Resolver Fault Prediction Using Offset and Wobble Trends
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Solution Overview
Problem
Existing diagnostic systems for electric vehicles and hybrid electric vehicles are unable to effectively predict faults in resolvers used with electric motors, leading to motor imbalances, torque errors, and inefficient propulsion.
Innovation Solution
A method and device for monitoring a propulsion system that involves determining a health indicator from diagnostic signals related to resolver offset and wobble, calculating a reference value, and continuously monitoring the health indicator to predict potential faults by comparing trend values to a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing diagnostic systems are used for resolver monitoring, then the system structure remains simple, but fault prediction capability is insufficient leading to motor imbalances and torque errors
Solution Approach 1:
The system performs preliminary fault prediction by continuously monitoring resolver health indicators and comparing them against reference values before actual faults occur. This allows early detection of deteriorating trends in resolver offset and wobble, enabling preventive maintenance before motor imbalances and torque errors develop.
Solution Approach 2:
The diagnostic system implements feedback by continuously comparing current health indicator values against reference values established during normal operation. When deviations exceed predetermined thresholds, the system generates fault predictions and alerts, creating a closed-loop monitoring system that improves reliability without requiring complex hardware modifications.
2Reliability
If continuous monitoring of health indicators is implemented, then early fault detection is achieved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on specific critical health indicators (resolver offset and wobble) rather than analyzing all possible sensor data. This selective approach enables early fault detection while maintaining efficient processing times by only calculating trend values for the most diagnostically significant parameters.
3Measurement precision
If threshold-based fault prediction is used, then fault detection accuracy improves, but false predictions may occur requiring multiple cycle confirmations
Solution Approach 1:
The system applies preliminary anti-action by requiring multiple cycle confirmations before generating a fault prediction alert. Instead of triggering immediate alerts on single threshold exceedances, the system waits for consistent deviations across multiple measurement cycles, thereby preventing false predictions while maintaining accurate fault detection through the use of predetermined confirmation thresholds.
Data Source
AI summary
A method of monitoring a propulsion system of a vehicle includes determining a health indicator from a diagnostic signal based on electrical outputs from a resolver connected to an electric motor of the propulsion system, the diagnostic signal related to a resolver offset and/or a resolver wobble, calculating a reference value based on values of the health indicator determined during a first time period, and monitoring the health indicator over a second time period, where the monitoring includes continuously or periodically calculating a trend value of the health indicator over a plurality of cycles. The method also includes comparing each trend value to the reference value and estimating a difference between the trend value and the reference value for each cycle, predicting whether a fault will occur based on the difference, and based on the predicting indicating that a fault will occur, outputting a fault indication.