Sub-resolution Fuel Level Measurement via Data Synchronization
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Solution Overview
Problem
Existing fuel-level sensors in vehicles and generators have limited precision due to sparse sensor arrangements and environmental distortions, leading to inaccurate fuel consumption, refill amount estimation, and fuel theft detection, with current solutions being either costly or prone to corrosion and calibration issues.
Innovation Solution
A method using hardware processors to integrate fuel injection rates and cluster discrete fuel levels into sub-resolution measurements, synchronizing fuel consumption and level series to generate accurate fuel level estimates, detect refill and pilferage events, and adapt clustering parameters over time, without requiring replacement of existing sensing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete electromagnetic sensors are used to measure fuel levels, then the sensing system is inexpensive and simple to implement, but the measurement precision is limited due to sparse sensor arrangement
Solution Approach 1:
The patent segments the measurement process into two independent parts: (1) discrete electromagnetic sensors provide coarse fuel level measurements, and (2) a processing system segments the time-series data into refill events, consumption events, and steady-state periods to generate sub-resolution measurements. This segmentation allows achieving high precision without increasing sensor density.
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between the discrete sensor readings and the final fuel level measurements. This intermediary system uses fuel consumption data, vehicle operational parameters, and clustering algorithms to generate continuous sub-resolution measurements, effectively bridging the gap between discrete sensors and continuous measurement requirements.
2Measurement precision
If more sensors are added to reduce dead-bands, then measurement coverage improves, but system cost and complexity increase
Solution Approach 1:
The system uses existing vehicle data (fuel injection rates, operational parameters) to self-generate continuous measurements without requiring additional sensors. The processing system serves itself by leveraging available data streams and algorithms to eliminate dead-bands and provide continuous measurement coverage.
Solution Approach 2:
The processing system performs multiple functions simultaneously: it clusters sensor readings, detects refill events, calculates fuel consumption, generates sub-resolution measurements, and adapts to sensor wear. This multi-functionality eliminates the need for additional specialized sensors to address each measurement gap.
3Measurement precision
If fixed sensor spacing is used, then manufacturing is simple, but measurement accuracy varies in different fuel tank regions
Solution Approach 1:
The patent implements dynamic measurement generation where the effective sensor spacing adapts based on operational conditions. During refill events, the system captures high-resolution data points; during steady-state periods, it uses fuel consumption rates to interpolate between discrete sensor readings. This dynamic approach achieves uniform accuracy without requiring dynamic physical sensor spacing.
Solution Approach 2:
The system changes the parameter of measurement resolution dynamically based on operational context. During refill events, it captures detailed level changes; during normal operation, it uses consumption-based interpolation. This parameter change in measurement granularity maintains manufacturing simplicity while achieving uniform accuracy across all fuel levels.
4Measurement precision
If existing sensing systems are replaced with high-precision sensors, then measurement accuracy improves, but system cost increases
Solution Approach 1:
The patent creates a virtual copy of continuous measurement capability through software algorithms rather than physical sensor replacement. The processing system generates sub-resolution measurements that replicate what high-precision sensors would provide, using existing discrete sensors combined with fuel consumption data and clustering algorithms.
Solution Approach 2:
The patent substitutes the mechanical/sensor-based measurement system with a computational system. Instead of relying on physical sensor density for precision, the system uses data processing, clustering, and consumption-based interpolation to achieve high measurement accuracy, replacing hardware complexity with software intelligence.
5Measurement precision
If discrete fuel level measurements are used, then the system is simple to operate, but fuel consumption and refill estimation accuracy deteriorates
Solution Approach 1:
The system implements feedback loops where sub-resolution measurements continuously refine fuel consumption estimates and refill detections. The clustering algorithm uses feedback from multiple data sources (sensor readings, consumption rates, operational parameters) to continuously improve measurement accuracy while maintaining automated operation without requiring complex user intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides more accurate fuel level measurements between discrete sensor levels, improving fuel consumption estimation, reducing fuel theft detection, and adapting to sensor wear and environmental changes, all while maintaining existing sensing systems.
Implementation Method 1
electromagnetic sensors are commonly used to measure fuel levels in the fuel tanks
Implementation Method 2
Magnetic reed switches are mounted and sealed in the tube at discrete levels. As the float moves vertically, it passes the reed switches. When the ring magnet on the float approaches a particular reed switch, it closes that reed switch.
Data Source
AI summary
Sub-resolution measurement of fuel in fuel tank. In an embodiment, data, which comprise discrete fuel levels in a fuel tank and fuel injection rates for an internal combustion engine, are received. The fuel injection rates are integrated over a traversed distance to produce a fuel consumption series, and the discrete fuel levels are clustered over the traversed distance to produce a fuel level series. The fuel consumption and fuel level series are synchronized into a model that is used to generate sub-resolution measurements of fuel levels between the discrete fuel levels.