Adaptive Auto Stop Threshold Adjustment for Vehicle Fuel Economy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing engine stop-start systems in vehicles rely on static threshold values for auto stops, which can lead to suboptimal fuel economy as they fail to adapt to individual driver behavior and geographic-specific conditions, limiting the frequency and duration of engine auto stops.
Innovation Solution
A vehicle system that adjusts auto stop parameter thresholds based on learned data from vehicle operation and geographic location, using machine learning techniques to alter the conditions under which engine auto stops are initiated, allowing for more opportunistic and efficient engine shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static threshold values are used for engine auto stops, then the system is simple and reliable, but fuel economy is suboptimal due to inability to adapt to driver behavior and geographic conditions
Solution Approach 1:
The patent implements dynamic threshold adjustment by transitioning from fixed static thresholds to adaptive thresholds that automatically adjust based on learned driver behavior patterns and geographic location data. The controller continuously modifies auto stop parameters (such as speed thresholds or duration thresholds) based on real-time and historical data, making the system dynamic rather than static. This resolves the contradiction by enabling adaptability while managing complexity through algorithmic automation rather than manual configuration.
Solution Approach 2:
The system incorporates feedback mechanisms by collecting vehicle operation data, analyzing driver behavior patterns, and using this information to adjust auto stop thresholds. The controller receives feedback from sensors about vehicle state, driver actions, and location, processes this information through learning algorithms, and adjusts thresholds accordingly. This closed-loop feedback system enables the adaptability needed for optimal fuel economy while keeping the system manageable through automated decision-making rules.
2Loss of energy
If adaptive threshold adjustment is implemented, then fuel economy is optimized, but the system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically learn and adapt to driver preferences and geographic conditions without requiring manual intervention or configuration. The controller autonomously collects data, identifies patterns, and adjusts thresholds based on learned information about when and where the driver typically stops the vehicle. This self-adjusting capability optimizes fuel economy while managing complexity by eliminating the need for manual system configuration and ongoing user input.
Solution Approach 2:
The system optimizes fuel consumption by dynamically changing key parameters such as auto stop speed thresholds, duration thresholds, and trigger conditions based on learned driver behavior and location data. Rather than using fixed parameters, the system continuously adjusts these parameters within acceptable ranges to maximize fuel savings. This parameter adaptation resolves the contradiction by achieving energy optimization through automated parameter tuning rather than complex manual configuration.
3Loss of energy
If frequent auto stops are implemented, then fuel economy improves, but driver comfort may deteriorate due to excessive engine shutdowns
Solution Approach 1:
The patent applies partial action by implementing auto stops selectively rather than maximizing frequency. The system learns to identify specific situations and locations where the driver actually stops the vehicle and applies auto stop functionality primarily in those scenarios. By partially implementing auto stops rather than attempting frequent stops in all conditions, the system achieves fuel savings in the most beneficial cases while avoiding excessive shutdowns that would compromise driver comfort. This selective approach resolves the contradiction by optimizing fuel economy without over-applying the intervention.
Solution Approach 2:
The system dynamically adjusts auto stop frequency and timing based on learned driver preferences and real-time conditions. Rather than applying auto stops uniformly or maximally, the controller adapts the frequency and duration of stops to match actual driver behavior patterns and geographic characteristics. This dynamic adjustment enables the system to achieve fuel savings when appropriate while automatically reducing stop frequency in situations where the driver prefers continuous operation, thereby maintaining comfort while improving efficiency.
Data Source
AI summary
A vehicle controller may initiate an auto stop of an engine in response to a value of an auto stop parameter falling within a specified range and alter the specified range based on learned information derived from vehicle data to change a frequency or duration of auto stops of the engine.