Off-Road Vehicle Energy Coaching for Accurate Range Prediction
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Solution Overview
Problem
Existing vehicle energy consumption prediction systems rely on range computations based on stored energy or fuel, failing to consider driver behavior, vehicle configurations, and environmental conditions, and require manual adjustments from the driver seat, lacking feedback on how to optimize energy consumption.
Innovation Solution
A system that predicts energy availability by considering driver behavior, vehicle configurations, and environmental conditions using data from other vehicles, providing recommendations for adjustments such as weight distribution and mode changes without manual driver intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If range computation based on stored energy or fuel is used, then the prediction method is simple, but the accuracy of energy availability prediction deteriorates because driver behavior, vehicle configurations, and environmental conditions are not considered
Solution Approach 1:
The patent combines multiple data sources including driver behavior data, vehicle configuration data, and environmental condition data with the stored energy information to create a comprehensive energy availability prediction model. This merging of diverse data types enables accurate predictions while accounting for the complex interactions between various factors affecting energy consumption.
Solution Approach 2:
The system is designed to handle multiple types of inputs and predictions universally. It can process different vehicle configurations, various driver behaviors, and diverse environmental conditions through a single integrated framework, making the prediction system adaptable to multiple scenarios without requiring separate specialized systems.
2Ease of operation
If manual adjustments from driver seat are required, then the system is easy to operate, but the ability to provide optimized energy consumption feedback deteriorates
Solution Approach 1:
The system implements continuous feedback loops that monitor actual energy consumption against predicted consumption and provide real-time recommendations to the driver. This feedback mechanism enables the system to automatically optimize energy consumption by suggesting specific adjustments to vehicle settings or driving behavior based on actual performance data, thereby improving productivity without requiring constant manual intervention.
Solution Approach 2:
The system performs automatic analysis and optimization recommendations without requiring manual driver intervention for data collection or system configuration. The vehicle's own sensors and systems provide the necessary data, and the system automatically processes this information to generate optimization suggestions, enabling self-service operation that maintains ease of use while improving energy efficiency.
3Productivity
If real-time coaching is provided during route traversal, then energy consumption efficiency is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary analysis of the route, vehicle configuration, and environmental conditions before the journey begins to establish baseline energy consumption predictions. This preliminary action allows the real-time coaching during traversal to focus on deviations from the plan rather than processing all data from scratch, thereby improving energy consumption efficiency while managing system complexity through staged processing.
Data Source
AI summary
Systems and methods are presented herein for generating energy consumption predictions for a vehicle, providing feedback on how to improve a rate of energy consumption of the vehicle, and generating recommendations (e.g., coaching) for improving an efficiency of energy consumption as a vehicle traverses a route (e.g., an off-road course). One or more of a rate of energy consumption of a vehicle or an amount of energy to be consumed by a vehicle arranged to traverse a route is predicted. Then one or more recommendations are provided to improve an efficiency of energy consumed by the vehicle while traversing the route. In response to determining the vehicle has completed the route, at least one recommendation is generated for improving an efficiency of energy consumption for a subsequent trip along the route.