Vehicle Charging Location Planning Using Fleet Charge-Level Data
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Solution Overview
Problem
Existing systems lack an efficient method to determine optimal vehicle charging locations based on the charge levels of multiple vehicles across different charging stations.
Innovation Solution
A system that determines a new charging location by analyzing the charge levels of a plurality of vehicles at existing charging locations, using a server to process data from vehicles and charging stations to identify areas where additional charging infrastructure is needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging locations are determined based on vehicle charge levels, then charging infrastructure optimization is improved, but system complexity increases
Solution Approach 1:
A server acts as an intermediary between vehicles and charging locations. The server receives charge level data from multiple vehicles, processes this information centrally, and determines optimal charging location recommendations. This intermediary approach optimizes infrastructure placement without requiring direct complex interactions between vehicles and charging stations.
Solution Approach 2:
The system implements feedback by continuously monitoring vehicle charge levels at different charging locations and using this data to refine charging location recommendations. The server analyzes charge level patterns and adjusts charging infrastructure optimization strategies based on observed vehicle behavior and charge level trends.
2Measurement precision
If multiple vehicle charge levels are analyzed, then charging location accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential data elements needed for charging location determination from vehicle data. Specifically, it focuses on charge level information and vehicle location data, filtering out unnecessary vehicle operational details. This extraction approach maintains charging location accuracy while reducing overall data processing requirements.
Solution Approach 2:
The data processing is segmented into distinct stages: data collection from vehicles, central processing at the server to determine charge level patterns, and generation of charging location recommendations. This segmentation allows efficient handling of multiple vehicle data streams by dividing the processing task into manageable segments.
Data Source
AI summary
An example operation includes one or more of determining a plurality of vehicles and a first charge level of the vehicles utilizing a first charging location, determining a portion of the plurality of vehicles and a second charge level of the portion of the plurality of vehicles utilizing a second charging location, and determining a new charging location based on the plurality of vehicles, the first charge level, the portion of the plurality of vehicles, and the second charge level.