Vehicle Controller Rough Road Detection Misfire Validation
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Solution Overview
Problem
Diagnostic systems often incorrectly diagnose rough road disturbances as engine misfires due to inadequate validation of rotational profiles, as they fail to accurately reflect the road surface conditions.
Innovation Solution
A method involving a vehicle controller that calculates the difference in wheel speeds between front and rear wheels, applies processing techniques such as scaling, filtering, and the cube law to determine a rough road detection bit, and monitors this bit for a hysteresis band to accurately identify rough road conditions and validate misfires.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wheel speed signals are processed with scaling, filtering, and cube law to detect rough road conditions, then rough road detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies scaling to wheel speed signals before processing to enhance resolution and detect subtle speed variations caused by rough roads. This preliminary signal conditioning improves measurement sensitivity without requiring complex hardware modifications
Solution Approach 2:
The patent introduces a low-pass filter as an intermediary processing step between raw wheel speed signals and the final rough road detection logic. This filter mediates by removing high-frequency noise while preserving the underlying speed differential patterns that indicate rough road conditions
Solution Approach 3:
The patent applies the cube law transformation to wheel speed signals, changing the parameter representation from linear to cubic. This nonlinear transformation amplifies small speed differences between front and rear wheels, making rough road conditions more detectable through parameter transformation rather than hardware complexity
2Reliability
If crankshaft sensor monitors rotational profile continuously, then misfire detection capability is improved, but false positive rate increases due to rough road disturbance
Solution Approach 1:
The patent introduces wheel speed differential analysis as an intermediary validation layer between crankshaft sensor data and misfire diagnosis. This intermediary system filters out false positives by cross-checking crankshaft irregularities against actual road condition indicators from wheel speed sensors
Solution Approach 2:
The patent implements a feedback mechanism where the rough road detection status (derived from wheel speed differentials) feeds back into the misfire detection logic. When rough road conditions are detected, the system adjusts its misfire detection thresholds or suppresses false misfire indications, creating a closed-loop validation system that reduces false positives while maintaining true misfire detection capability
Data Source
AI summary
Computer-implemented techniques include determining, at a controller of a vehicle, a wheel speed for each of two front wheels of the vehicle and two rear wheels of the vehicle. The techniques include calculating, at the controller, a difference between (i) an average of the wheel speeds for the two front wheels of the vehicle and (ii) an average of the wheel speeds for the two rear wheels of the vehicle to obtain an error. The techniques include setting, at the controller, a rough road detection bit when the error is greater than or equal to a threshold indicative of an error corresponding to rough road. The techniques include monitoring, at the controller, the rough road detection bit for a period to obtain a hysteresis band. The techniques also include determining, at the controller, whether the vehicle is traveling on rough road based on the hysteresis band.