Vehicle Range Prediction Using Dynamic Load Adjustment
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Solution Overview
Problem
Current vehicle range prediction systems are inaccurate due to changes in tow load during a trip, requiring vehicles to travel a long distance before predicting range, which hinders effective trip planning.
Innovation Solution
A system that continuously monitors vehicle mass changes using sensors like accelerometers and motor torque sensors to calculate vehicle load, adjusting the predicted range based on real-time data and fuel efficiency reduction factors, allowing for dynamic range adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle range prediction is performed only after a long period of driving with the tow load, then the prediction accuracy is improved, but the usefulness for trip planning is worsened due to delayed information availability
Solution Approach 1:
The system performs preliminary range predictions using initial vehicle load estimates before the vehicle actually travels long distances with the tow load. This allows trip planning to occur in advance, while subsequent actual driving data is used to refine and update the predictions, thus eliminating the need to wait for long periods of actual driving before providing useful range information to the user.
Solution Approach 2:
The system dynamically updates range predictions as the vehicle accumulates driving data with the actual tow load. Instead of performing a single static prediction after a fixed long period, the system continuously refines predictions based on actual observed fuel consumption and load conditions, providing progressively more accurate information throughout the trip rather than only after extensive driving.
2Reliability
If the vehicle range prediction system waits for extensive driving data before providing predictions, then the reliability of predictions is improved, but the adaptability to changing load conditions is worsened
Solution Approach 1:
The system implements continuous feedback loops where actual fuel consumption data, vehicle speed, and load conditions are monitored in real-time. This feedback is used to adjust and refine range predictions dynamically, allowing the system to adapt to changing load conditions while maintaining reliability through continuous validation against actual observed performance rather than relying solely on pre-trip estimates.
Data Source
AI summary
A system is provided for vehicle range prediction. The system determines a change in mass to a vehicle while driving. Additionally, the system calculates a vehicle load in response to determining the change in mass and adjusts a vehicle range in response to calculating the vehicle load. The vehicle range is indicative of a distance in which the vehicle is predicted to travel with a remaining fuel. The adjusted vehicle range is based on the vehicle load.