Fuel Consumption Estimation Using Refueling Bias Correction
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Solution Overview
Problem
There is a discrepancy between real-world fuel efficiency and controlled setting measurements, known as the fuel consumption gap, which has been increasing over time, necessitating new methods for vehicles to monitor and control fuel consumption effectively.
Innovation Solution
A vehicle system that uses a fuel flow model with a bias term, updated through refueling data, to monitor and adjust fuel consumption, including communication with offboard refueling stations for data exchange and alert generation, and calculates actual fuel efficiency metrics for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If controlled setting testing is used to demonstrate fuel efficiency compliance, then regulatory standards can be met, but the measured fuel efficiency does not reflect real-world conditions
Solution Approach 1:
The system continuously monitors actual fuel consumption using onboard sensors and compares it with the fuel consumption model predictions. This feedback loop allows the system to detect discrepancies between modeled and actual fuel efficiency, enabling continuous refinement of the model to better reflect real-world conditions while maintaining regulatory compliance capabilities.
Solution Approach 2:
The fuel consumption model automatically updates itself using refueling data from the vehicle's fuel tank measurements. The system self-calibrates by comparing the difference between modeled fuel consumption and actual fuel consumption (determined at refueling events), eliminating the need for external calibration and continuously improving measurement accuracy in real-world operations.
2Productivity
If a fuel flow model is used to monitor fuel consumption, then continuous monitoring capability is achieved, but model biases reduce measurement accuracy
Solution Approach 1:
The system uses refueling data as feedback to detect and correct biases in the fuel flow model. By comparing the cumulative fuel consumption predicted by the model with the actual fuel consumption determined at refueling events, the system identifies model biases and applies corrections to improve measurement precision while maintaining continuous monitoring capability.
Solution Approach 2:
The system dynamically adjusts model parameters based on detected biases. When discrepancies between modeled and actual fuel consumption are identified, the system modifies the fuel flow model parameters to eliminate the bias, thereby improving the accuracy of continuous fuel consumption measurements without sacrificing monitoring capability.
3Measurement precision
If refueling data is used to update the fuel consumption model, then measurement accuracy improves, but system complexity increases due to data management requirements
Solution Approach 1:
The system automatically manages refueling data without requiring complex external intervention. The controller autonomously detects refueling events, retrieves refueling data from the communication apparatus, processes the data to determine actual fuel consumption, and updates the fuel consumption model accordingly. This self-service approach minimizes data management complexity while improving measurement accuracy.
Solution Approach 2:
The system combines multiple functions into the existing fuel management controller: continuous fuel consumption monitoring, refueling data management, model bias detection, and model updating. By merging these functions into a single integrated system rather than separate components, the patent reduces overall system complexity while achieving improved measurement precision through refueling data utilization.
Data Source
AI summary
Engine systems, vehicles and methods related to refueling in conjunction with infrastructure to vehicle communication. A vehicle or engine system may receive refueling information including a fuel quantity at a refueling event, and uses the refueling information to identify and/or eliminate measurement or modelling bias in the underlying engine system controls. The vehicle may also self-determine fuel efficiency. The vehicle may use charging data, rather than refueling data, to perform calculations of battery life, state of charge, state of health, and efficiency in electric vehicle examples.