Vehicle Fuel Control via Route Segmentation
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Solution Overview
Problem
Drivers face challenges in maximizing fuel economy due to varying driving conditions and the difficulty in accurately gauging instantaneous fuel consumption, which can be exacerbated by inconvenient charging times and limited charging station availability for electric vehicles.
Innovation Solution
A vehicle computing system that processes travel data to derive and execute fuel-efficient control strategies by breaking routes into segments, determining optimal vehicle behavior, and generating control strategies to optimize fuel usage based on previous travel data, allowing for improved vehicle control and fuel management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drivers rely on instantaneous fuel consumption displays to optimize fuel economy, then they can access real-time consumption data, but the numbers vary wildly during acceleration and cruising making it difficult to gauge accurate consumption
Solution Approach 1:
The route is divided into multiple segments with distinct characteristics (acceleration zones, cruising zones, deceleration zones). The system calculates fuel consumption separately for each segment type and provides targeted guidance for each, rather than presenting a single instantaneous value that varies wildly. This segmentation allows drivers to understand fuel consumption in the context of specific driving phases.
Solution Approach 2:
The system pre-calculates optimal fuel economy strategies for each route segment before the driver reaches it. By analyzing historical data and segment characteristics in advance, the system provides proactive guidance on how to drive each segment most efficiently, rather than reacting to instantaneous values after the fact.
2Measurement precision
If vehicles use estimated miles per gallon numbers to guide driving behavior, then drivers have a target fuel economy metric, but those numbers accommodate very specific driving parameters that rarely match real-world conditions
Solution Approach 1:
Instead of providing a single MPG estimate for the entire route, the system segments the route and provides fuel economy guidance specific to each segment type. Acceleration segments, cruising segments, and deceleration segments each receive tailored advice based on their characteristics, making the guidance applicable to varied driving conditions rather than relying on an average that accommodates specific parameters.
Solution Approach 2:
The system dynamically adapts fuel economy guidance based on actual driving conditions encountered. Rather than relying on static MPG estimates for specific driving parameters, the system continuously adjusts recommendations based on real-time segment characteristics, traffic conditions, and weather, making the guidance versatile across different scenarios.
3Use of energy by moving object
If drivers optimize charge consumption to ensure recharging opportunities at home, then they can extend driving range, but this requires complex monitoring and planning of charge levels against route segments
Solution Approach 1:
The route is divided into segments with calculated energy consumption characteristics. The system monitors charge levels against segment-specific requirements, providing simple guidance on when to charge based on segment analysis rather than requiring complex overall route optimization. This breaks down the complex charge management task into manageable segment-level decisions.
Solution Approach 2:
The system continuously monitors actual charge consumption against predicted consumption for each segment and provides feedback to the driver. This simple feedback mechanism guides drivers on charge management without requiring complex planning, as the system adjusts recommendations based on real-time monitoring of charge levels and segment characteristics.
Data Source
AI summary
A system includes a processor configured to determine that travel data, reflecting previous travel, exists for an upcoming route segment. The processor is also configured to derive a fuel-efficient control strategy for the upcoming segment, based on fuel efficient behavior reflected in the travel data. The processor is further configured to execute the control strategy in a vehicle while the vehicle travels over the route segment to instruct vehicle control in accordance with the control strategy for at least a portion of the segment.