Electric Machine Winding Temperature Estimation for Torque Control
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Solution Overview
Problem
Electric machines, such as IPM motors, lack direct temperature sensors for rotor magnets, leading to decreased output torque and efficiency due to temperature-induced magnetic field changes, necessitating a method to estimate winding temperature for effective control.
Innovation Solution
A system utilizing sensors to measure current, voltage, and coolant parameters, combined with a trained deep learning model, estimates stator winding temperature and adjusts current commands to compensate for temperature changes, thereby maintaining torque and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If no direct temperature sensors are installed on rotor magnets, then device complexity is reduced, but temperature measurement precision deteriorates
Solution Approach 1:
The patent uses stator winding temperature as an intermediary parameter to infer rotor magnet temperature. Since stator windings are thermally coupled to rotor magnets through the air gap and housing, measuring stator temperature provides indirect information about magnet temperature without requiring direct sensors on the magnets themselves.
Solution Approach 2:
The patent replaces direct physical temperature sensing on rotor magnets with an estimation algorithm that processes electrical measurements (currents, voltages, power losses) to infer temperature. This substitutes a mechanical/sensor-based approach with an computational/electrical approach.
2Device complexity
If temperature compensation is not implemented, then control system complexity is reduced, but output torque and efficiency deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where estimated magnet temperature is continuously fed back to the control system. This temperature information is used to adjust control parameters (such as current commands or voltage limits) to compensate for magnetic field strength changes, thereby maintaining optimal torque and efficiency.
Solution Approach 2:
The patent changes control parameters based on temperature conditions. As magnet temperature varies, the magnetic field strength changes, and the controller adjusts electrical parameters (currents, voltages, switching frequencies) to compensate for these changes and maintain consistent performance.
3Reliability
If derating control is implemented to allow thermal dissipation, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent implements dynamic control where the derating level is not fixed but adjusts continuously based on real-time temperature estimates and operating conditions. This allows the system to operate at full capacity when safe and apply derating only when necessary, optimizing both reliability and productivity dynamically.
Solution Approach 2:
The patent uses predicted temperature trends to take preliminary actions before thermal damage occurs. By monitoring temperature evolution and predicting future thermal states, the controller can proactively adjust power limits to prevent overheating while minimizing unnecessary derating.
Data Source
AI summary
In accordance with a trained deep learning model, the data processing system is configured to estimate a temperature of the stator windings of the electric machine based on the following input data: observed current into direct-axis current, observed quadrature-axis current, observed or estimated direct-axis voltage, observed or estimated quadrature-axis voltage, observed direct-current bus voltage, estimated torque of the rotor of the electric machine, estimated speed of the rotor of the electric machine, sensed coolant inlet temperature, and sensed coolant flow rate, wherein the trained deep learning model is trained in accordance with a truncated back propagation through time technique.