Fuel Economy Optimization via Driver-Environment Segmentation
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Solution Overview
Problem
Existing fuel-economy driver interface technologies lack actionable information and fail to differentiate between driver-caused and environment-caused fuel economy inefficiencies, leading to ineffective feedback and limited optimization of vehicle operation for maximum fuel efficiency.
Innovation Solution
An active driver assistance system (ADAS) that utilizes a look-ahead powertrain management module, sensor fusion, scenario recognition, and arbitration to provide actionable feedback and optimize vehicle operation by distinguishing between driver and environmental factors, employing on-board sensors, GPS, and vehicle-to-infrastructure communication to predict future conditions and adjust engine and transmission controls accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If passive feedback systems are used to provide fuel economy information, then the driver receives information about fuel consumption, but the feedback lacks actionable information and cannot differentiate between driver-caused and environment-caused inefficiencies
Solution Approach 1:
The system segments fuel economy information into driver-caused and environment-caused components by analyzing multiple data sources including vehicle sensors, GPS location, digital map data, and traffic information. This segmentation allows the system to provide differentiated feedback that specifically addresses driver behavior while accounting for environmental factors.
Solution Approach 2:
The system performs preliminary analysis of driving conditions by comparing actual vehicle operation with predicted optimal operation based on advance knowledge of road geometry, elevation changes, and traffic patterns. This allows the system to determine in advance what portion of fuel consumption is attributable to driver behavior versus environmental conditions.
2Measurement precision
If existing fuel economy scoring systems are used, then drivers receive a fuel efficiency rating, but the score is confusing and misleading because it does not differentiate between driver and environment factors
Solution Approach 1:
The fuel economy score is segmented into distinct components: a driver behavior score that reflects only actions within the driver's control, and an environmental factor assessment that accounts for road grade, traffic conditions, and weather. This segmentation makes the feedback both more accurate and easier for drivers to understand and act upon.
Solution Approach 2:
The system provides continuous feedback that compares actual fuel consumption with predicted consumption based on optimal driving behavior for the given conditions. This feedback loop enables drivers to see the direct impact of their actions on fuel economy in real-time, making the information both precise and actionable.
3Ease of operation
If passive driver assistance is used, then the driver receives advisory feedback, but the response is slow and coarse and becomes distracting in situations requiring fast response
Solution Approach 1:
The system dynamically adjusts the level and type of feedback provided to the driver based on the driving situation. In normal conditions, it provides detailed advisory feedback to optimize fuel economy. In situations requiring fast driver response, it reduces feedback to avoid distraction while maintaining fuel economy optimization capabilities.
Solution Approach 2:
The system introduces an intermediary layer of intelligence that processes driving conditions and determines the appropriate level of driver intervention needed. This intermediary can suggest optimal actions to the driver while respecting situations where immediate driver response is required, balancing fuel economy optimization with safety and responsiveness.
4Productivity
If active assistance systems are used to control vehicle operation, then fuel efficiency can be improved, but the systems lack intelligence to differentiate between driver-caused and environment-caused inefficiency
Solution Approach 1:
The active assistance system segments control actions based on their intended effect: some actions are designed to compensate for environmental factors (such as adjusting for road grade), while others are designed to encourage optimal driver behavior. The system uses segmentation to ensure that control interventions are appropriately targeted and do not inadvertently punish drivers for environment-caused fuel consumption.
Solution Approach 2:
The system performs preliminary assessment of driving conditions using GPS, digital map data, and sensor information to predict environmental factors that will affect fuel consumption. Based on this advance knowledge, it adjusts its control strategy to differentiate between driver-caused and environment-caused inefficiencies, applying active assistance only when driver behavior can be improved without compromising safety or comfort.
Data Source
AI summary
Disclosed is an exemplary method for optimizing vehicle performance. The method includes determining an optimized drive torque for maximizing vehicle fuel economy and detecting a driver requested drive torque. A determination is made on whether the driver requested drive torque is performance related or safety related. The arbitrated drive torque is set to the optimized drive torque when it is determined that the driver requested drive torque is not performance and safety related. The arbitrated drive torque is set to the driver requested drive torque when it is determined that the driver requested drive torque is either performance or safety related.