Alternative Energy Vehicle Supply Station Network Planning
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Solution Overview
Problem
Creating and operating an alternative energy vehicle supply station network that meets the needs of a specific community is challenging, particularly when approached from the top down.
Innovation Solution
A computer-implemented method and system that allows users to select potential alternative energy vehicle supply stations based on preferences, processes these submissions using preselected criteria, and outputs optimal locations for station construction, utilizing a graphical user interface and mathematical models to determine the most suitable sites for building alternative energy vehicle supply stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a top-down approach is used to create alternative energy vehicle supply station network, then network coverage can be established, but it fails to meet the specific needs of local communities and users
Solution Approach 1:
The patent inverts the traditional top-down network planning approach by implementing a bottom-up methodology where user preferences and local community needs drive the selection and placement of supply stations. Users directly indicate their preferred locations and requirements, which are then processed through optimization algorithms to determine the optimal network configuration.
Solution Approach 2:
The system enables users to self-serve by allowing them to directly submit their preferences for supply station locations and requirements. This user-driven approach eliminates the need for complex centralized planning while ensuring that the network adapts to actual user needs and local conditions.
2Measurement precision
If user preferences are collected and processed through optimization algorithms, then the network meets user demand accurately, but the computational processing requirements increase
Solution Approach 1:
The patent extracts the essential user preferences and requirements from complex input data, focusing only on the critical parameters needed for optimization. By separating essential from non-essential data elements, the system achieves high precision in meeting user demand while reducing computational burden.
Solution Approach 2:
The optimization algorithms process user preferences by transforming and adjusting parameters to find optimal solutions. The system dynamically changes parameters such as station locations, capacities, and configurations to maximize user satisfaction while managing computational resources efficiently.
Data Source
AI summary
A computer implemented method for creating an alternative energy vehicle supply station network is provided. The method includes displaying selectable potential alternative energy vehicle supply stations to users via a graphical user interface displayed on remote devices, the potential alternative energy vehicle supply stations being selectable by each of the users of remote devices to submit at least one preference of each user as a user submission; updating a memory of a computer to include the user submissions; processing, by a processor of the computer, the user submissions as a function of preselected criteria; and outputting, by the computer, the potential alternative energy vehicle supply stations satisfying the preselected criteria.