Battery Cluster Fire Detection Using Multi-Sensor Verification
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Solution Overview
Problem
Current fire-protection solutions for container-type energy storage systems face challenges in accurately identifying abnormal lithium-ion batteries and effectively controlling fires, leading to potential safety hazards and inefficiencies in fire management.
Innovation Solution
A fire-protection detecting method and device that utilize a combination of aspirating detectors, infrared temperature detectors, and camera detectors to monitor lithium-ion batteries, analyze data, and trigger fire-protection measures, including extinguishing systems and alerting terminal devices, while continuously assessing the need for system shutdown based on temperature and cooling rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of detectors (aspirating, infrared, camera) are used to monitor lithium-ion batteries, then the accuracy of fire identification is improved, but the device complexity increases
Solution Approach 1:
The detection system is segmented into three independent detector types (aspirating detector for smoke particles, infrared temperature detector for heat, camera detector for visual confirmation), each targeting specific fire characteristics. This segmentation allows the system to maintain high measurement precision through multi-parameter detection while managing device complexity by modularizing the detection architecture.
Solution Approach 2:
The patent merges three different detection technologies (aspirating, infrared, camera) into a unified fire detection system. By combining these detectors that operate on different physical principles and detect different fire parameters, the system achieves comprehensive fire identification accuracy while the integrated control logic manages the overall system complexity.
2Reliability
If continuous monitoring and multiple detection methods are implemented, then the reliability of fire detection is improved, but the energy consumption increases
Solution Approach 1:
The system implements periodic sampling of detection data from all three detector types rather than continuous monitoring. The control logic processes data at predetermined time intervals, which maintains fire detection reliability by capturing fire development stages while significantly reducing energy consumption compared to continuous operation of all detectors.
Solution Approach 2:
The control logic analyzes detection data and provides feedback to adjust monitoring intensity. When fire risk is detected (abnormal temperature rise, smoke particles, or visual indicators), the system increases monitoring frequency and activates fire suppression. When no risk is detected, monitoring operates at lower intensity, optimizing the balance between reliability and energy consumption.
3Reliability
If fire suppression is activated based on multiple detection criteria, then the effectiveness of fire control is improved, but the response time may be delayed
Solution Approach 1:
The system performs preliminary analysis of detection data using multiple criteria (temperature thresholds, smoke particle concentration, visual fire indicators) before activating fire suppression. This preliminary multi-parameter verification ensures fire control effectiveness by confirming actual fire conditions rather than false alarms, while the control logic is designed to trigger suppression rapidly once criteria are met to minimize response time delay.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy and efficiency of fire identification and control in lithium-ion battery clusters, reducing the risk of harm and optimizing the use of fire-extinguishing materials by determining when fires have been successfully extinguished and when to shut down protection systems.
Implementation Method 1
obtaining first sampling data from the infrared temperature detector, second sampling data from the at least one camera detector, and third sampling data from the aspirating detector; determining a first sampling temperature based on the first sampling data
Implementation Method 2
third sampling data from the aspirating detector; determining whether the target lithium battery cluster has caught fire based on the second sampling data from the at least one camera detector and/or the third sampling data from the aspirating detector
Data Source
AI summary
The present disclosure provides fire-protection detecting method and device, applied to a container-type energy storage system. The method includes: obtaining first sampling data, second sampling data, and third sampling data; determining a first sampling temperature based on the first sampling data, and determining whether a risk event has occurred in the container-type energy storage system based on the first sampling temperature; if it is determined that the risk event has occurred, determining whether the target lithium battery cluster has caught fire based on the second sampling data and/or the third sampling data; if it is determined that the target lithium battery cluster has caught fire, calling the fire-protection system to extinguish fire and sending a first prompt message to the terminal device to prompt that the target lithium battery cluster has caught fire; determining whether to shut down the fire-protection system based on the updated first sampling data.