Abnormal Sensor Troubleshooting in Energy Storage Systems
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Solution Overview
Problem
Existing methods for determining abnormal sensors in energy-storage apparatuses are inaccurate due to sensor failures, which can lead to incorrect assessments of battery conditions such as thermal runaway or electrolyte leakage, affecting safety and reliability.
Innovation Solution
A method that receives feedback information from multiple sensors, groups monitoring data by type, selects data sets based on spatial distance, determines compensation coefficients, and performs data screening to identify target sensors in fault states, using distance attenuation models and historical data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is used directly for determining battery abnormal conditions, then the determination process is simple, but the accuracy is greatly affected when sensors fail
Solution Approach 1:
The patent segments the sensor data processing into multiple stages: data collection from multiple sensors, data grouping by monitoring type, data inspection to identify abnormal sensors, and finally determination of battery conditions. This segmentation allows systematic handling of sensor failures while maintaining determination accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where sensor data is continuously monitored and compared against expected patterns. When sensor readings deviate from normal ranges or show inconsistent patterns, the system identifies these as potential sensor failures and adjusts the determination process accordingly, using feedback from multiple sensors to validate each other's readings.
2Reliability
If multiple sensors are used for monitoring, then the accuracy of determining battery conditions improves, but the complexity of identifying faulty sensors increases
Solution Approach 1:
The patent merges data from multiple sensors of the same type into groups, comparing their readings collectively. By combining sensor data and analyzing patterns across the group, the system can identify which sensors are malfunctioning based on deviations from the group norm, thereby reducing the difficulty of detecting faulty sensors while maintaining high reliability.
Solution Approach 2:
The patent changes the parameter of analysis from individual sensor readings to comparative relationships between sensors. By examining parameters such as the difference between sensor readings, the consistency of readings over time, and the spatial distribution of abnormal readings, the system can identify faulty sensors through parameter changes rather than direct observation.
3Productivity
If sensor failure is not detected, then the monitoring system operates continuously, but incorrect determination of battery conditions occurs
Solution Approach 1:
The patent performs preliminary data inspection and sensor validation before using sensor readings to determine battery conditions. By pre-identifying and flagging potentially faulty sensors through pattern recognition and consistency checks, the system ensures that only reliable sensor data is used in the final determination, preventing incorrect assessments while maintaining continuous operation.
Solution Approach 2:
The patent implements preliminary anti-action by proactively detecting and compensating for sensor failures before they lead to incorrect battery condition determinations. The system anticipates potential errors by monitoring sensor performance metrics and takes corrective action by excluding faulty sensor data or adjusting the analysis methodology, thereby preventing the harmful effect of incorrect determinations.
Data Source
AI summary
A method for troubleshooting an abnormal sensor in an energy-storage apparatus, a terminal device, and storage medium are provided. The method may include the following. Feedback information sent by at least two sensors of at least one type is received according a preset period. Monitoring data are grouped according to a monitoring type. One monitoring type corresponds to one data set. Data screening is performed on a data inspecting group updated, and a target sensor is determined.