Predictive Electric Motor Torque Control With Kalman Feedback
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Solution Overview
Problem
Current electric motor torque control systems rely on open-loop systems requiring significant calibration, which is inefficient and lacks robustness in regulating torque output effectively.
Innovation Solution
A system incorporating a power inverter and a model predictive control (MPC) module that uses a linear time varying or linear parameter varying model, along with feedback from Kalman filters and torque sensors, to regulate torque output in electric motors, employing a two-loop control architecture for improved control precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If open-loop control systems are used for torque control, then the system structure is simple, but significant calibration of numerous look-up tables is required and robust performance is not achieved
Solution Approach 1:
The patent implements closed-loop control by incorporating feedback mechanisms that continuously monitor motor performance and adjust control parameters in real-time. This feedback approach eliminates the need for extensive calibration of look-up tables while achieving robust torque control performance across varying operating conditions.
Solution Approach 2:
The patent employs dynamic control strategies that adapt control parameters based on real-time operating conditions rather than relying on static pre-calibrated look-up tables. This dynamic adjustment enables the system to maintain robust performance without requiring extensive calibration data for all possible operating scenarios.
2Reliability
If extensive calibration of look-up tables is performed, then torque control performance is improved, but calibration time and system complexity increase significantly
Solution Approach 1:
By using feedback-based closed-loop control, the system achieves accurate torque control performance without requiring extensive pre-calibration. The feedback mechanism allows the system to automatically adapt to operating conditions, eliminating the time-consuming calibration process while maintaining high performance.
Solution Approach 2:
The control system performs self-adjustment through real-time feedback and adaptation, eliminating the need for external calibration processes. The system automatically optimizes its control parameters based on measured performance, thereby reducing calibration time to minimal or zero while maintaining torque control accuracy.
3Measurement precision
If more sensors are added to improve measurement accuracy, then control precision is enhanced, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical torque sensors with model-based estimation techniques that calculate torque from electrical measurements and motor parameters. This substitution eliminates the need for additional mechanical sensors while maintaining measurement precision through mathematical modeling and observer-based estimation methods.
Solution Approach 2:
The patent introduces intermediate calculation layers that derive torque information from existing electrical measurements (currents, voltages) through mathematical models. These intermediary calculations act as virtual sensors, providing accurate torque measurements without requiring direct physical sensing, thereby reducing system complexity.
Data Source
AI summary
A system for torque control of an electric motor in a motor vehicle includes a power inverter that delivers a current to the electric motor to regulate the torque of the electric motor and a model predictive control module that sends a three-phase voltage to the power inverter to control the operation of the power inverter.