Charging Station Cluster Analysis for Defect Detection
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Solution Overview
Problem
Existing methods for determining defective charging stations for battery-powered vehicles are not reliable, as they may incorrectly record their status, leading to user discomfort and inefficient charging processes.
Innovation Solution
A method where charging stations transmit usage data to a central computing unit, which assigns them to clusters based on geographical proximity. The unit determines a malfunction if at least one usage parameter is outside a set target range, derived from a reference charging station within the cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If charging stations transmit usage data to a central computing unit for analysis, then the accuracy of detecting defective charging stations is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments charging stations into geographic clusters and divides detection tasks by comparing each station against its cluster peers rather than analyzing all stations centrally. This distributes the computational burden and reduces the complexity of the central computing unit while maintaining detection accuracy through localized comparative analysis.
Solution Approach 2:
The patent introduces a layered architecture where regional cluster analysis acts as an intermediary between individual charging stations and the central computing unit. Cluster-level processing filters and pre-analyzes data locally, reducing the volume and complexity of data requiring central processing while preserving detection accuracy.
2Device complexity
If charging stations are monitored individually using existing methods, then the implementation is simpler, but the reliability of defect detection deteriorates due to incorrect status recording
Solution Approach 1:
The patent merges the detection capabilities of multiple charging stations by analyzing usage data across entire clusters rather than evaluating stations in isolation. This collective analysis approach compensates for individual station measurement errors and improves reliability while maintaining manageable system complexity through standardized cluster-based processing.
Solution Approach 2:
The system implements feedback mechanisms where cluster analysis results continuously refine the detection criteria for individual stations. By comparing each station's performance against cluster averages and deviations, the system creates a self-correcting detection mechanism that improves reliability over time while using simple, repeatable analysis procedures.
3Device complexity
If manual reporting of defective charging stations is required, then the system complexity is reduced, but the loss of time and user convenience deteriorate
Solution Approach 1:
The system enables self-service defect detection by automatically analyzing usage data from charging stations and identifying malfunctions without requiring manual user reporting. The automated cluster-based analysis continuously monitors station performance and flags defects proactively, eliminating the time users would spend manually reporting issues while using straightforward automated comparison algorithms.
Data Source
AI summary
Defective charging stations for battery-powered vehicles are identified by collecting usage data and transmitting the usage data to a central computing unit. The central computing unit analyzes the usage data, after which the central computing unit determines a malfunction of at least one charging station if at least one usage parameter of a charging station comprised by the usage data is inside or outside of a set target range. At least two charging stations, stationed in a set geographical area, of one or more charging station networks in a set geographical area, are assigned to a common charging station cluster and respectively the target range of the individual usage parameters is derived from the usage data of at least one charging station, classified as a reference charging station, of the charging station cluster.