Motor Lash Angle Detection Using Regression-Based Zero Point Tracking
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Solution Overview
Problem
Modern vehicles equipped with electric motors experience driveline lash due to manufacturing tolerances, leading to poor drive quality, as conventional methods struggle to accurately detect and adapt to changing lash angles over time, causing reduced performance and variability across vehicles.
Innovation Solution
A computer-implemented method calculates motor acceleration error, uses linear regression to determine a zero point, and integrates differences to determine the lash angle, creating a feedback loop for real-time control and adaptation, thereby improving drive quality by accurately accounting for lash angle changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to detect lash angle, then the system structure remains simple, but measurement precision deteriorates due to inability to accurately detect and adapt to changing lash angles
Solution Approach 1:
The patent replaces mechanical measurement devices with a computational approach. A processing device calculates motor acceleration error by comparing actual motor acceleration (derived from motor torque and motor speed) with commanded acceleration, then uses regression analysis to detect the zero point and determine lash angle. This substitution of mechanical sensing with computational methods achieves accurate lash angle detection without adding complex mechanical measurement hardware.
Solution Approach 2:
The system uses the motor's own operational data (motor torque and motor speed) to detect lash angle changes. By calculating motor acceleration error from the motor's existing control signals and sensor data, the system enables self-diagnosis and self-adjustment without requiring external measurement equipment, thereby improving measurement precision while maintaining system simplicity.
2Adaptability or versatility
If conventional fixed lash angle values are used, then device complexity remains low, but adaptability deteriorates due to inability to account for lash angle changes over time
Solution Approach 1:
The patent implements a feedback mechanism where the processing device continuously monitors motor acceleration error and uses regression analysis to detect zero points, thereby determining current lash angle values. This real-time feedback loop allows the control system to adapt to changing lash angles caused by driveline wear and manufacturing tolerances, significantly improving adaptability compared to fixed lash angle values while managing algorithmic complexity through efficient computational methods.
Solution Approach 2:
The system transitions from static, fixed lash angle values to dynamic, time-varying lash angle detection. By continuously calculating motor acceleration error and detecting zero points through regression analysis, the system adapts lash angle values in real-time based on actual driveline conditions, enabling the control system to respond to wear and manufacturing variations without requiring complex mechanical adjustment mechanisms.
3Reliability
If real-time lash angle detection is implemented, then drive quality improves, but use of energy increases due to continuous calculation and control
Solution Approach 1:
The patent applies regression analysis to detect the zero point in motor acceleration error, which occurs only during specific driveline transitions when lash changes occur. Rather than continuously analyzing all motor operation data, the system focuses computational resources on detecting zero points during relevant transition events, thereby improving drive quality through accurate lash angle detection while minimizing energy consumption by avoiding unnecessary continuous processing during steady-state operation.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes calculating, by a processing device, a motor acceleration error based at least in part on a motor torque and a motor speed. The method further includes calculating, by the processing device, a regression fit line based at least in part on the motor acceleration error. The method further includes identifying, by the processing device, a zero point using the regression fit line. The method further includes comparing, by the processing device, the zero point to a datum reference to determine a difference. The method further includes integrating, by the processing device, the difference to determine the lash angle. The method further includes controlling, by the processing device, the motor based at least in part on the lash angle.