Battery State Diagnosis Data Extraction via Threshold Filtering
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Solution Overview
Problem
The existing technologies for estimating the state of a battery installed in vehicles result in high computing costs and processing delays due to the large amount of data collected from multiple vehicles, making it challenging to efficiently process and diagnose battery state information.
Innovation Solution
An information processing device that acquires and judges state information from batteries, extracting relevant data as subject information by setting threshold values for the number of start times and activated time periods, and using a learned model to determine the condition of the battery, thereby reducing computing costs and focusing on data that requires diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state information from multiple vehicles is collected for battery state estimation, then the accuracy of battery diagnosis is improved, but the computing costs and processing time increase significantly
Solution Approach 1:
The patent extracts only the necessary state information from the collected data by setting specific conditions (number of start times ≥ first threshold value and activated time period ≥ second threshold value). This extraction approach maintains diagnostic accuracy by focusing on relevant data while discarding unnecessary information, thereby reducing computing costs for processing.
2Measurement precision
If state information from multiple vehicles is collected for battery state estimation, then the accuracy of battery diagnosis is improved, but the processing time increases significantly
Solution Approach 1:
The patent extracts only the necessary state information from the collected data by setting specific conditions (number of start times ≥ first threshold value and activated time period ≥ second threshold value). This extraction approach maintains diagnostic accuracy by focusing on relevant data while discarding unnecessary information, thereby reducing processing time.
3Reliability
If all acquired state information is processed for battery diagnosis, then comprehensive diagnosis coverage is achieved, but computing costs increase
Solution Approach 1:
The patent extracts only the necessary state information from the collected data by setting specific conditions (number of start times ≥ first threshold value and activated time period ≥ second threshold value). This extraction approach maintains comprehensive diagnosis coverage for relevant cases while discarding unnecessary information, thereby reducing computing costs for processing.
4Use of energy by stationary object
If threshold values for start times and activated time period are set to filter state information, then computing costs are reduced, but the risk of excluding relevant data increases
Solution Approach 1:
The patent changes the parameters of state information by setting specific threshold values (first threshold value for number of start times, second threshold value for activated time period). These parameter thresholds enable systematic filtering of data, reducing computing costs while maintaining reliability through statistically meaningful cutoff points that capture relevant battery degradation patterns.
Data Source
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AI summary
An information processing device has: an acquiring section acquiring state information relating to a state of a battery installed in a vehicle; a judging section judging whether or not the acquired state information satisfies a predetermined condition relating to the battery; and an outputting section that, in a case in which the state information satisfies the predetermined condition, outputs the state information as a subject for diagnosing the battery.