Electrochemical energy storage system safety assessment method fusing multi-sensor data

Abstract

The invention discloses an electrochemical energy storage system safety assessment method fusing multi-sensor data, and relates to the technical field of electrochemical energy storage safety monitoring, and the method comprises the following steps: S1, multi-modal data acquisition; s2, data preprocessing; s3, multi-sensing data fusion: carrying out weighted feature fusion on the standardized data obtained in the step S2 through a multi-sensing fusion algorithm built in the microcontroller, and outputting a fusion feature value; s4, evaluating a safety state; and S5, performing early warning alarm and communication. The gas sensor array, the high-precision temperature sensor group, the current and voltage Hall sensor and other sensing devices are arranged, multi-dimensional data acquisition of the operation state of the electrochemical energy storage system is achieved, synchronous acquisition and fusion analysis of multi-source data are matched, and a complete energy storage system safety monitoring system is constructed. A monitoring blind area existing in a single sensor is effectively overcome, and early warning and accurate recognition of potential risks such as thermal runaway and overcharge and overdischarge are achieved.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage safety monitoring technology, specifically to a method for safety assessment of electrochemical energy storage systems that integrates data from multiple sensors. Background Technology

[0002] Electrochemical energy storage systems are widely used in new energy power generation and grid peak shaving due to their advantages such as high energy density and fast response speed. However, electrochemical energy storage systems are prone to safety accidents during charging and discharging due to problems such as overcharging, over-discharging, thermal runaway, and battery aging. The evolution of these faults is accompanied by characteristics such as gas leakage, temperature rise, and abnormal voltage and current.

[0003] In complex environments, single sensors are susceptible to electromagnetic interference, temperature and humidity fluctuations, and other factors, which can lead to significant deviations in measurement data and result in frequent false alarms and missed alarms during system safety assessments. Summary of the Invention

[0004] The purpose of this invention is to provide a safety assessment method for electrochemical energy storage systems that integrates multi-sensor data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A safety assessment method for electrochemical energy storage systems that integrates multi-sensor data includes the following steps: S1: Multimodal data acquisition: Using an STM32F103VET6 microcontroller as the control core, driving the CO sensor, VOC gas sensor, Gas sensors, temperature sensors, and voltage and current sensors synchronously collect gas concentration data, temperature data, and voltage and current data of the charging and discharging circuit of the electrochemical energy storage system according to a preset sampling frequency. S2: Data preprocessing: The STM32F103VET6 microcontroller performs filtering, noise reduction, outlier removal, and standardization on the acquired multimodal raw data to obtain standardized data; S3: Multi-sensor data fusion: By utilizing the multi-sensor fusion algorithm built into the microcontroller and calculating the variance contribution rate of each sensor's data in real time, it solves the fusion distortion problem of traditional fixed weights when SOC ≥ 80% or ≤ 20%. Step 1: Calculate the measurement error variance of each sensor: Based on the deviation between the real-time data collected by the sensors and the historical benchmark database, calculate the measurement error variance σᵢ² for each sensor; Step 2: Dynamically allocate weight coefficients: Calculate the weight coefficients of each sensor based on the error variance, following the principle of "the smaller the error, the greater the weight"; Step 3: Preliminary fusion using weighted summation: Using the dynamic weighting coefficients mentioned above, perform a weighted summation operation on the standardized data obtained in step S2 to obtain the preliminary fusion result. Step 4: Principal Component Extraction and Optimization: Principal Component Analysis (PCA) is used to extract features from the preliminary fusion results, remove redundant information, retain key features, and finally obtain the fusion feature values. S4: Safety Status Assessment: A safety assessment index system for the electrochemical energy storage system is preset, including gas concentration threshold range, temperature threshold range, and rated voltage and current range. The fused characteristic value in step S3 is compared with the assessment index system to output the safety status level. Based on the fused characteristic value, a risk matrix specific to the energy storage system is adopted, and the safety status is divided into three levels with reference to the nuclear power-grade RMAP standard: A Unacceptable (characteristic value ≥ 80), B Needs Mitigation (40 ≤ characteristic value < 80), and C Acceptable (characteristic value < 40). Among them, level A corresponds to the precursor of cell thermal runaway, and level B corresponds to electrolyte leakage / local overheating. S5: Early Warning and Communication: Based on the safety status level in step S4, trigger the corresponding audible and visual early warning / alarm signal, and upload real-time status data, fused feature values ​​and safety level information to the monitoring platform through the communication module. The early warning signal communicates with the energy storage BMS in real time through the CAN bus. Level A risk triggers audible and visual alarm + emergency shutdown command, and Level B risk triggers SMS push + maintenance work order generation.

[0006] A further improvement of the technical solution of the present invention is that: in step S1, the preset sampling frequency is 10 to 100 Hz, and the STM32F103VET6 microcontroller establishes communication with each sensor through I²C, SPI or ADC interface to realize synchronous data acquisition.

[0007] A further improvement of the technical solution of the present invention is that, in step S1, for the electrochemical energy storage system in a high temperature and high humidity environment, the sensor selection and adaptation are as follows: the CO sensor adopts the MQ-7H type high temperature resistant electrochemical sensor, the VOC sensor adopts the TGS2600-HT type high humidity adapted semiconductor sensor, and the temperature sensor adopts the DS18B20-PRO type industrial-grade digital sensor.

[0008] A further improvement of the technical solution of the present invention is that: in step S2, the filtering and denoising adopts a moving average filtering algorithm, outlier removal adopts the 3σ criterion, and the standardization process adopts the Z-score standardization formula. ,in This is the original data. The mean of the data. This represents the standard deviation of the data.

[0009] A further improvement to the technical solution of this invention lies in the following: In step S3, the weight coefficients of the improved weighted least squares fusion algorithm are calculated as follows: ,in Let be the weighting coefficient of the i-th sensor. Let be the measurement error variance of the i-th sensor, and n be the number of sensors.

[0010] A further improvement of the technical solution of the present invention is that: in step S3, the multi-sensor fusion algorithm further includes a sensor real-time reliability calibration step: every 5 to 15 minutes, the STM32F103VET6 microcontroller calls the built-in historical benchmark database to calculate the deviation between the current measurement value of each sensor and the corresponding benchmark value.

[0011] A further improvement to the technical solution of this invention lies in the following: In step S4, the safety assessment index system is determined by: combining the rated operating parameters of the electrochemical energy storage system, the fault evolution law, and industry safety standards, a safety assessment index system based on the analytic hierarchy process is preset, and the fused feature values ​​are compared with preset threshold ranges: If the fused feature value falls within the normal level range, the system is considered to be operating normally; If the value falls within the warning level range, the system is deemed to have potential security risks. If the alarm level falls within the range, the system is considered to have malfunctioned. The evaluation cycle is ≤100ms, enabling real-time dynamic evaluation.

[0012] A further improvement of the technical solution of the present invention is that: in step S4, the security status assessment adopts a real-time dynamic update mechanism: each time a set of data is collected, a fusion and assessment is performed once, and the assessment cycle is ≤100ms.

[0013] A further improvement of the technical solution of the present invention is that: in step S5, the communication module is an RS485, LoRa or Ethernet module, and the communication protocol adopts Modbus-RTU or MQTT to realize wired / wireless remote transmission of data.

[0014] A further improvement to the technical solution of this invention lies in the following: In step S5, the early warning alarm strategy is linked to the energy storage system's operating mode: (1) In charging mode: If an alarm is triggered, the charging current will be reduced to 50% of the rated value and maintained; if an alarm is triggered, the charging circuit will be cut off immediately and the module cooling fan will be started at the same time. (2) In discharge mode: if an early warning is triggered, the current discharge current is maintained but the duration of continuous discharge is limited to ≤30min; if an alarm is triggered, the discharge circuit is cut off and the power supply is switched to the backup power supply. (3) In standby mode: If a warning / alarm is triggered, in addition to the sound and light prompts, a "device wake-up request" is sent to the monitoring platform through the communication module. After the platform confirms the request, the protection action is executed.

[0015] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: 1. This invention provides a safety assessment method for electrochemical energy storage systems that integrates multi-sensor data. By deploying various sensing devices such as gas sensor arrays, high-precision temperature sensor groups, and current and voltage Hall sensors, it achieves multi-dimensional data acquisition of the operating status of the electrochemical energy storage system. Combined with the synchronous acquisition and fusion analysis of multi-source data, a complete safety monitoring system for the energy storage system is constructed, effectively overcoming the monitoring blind spots of single sensors and realizing early warning and accurate identification of potential risks such as thermal runaway and overcharging / over-discharging.

[0016] 2. This invention provides a safety assessment method for electrochemical energy storage systems that integrates multi-sensor data. The safety assessment platform supports multiple alarm modes, including local audible and visual alarms, SMS alerts, and remote cloud platform monitoring. Through an intelligent linkage mechanism, when abnormalities such as over-temperature or over-voltage of the battery pack are detected locally, the system immediately triggers the audible and visual alarm device and simultaneously pushes the alarm information to the mobile terminals of maintenance personnel and the remote monitoring center. This addresses the needs of different scenarios such as industrial and commercial energy storage and residential energy storage. Whether it is a distributed energy storage system that is sensitive to the environment or a centralized energy storage power station with extremely high real-time requirements, it can provide stable and reliable safety assessment and early warning services, greatly improving the application scope and practicality of the system.

[0017] 3. This invention provides a safety assessment method for electrochemical energy storage systems that integrates multi-sensor data. It innovatively employs an improved weighted least squares fusion algorithm, deeply coupled with principal component analysis feature optimization technology, and dynamically adjusts the weights of each sensor in real time through an adaptive mechanism. This method effectively eliminates measurement error interference, significantly reduces data redundancy, and greatly improves the accuracy and reliability of safety assessment. Actual measurements have verified that the false alarm rate can be reduced by more than 30%. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 This is a flowchart of the multi-sensor data fusion steps of the present invention; Figure 3 This is a flowchart illustrating the comparison between the fused feature values ​​and the preset threshold range of the present invention. Figure 4 This is a flowchart of the testing and verification process for the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to embodiments: The present invention has the following three specific embodiments.

[0020] Example 1 Hardware configuration Core controller: STM32F103VET6; Sensor modules: CO sensor, VOC sensor, H2 sensor, temperature sensor, voltage and current sensor; Communication module: LoRa module; Alarm module: red and green dual-color LED light, buzzer, 5V relay.

[0021] Example 2 Software Implementation Process Initialization configuration: Initialize the STM32F103VET6 I²C, ADC, single bus and UART interfaces, set the sensor sampling frequency to 50Hz, and initialize the fusion algorithm parameters and security threshold; Data acquisition: The microcontroller periodically sends acquisition commands to each sensor, reads the raw data, and stores it; Data preprocessing: Perform moving average filtering, 3σ outlier removal, and Z-score standardization; Data fusion: Calculate the error variance of each sensor, assign weight coefficients, and after weighted fusion, extract three principal components through PCA to obtain fusion feature values; Security assessment: The fused feature values ​​are compared with preset thresholds to output the security level; Early warning communication: Drives the alarm module to upload data to the monitoring platform via the LoRa module.

[0022] Example 3 Test and verification Verification was conducted on a lithium battery energy storage module testing platform. Normal state: All sensor data are within the normal threshold, the fusion feature value is 0.3, and the evaluation result is "normal"; Warning status: Simulates mild thermal runaway, temperature rises to 50℃, CO concentration is 15ppm, fusion characteristic value is 0.7, triggering green audible and visual warning; Alarm status: Simulates severe thermal runaway, temperature rises to 65℃, H2 concentration is 120ppm, fusion characteristic value is 1.2, triggers red audible and visual alarm + relay circuit cut-off, data upload delay ≤50ms. Test results show that this method can accurately identify different safety states, provide timely warnings, and has a false alarm rate of ≤5%, meeting the safety monitoring requirements of electrochemical energy storage systems.

[0023] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A safety assessment method for an electrochemical energy storage system that integrates multi-sensor data, characterized in that: Includes the following steps: S1: Multimodal data acquisition: Using an STM32F103VET6 microcontroller as the control core, driving the CO sensor, VOC gas sensor, Gas sensors, temperature sensors, and voltage and current sensors synchronously collect gas concentration data, temperature data, and voltage and current data of the charging and discharging circuit of the electrochemical energy storage system according to a preset sampling frequency. S2: Data preprocessing: The STM32F103VET6 microcontroller performs filtering, noise reduction, outlier removal, and standardization on the acquired multimodal raw data to obtain standardized data; S3: Multi-sensor data fusion: By utilizing the multi-sensor fusion algorithm built into the microcontroller and calculating the variance contribution rate of each sensor's data in real time, it solves the fusion distortion problem of traditional fixed weights when SOC ≥ 80% or ≤ 20%. Step 1: Calculate the measurement error variance of each sensor: Based on the deviation between the real-time data collected by the sensors and the historical benchmark database, calculate the measurement error variance σᵢ² for each sensor; Step 2: Dynamically allocate weight coefficients: Calculate the weight coefficients of each sensor based on the error variance, following the principle of "the smaller the error, the greater the weight"; Step 3: Preliminary fusion using weighted summation: Using the dynamic weighting coefficients mentioned above, perform a weighted summation operation on the standardized data obtained in step S2 to obtain the preliminary fusion result. Step 4: Principal Component Extraction and Optimization: Principal Component Analysis (PCA) is used to extract features from the preliminary fusion results, remove redundant information, retain key features, and finally obtain the fusion feature values. S4: Safety Status Assessment: A safety assessment index system for the electrochemical energy storage system is preset, including gas concentration threshold range, temperature threshold range, and rated voltage and current range. The fused characteristic value in step S3 is compared with the assessment index system to output the safety status level. Based on the fused characteristic value, a risk matrix specific to the energy storage system is adopted, and the safety status is divided into three levels with reference to the nuclear power-grade RMAP standard: A Unacceptable (characteristic value ≥ 80), B Needs Mitigation (40 ≤ characteristic value < 80), and C Acceptable (characteristic value < 40). Among them, level A corresponds to the precursor of cell thermal runaway, and level B corresponds to electrolyte leakage / local overheating. S5: Early Warning and Communication: Based on the safety status level in step S4, trigger the corresponding audible and visual early warning / alarm signal, and upload real-time status data, fused feature values ​​and safety level information to the monitoring platform through the communication module. The early warning signal communicates with the energy storage BMS in real time through the CAN bus. Level A risk triggers audible and visual alarm + emergency shutdown command, and Level B risk triggers SMS push + maintenance work order generation.

2. The method for safety assessment of an electrochemical energy storage system integrating multi-sensor data according to claim 1, characterized in that: In step S1, the preset sampling frequency is 10-100Hz. The STM32F103VET6 microcontroller establishes communication with each sensor through I²C, SPI or ADC interfaces to achieve synchronous data acquisition.

3. The method for safety assessment of an electrochemical energy storage system that integrates multi-sensor data according to claim 1, characterized in that: In step S1, for the electrochemical energy storage system in a high temperature and high humidity environment, the sensor selection and adaptation are as follows: the CO sensor adopts the MQ-7H high temperature resistant electrochemical sensor, the VOC sensor adopts the TGS2600-HT high humidity adapted semiconductor sensor, and the temperature sensor adopts the DS18B20-PRO industrial-grade digital sensor.

4. The method for safety assessment of an electrochemical energy storage system that integrates multi-sensor data according to claim 1, characterized in that: In step S2, the filtering and denoising uses a moving average filtering algorithm, outlier removal uses the 3σ criterion, and standardization uses the Z-score standardization formula. ,in The original data, The mean of the data. This represents the standard deviation of the data.

5. The method for safety assessment of an electrochemical energy storage system integrating multi-sensor data according to claim 1, characterized in that: In step S3, the weight coefficients of the improved weighted least squares fusion algorithm are calculated as follows: ,in Let be the weighting coefficient of the i-th sensor. Let be the measurement error variance of the i-th sensor, and n be the number of sensors.

6. The method for safety assessment of an electrochemical energy storage system that integrates multi-sensor data according to claim 1, characterized in that: In step S3, the multi-sensor fusion algorithm further includes a real-time sensor reliability calibration step: every 5 to 15 minutes, the STM32F103VET6 microcontroller calls the built-in historical benchmark database to calculate the deviation between the current measurement value of each sensor and the corresponding benchmark value.

7. The method for safety assessment of an electrochemical energy storage system integrating multi-sensor data according to claim 1, characterized in that: In step S4, the safety assessment index system is determined as follows: Combining the rated operating parameters of the electrochemical energy storage system, fault evolution patterns, and industry safety standards, a safety assessment index system based on the analytic hierarchy process (AHP) is pre-defined, and the fused characteristic values ​​are compared with a pre-defined threshold range. If the fused feature value falls within the normal level range, the system is considered to be operating normally; If the value falls within the warning level range, the system is deemed to have potential security risks. If the alarm level falls within the range, the system is considered to have malfunctioned. The evaluation cycle is ≤100ms, enabling real-time dynamic evaluation.

8. The method for safety assessment of an electrochemical energy storage system that integrates multi-sensor data according to claim 1, characterized in that: In step S4, the security status assessment adopts a real-time dynamic update mechanism: each time a set of data is collected, a fusion and assessment is performed, and the assessment cycle is ≤100ms.

9. The method for safety assessment of an electrochemical energy storage system integrating multi-sensor data according to claim 1, characterized in that: In step S5, the communication module is an RS485, LoRa, or Ethernet module, and the communication protocol adopts Modbus-RTU or MQTT to realize wired / wireless remote data transmission.

10. The method for safety assessment of an electrochemical energy storage system integrating multi-sensor data according to claim 1, characterized in that: In step S5, the early warning and alarm strategy is linked to the energy storage system's operating mode: (1) In charging mode: If an alarm is triggered, the charging current will be reduced to 50% of the rated value and maintained; if an alarm is triggered, the charging circuit will be cut off immediately and the module cooling fan will be started at the same time. (2) In discharge mode: if an early warning is triggered, the current discharge current is maintained but the duration of continuous discharge is limited to ≤30min; if an alarm is triggered, the discharge circuit is cut off and the power supply is switched to the backup power supply. (3) In standby mode: If a warning / alarm is triggered, in addition to the sound and light prompts, a "device wake-up request" is sent to the monitoring platform through the communication module. After the platform confirms the request, the protection action is executed.

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