Vehicle Refueling Optimization Using Environmental Data
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Solution Overview
Problem
Current vehicles lack the ability to determine optimal times and locations for refueling based on environmental and vehicle data, leading to constrained refueling opportunities.
Innovation Solution
A computer system that collects data on coolant temperature, atmospheric ozone levels, air quality, and traffic congestion, among other factors, to determine a weighted refueling score and location score, enabling the vehicle to autonomously navigate to a suitable fueling station for refueling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the vehicle refuels at any time and location, then refueling flexibility is improved, but environmental constraints (ozone levels, air quality) worsen
Solution Approach 1:
The system dynamically adjusts refueling recommendations based on real-time environmental conditions, vehicle state, and historical data. The refueling score changes dynamically as environmental factors (ozone levels, air quality) and vehicle parameters (coolant temperature, fuel level) vary, allowing the system to adapt to changing conditions rather than using fixed refueling rules
Solution Approach 2:
The system incorporates feedback loops where environmental sensor data, vehicle operational data, and historical refueling patterns are continuously monitored and fed back into the decision-making algorithm. This feedback mechanism allows the system to learn from past refueling outcomes and adjust future recommendations to balance flexibility with environmental constraints
2Productivity
If the vehicle collects and processes multiple data factors to determine refueling, then refueling optimization is improved, but system complexity worsens
Solution Approach 1:
The system segments the complex decision-making process into distinct evaluable factors: environmental conditions (ozone levels, air quality), vehicle state (coolant temperature, fuel level), historical patterns (time of use, previous refueling), and external conditions (traffic congestion). Each factor is processed independently and then aggregated into an overall refueling score, making the complex system more manageable and interpretable
Solution Approach 2:
The computer system performs multiple functions: it monitors environmental conditions, tracks vehicle operational parameters, analyzes historical refueling patterns, processes real-time sensor data, and generates optimized refueling recommendations. This multi-functional approach consolidates various data collection and analysis tasks into a single integrated system rather than requiring separate systems for each function
3Object-affected harmful factors
If the vehicle refuels at constrained times and locations, then environmental impact is reduced, but refueling opportunity worsens
Solution Approach 1:
The system performs preliminary analysis of environmental conditions, vehicle state, and historical patterns before making refueling recommendations. By pre-processing this data and calculating refueling scores in advance, the system can identify optimal refueling opportunities that satisfy environmental constraints without causing last-minute delays or forcing the vehicle to travel long distances to suitable locations
Data Source
AI summary
Data are collected on at least one of a coolant temperature of a vehicle, an atmospheric ozone level, and air quality. A refueling time is determined based on the collected data. The vehicle is moved to a fueling station based on the refueling time.