winding temperature is allowed to go, the shorter the predicted life of the motor will be. In addition, the user may decide if the motor should fault in an over-temperature condition or have its output torque reduced to a value lower than its continuous rating in order to allow the motor time to cool. All these features are economically possible with the continual introduction of new lower cost DSPs and mixed-signal ASICS.

Servomotor thermal ratings

For a given motor package size and heatsink combination, there is a fundamental limit on the maximum allowable continuous power loss that can be dissipated as heat. This assumes both the ambient and maximum allowable winding temperatures are known as well as the desired life expectancy of the motor. Servomotors are also specified as having a particular continuous shaft torque rating. This rating coincides with the maximum allowable continuous power loss point. In general, it is acceptable for the motor to have short excursions above the continuous allowable operating point, but to allow the motor to cool they must be balanced with power loss points below the continuous power loss average point. If the motor is mounted to a gearbox, the continuous torque rating has to be decreased.This is because the gearbox adds another thermal interface and generates its own heat that must be accounted for in the total power loss budget.

One important servo motor specification is the insulation winding class. Typical permanent magnet brushless motors use class H winding insulation. This means that for every 10° C rise in temperature, the average life expectancy of the winding insulation is reduced by approximately 50%, as shown in Figure 1.

For class H insulation, setting a maximum allowable winding temperature of 180° C results in an average life of 30,000 hours or approximately 3.4 years of continuous duty. If the servomotor can potentially come into physical contact with an operator, the maximum case temperature is usually limited to about 50° C. This results in maximum average winding temperatures much lower than 180° C.

Servomotor losses

The losses for a three-phase sinusoidal servomotor can be modeled as the sum of several components.

Each of these loss components can be approximated. R_m is the motor’s electrical resistance phase to phase at a specified operating temperature. The motor’s effective phase current, i_rms must also be determined from the motion profile.

Shown here is the relationship between the motor speed and magnetic loss.

The value of n can range from one to two depending on the motor design. The values for K1 are generally quite small allowing for this term to be safely neglected for low pole count motors.

This illustrates the relationship between the mechanical losses and speed. This term is the combination of bearing friction, F and viscous damping, K₂. Again, this term is also generally neglected except for cases of very high speed or very small operating torques.

These assumptions allow us to approximate with.

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