Lithium ion battery thermal runaway early warning method based on multi-parameter monitoring

Abstract

The invention relates to a lithium ion battery thermal runaway early warning method based on multi-parameter monitoring, and aims to early recognize and prevent the thermal runaway risk of a lithium ion battery. According to the method, a multi-level early warning characteristic parameter system is formed by constructing a combined early warning mechanism and combining statistical analysis and an electrochemical model based on dynamic monitoring of basic parameters such as voltage, current and temperature. According to the method, the early warning boundary can be dynamically adjusted based on the multi-parameter synergistic effect, the accuracy and adaptability of battery thermal runaway early warning are remarkably improved, effective guarantee is provided for safe operation of the battery, and the method is suitable for various application scenes such as power batteries and energy storage batteries.

Description

Technical Field

[0001] The present invention relates to a method for warning thermal runaway of lithium-ion batteries based on multi-parameter monitoring, belonging to the technical field of safety monitoring and warning of lithium-ion batteries. Background Art

[0002] Due to their superior energy density, long cycle life, and low self-discharge rate, lithium-ion batteries are widely used in portable electronic devices, electric vehicles, and renewable energy storage. However, lithium-ion batteries are prone to thermal runaway under extreme conditions such as high temperature, overcharging, and internal short circuit, resulting in overheating, fire, or even explosion of the battery. These safety hazards have attracted extensive attention to the safety of lithium-ion batteries, and there is an urgent need for effective thermal runaway warning technology to ensure the safe use of the battery.

[0003] Currently, the warning methods for thermal runaway of lithium-ion batteries mainly include the following:

[0004] Single-parameter monitoring: Many existing technologies only rely on the monitoring of a single parameter, such as temperature, cell voltage, or current. These methods usually set thresholds and issue alarms once the monitored value exceeds the threshold. However, the monitoring of a single parameter is easily affected by noise and environmental factors, which may lead to false alarms or missed alarms.

[0005] Model-based prediction: Some researchers use the electrochemical model of the battery for thermal runaway prediction. These models are based on complex mathematical equations and can provide a certain degree of accuracy, but their calculation process is complex and the real-time performance is poor, making it difficult to apply to rapidly changing actual situations.

[0006] Data-driven methods: In recent years, with the development of machine learning and big data technologies, data-driven methods have gradually emerged. These methods can identify potential thermal runaway risks through learning historical data. However, these methods still require a large amount of data support and may have the problem of model overfitting.

[0007] Although existing technologies have made progress in warning thermal runaway of lithium-ion batteries, there are still some deficiencies, such as insufficient accuracy, poor real-time performance, and insufficient comprehensive analysis of multiple factors. Therefore, there is an urgent need for a warning method that can comprehensively consider multiple parameters.

[0008] After retrieval, a Chinese patent with the patent number 2021111691677 discloses a method for detecting and locating thermal anomalies in cylindrical lithium-ion batteries, including: establishing a normalized temperature spatial distribution model when disturbances and thermal anomalies exist; identifying the parameters of the normalized temperature spatial distribution model according to a preset optimization objective function; collecting battery surface temperature data, battery current and terminal voltage data, and solving the error system by the successive approximation method to obtain the output temperature estimation error; it can obtain a distributed residual quantity for the output temperature estimation error, substitute it into a preset residual evaluation function to obtain an evaluation value, and when the evaluation value is greater than a preset threshold, locate that there is a thermal anomaly at the spatial position. However, the warning method of this patent is single and the algorithm is complex, resulting in a high probability of missed judgment. Summary of the Invention

[0009] In order to solve the above existing problems, the present invention discloses a method for warning thermal runaway of lithium-ion batteries based on multi-parameter monitoring, and its specific technical solution is as follows:

[0010] A method for warning thermal runaway of lithium-ion batteries based on multi-parameter monitoring includes the following steps:

[0011] Step 1. Collect data of the voltage V(t), current I(t) and temperature T(t) of a single battery cell, and monitor its abnormal values and / or abnormal growth rates to generate the first type of warning characteristic parameters;

[0012] Step 2. Conduct data statistical analysis on the voltage of a single battery cell to detect the mean deviation and / or information entropy deviation to generate the second type of warning characteristic parameters;

[0013] Step 3. Based on the battery electrochemical model, identify the electrochemical parameters of a single battery cell by the particle swarm optimization method, and generate the third type of warning characteristic parameters according to the abnormalities of the identified electrochemical parameters;

[0014] Step 4. Once any parameter in any type of warning characteristic parameters in Step 1 to Step 3 is abnormal, immediately initiate a warning.

[0015] Furthermore, before performing the operations of abnormal values and / or abnormal growth rates on the data of the voltage V(t), current I(t) and temperature T(t) of a single battery cell collected in Step 1, preprocess the data of the voltage V(t), current I(t) and temperature T(t). The specific process of the preprocessing is as follows:

[0016] 1.1: Conduct data cleaning, perform denoising and abnormal value processing on the collected data to ensure the quality and accuracy of the data, and remove obvious error data with negative values or outside the reasonable range;

[0017] 1.2: Perform data segmentation. Segment the continuous time series data. The segmentation method is based on time window or event trigger to ensure that each segment of data has similar characteristics.

[0018] Further, the outliers of the voltage V(t), current I(t), and temperature T(t) of the battery cell in step 1 are determined by comparing the measured values with preset thresholds. If the measured values of the voltage V(t), current I(t), and temperature T(t) exceed the preset thresholds, they are determined as outliers.

[0019] Further, the calculation formula of the growth rate in step 1 is as follows:

[0020]

[0021] where y represents the type of collected data, including voltage, current, or temperature, and σ y is the real-time growth rate of data y, y(i) is the real-time value of data type y at the i-th moment, and y(i + 1) is the real-time value of data type y at the (i + 1)-th moment;

[0022] Preset the growth rate thresholds for voltage, current, and temperature. When any one of the real-time growth rates of voltage, current, or temperature exceeds the corresponding preset growth rate threshold of voltage, current, or temperature, it is determined as an abnormal growth rate.

[0023] Further, the calculation formula of the mean deviation in step 2 is as follows:

[0024]

[0025] where V i is the voltage of the battery cell at the i-th moment point, is the mean value of all voltages, and N is the number of data points;

[0026] The calculation formula of information entropy is as follows:

[0027]

[0028] where P(V j ) is the probability distribution of the j-th type of monomer voltage value, k is the number of voltage value categories,

[0029] Based on the statistical analysis of historical data, determine the judgment standard values of the mean deviation and information entropy deviation. When any one of the measured values of the mean deviation or information entropy deviation is greater than the corresponding judgment standard value of the mean deviation or information entropy deviation, generate the second type of early warning characteristic parameter.

[0030] Further, in step 3, select the lithium ion concentration C Li+ and the diaphragm equivalent conductivity σ sepAs an electrochemical parameter to characterize the internal state of the battery, the lithium-ion concentration C Li+ and the equivalent conductivity σ of the separator sep The changes directly reflect the potential fault characteristics of internal short circuit and separator failure in the battery,

[0031] The specific process of electrochemical parameter identification is as follows: Combining the electrochemical model with the particle swarm optimization algorithm for real-time monitoring, by collecting external signals of voltage and current during the operation of the battery, dynamically invert C Li+ and σ sep The change situation; Let the simulated voltage fit the collected voltage, that is, the loss function of parameter identification, calculate the difference between the simulated voltage and the measured voltage. When the difference between the simulated voltage and the measured voltage is the smallest, the value of the objective function reaches the minimum, so as to obtain the electrochemical parameter value in the electrochemical model;

[0032] The particle swarm optimization algorithm takes the minimization of the root mean square error between the simulated voltage and the measured voltage of the lithium-ion battery as the objective function, and iteratively optimizes the model parameters to obtain high-precision real-time C Li+ and σ sep Electrochemical parameters,

[0033]

[0034] where V sim (i) is the simulated voltage at the i-th time point, and V meas (i) is the measured voltage at the i-th time point, and N is the number of data points.

[0035] Furthermore, the process of obtaining the simulated voltage is as follows: During the operation of the battery, the voltage data of the battery cell is collected, and the simulated voltage of the battery is obtained through electrochemical model simulation.

[0036] Furthermore, in the combined warning mechanism of the third type of electrochemical parameters in step 3, based on the monitored lithium-ion concentration C Li+ and the equivalent conductivity σ of the separator sep of the electrochemical parameters, set the combined warning boundary. When the combination of the electrochemical parameters of the lithium-ion concentration C Li+ and the equivalent conductivity σ of the separator sep meets specific conditions, trigger the thermal runaway warning. The specific conditions are:

[0037]

[0038] where is the threshold of the equivalent conductivity of the separator dynamically adjusted according to the lithium-ion concentration. The formula is as follows:

[0039]

[0040] where σ0 is the equivalent conductivity threshold of the base diaphragm, k is an adjustment coefficient used to represent the influence of lithium ion concentration on the diaphragm conductivity, and C ref is the reference lithium ion concentration.

[0041] Furthermore, in order to adapt to the operating environment of high load or low temperature, the combined warning boundary is dynamically adjusted by combining real-time data and historical data. When the battery is in a high-load state for a long time, then reduce Thereby, a warning signal is sent in advance. Once the combined warning condition is triggered, the system will send an alarm to the user, recommending to immediately stop using the battery; automatically record the relevant data triggering the warning, including abnormal parameters and the working condition information at the time of triggering.

[0042] The working principle of the present invention is:

[0043] The present invention realizes effective warning of thermal runaway by collecting voltage, current and temperature data of battery cells in real time and combining the analysis of electrochemical parameters. This method considers multiple key parameters at the same time, provides more comprehensive monitoring information, and reduces the risks of false alarms and missed alarms; through statistical analysis and parameter identification, it can more accurately judge the thermal runaway risk of the battery, improving the accuracy and reliability of the warning; the method of the present invention is applicable to lithium ion batteries of different types and scales, and has broad application prospects.

[0044] The beneficial effects of the present invention are:

[0045] The present invention not only constructs multiple warning characteristic parameters, but also can more accurately judge the thermal runaway risk of the battery through statistical analysis and parameter identification.

[0046] The implementation of the present invention will significantly improve the safety of lithium ion batteries, reduce the occurrence risk of thermal runaway events, prevent the occurrence of thermal runaway events, provide practical technical support for the safe use of batteries, and has important practical significance and application value. Description of the Drawings

[0047] Figure 1 is the flow chart of the method for warning thermal runaway of lithium ion batteries based on multi-parameter monitoring of the present invention. Specific Embodiments

[0048] The following further clarifies the present invention in conjunction with the drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0049] Combined with the attached Figure 1 It can be seen that the specific execution process of the present invention is:

[0050] 1. Data collection: The single-cell voltage, current, and temperature data of the battery are collected through the sensors set inside the battery pack and stored in the database for subsequent analysis.

[0051] 2. Data preprocessing:

[0052] First, data cleaning is performed to denoise and handle outliers in the collected data to ensure the quality and accuracy of the data. For example, obvious incorrect data (such as negative values or data outside the reasonable range) is removed. Then, data segmentation is carried out, segmenting the continuous time-series data so as to conduct subsequent analysis. The segmentation method can be based on time windows or event triggers to ensure that each segment of data has similar characteristics.

[0053] 3. Outlier detection: The first type of warning feature parameters

[0054] Monitor the values of voltage, current, and temperature. The outliers of the voltage V(t), current I(t), and temperature T(t) of the battery single cells are determined by comparing the measured values with the preset thresholds.

[0055] Calculate the abnormal growth rate. When the real-time data value or the growth rate of the data value collected exceeds the set threshold, a warning is triggered. The calculation formula for the growth rate is as follows:

[0056]

[0057] where y represents various types of collected data, including voltage, current, or temperature, and σ y is the growth rate of the data y, y(i) is the real-time value of the data type y at the i-th moment, and y(i + 1) is the real-time value of the data type y at the (i + 1)-th moment.

[0058] 4. Statistical analysis: The second type of warning feature parameters

[0059] Analyze the historical data, calculate the mean deviation and information entropy, generate the second type of warning feature parameters, and evaluate the battery state in real time:

[0060] The calculation formula for the mean deviation is as follows:

[0061]

[0062] where V i is the single-cell voltage of the battery at the i-th moment point, is the mean value of all voltages, and N is the number of data points;

[0063] The calculation formula for the information entropy is as follows:

[0064]

[0065] where P(V j) is the probability distribution of the voltage value of the j-th type of monomer, and k is the number of voltage value categories. When the mean deviation is too large or the information entropy deviation is too large, the second type of early warning characteristic parameter is generated;

[0066] The determination criteria for excessive mean deviation and excessive information entropy deviation are based on the statistical analysis of historical data. Due to the relevance of specific battery pack structures, cell types, etc., there may be different threshold determination criteria. Therefore, it is necessary to formulate the determination criteria according to the historical normal data of the actual battery pack.

[0067] 5. Electrochemical parameter monitoring: The third type of early warning characteristic parameter

[0068] Using an electrochemical model combined with a particle swarm optimization algorithm, the equivalent conductivity σ of the separator and the lithium-ion concentration C of the negative electrode are obtained in real time sep and the lithium-ion concentration C of the negative electrode Li+ . First, the voltage data of the battery monomers are collected during the operation of the battery, and then the voltage of the battery is obtained through electrochemical model simulation. Let the simulated voltage fit the collected voltage (this function is the loss function of parameter identification, which is the difference between the simulated voltage and the measured voltage, and the goal of parameter identification is to minimize the value of this objective function), so as to obtain the electrochemical parameter values in the electrochemical model, which is parameter identification. For the voltage data of the battery monomers, the values of these two electrochemical parameters are obtained through parameter identification, and then it is judged whether they exceed the safety threshold, so as to achieve early warning. The optimization objective function is to minimize the root mean square error between the simulated voltage and the measured voltage:

[0069]

[0070] where, V sim (i) is the simulated voltage at the i-th time point, and V meas (i) is the measured voltage at the i-th time point, and N is the number of data points.

[0071] 6. Combined early warning mechanism:

[0072] In the combined early warning mechanism of the third type of electrochemical parameters, the system sets a combined early warning boundary based on the existing monitored electrochemical parameters, including lithium-ion concentration and separator equivalent conductivity. When these parameter combinations meet specific conditions, the system triggers a thermal runaway early warning. The specific conditions include:

[0073]

[0074] The calculation formula is as follows:

[0075]

[0076] where, is the threshold of the separator equivalent conductivity dynamically adjusted according to the lithium-ion concentration, and σ0 is the equivalent conductivity threshold of the base separator, k is an adjustment coefficient used to represent the effect of lithium ion concentration on the separator conductivity, and C ref is the reference lithium ion concentration.

[0077] To adapt to different operating environments (such as high load or low temperature conditions), the system dynamically adjusts the combined warning boundary by combining real-time data and historical data. For example, when the battery is in a high load state for a long time, the system will reduce so as to issue a warning signal in advance.

[0078] Once the combined warning condition is triggered, the system will send an alarm to the user, suggesting to immediately stop using the battery; automatically record the relevant data triggering the warning, including abnormal parameters and the operating condition information at the time of triggering; and use the recorded data for subsequent optimization and improvement of the warning algorithm.

[0079] This design can more accurately judge the thermal runaway risk of the battery by comprehensively analyzing the synergistic relationship between multiple parameters, and has good adaptability and scalability.

[0080] Those skilled in the art of this technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the art to which this application belongs. It should also be understood that terms defined in general dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as here.

[0081] The meaning of "and / or" described in this application refers to the situation where each exists alone or both exist simultaneously.

[0082] Based on the above inspiration from the ideal embodiments of the present invention, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of this invention. The technical scope of this invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring, characterized in that: The following steps are involved: Step 1. Collecting the voltage V(t), current I(t) and temperature T(t) data of the battery cell, and monitoring its abnormal value and / or abnormal growth rate to generate the first type of warning characteristic parameters; Step 2. Performing statistical analysis on the battery cell voltage to detect mean deviation and / or information entropy deviation to generate a second type of warning characteristic parameter; Step 3. Based on the battery electrochemical model, the electrochemical parameters of the battery cells are identified by particle swarm optimization method, and the third type of warning characteristic parameters are generated according to the abnormal electrochemical parameters obtained by identification; Step 4. If any parameter in any type of warning characteristic parameters in steps 1 to 3 is abnormal, the warning is immediately initiated.

2. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 1, characterized in that: Before performing abnormal value and / or abnormal growth rate calculation on the voltage V(t), current I(t) and temperature T(t) data of the battery cell collected in step 1, the voltage V(t), current I(t) and temperature T(t) data are preprocessed. The specific process of the preprocessing is as follows: 1.1: Perform data cleaning, remove noise and outliers from the collected data to ensure data quality and accuracy, and remove negative values ​​or obviously erroneous data that exceeds a reasonable range; 1.2: Perform data segmentation to segment continuous time series data based on time windows or event triggers to ensure that each segment of data has similar characteristics.

3. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 2, characterized in that: The abnormal values ​​of the voltage V(t), current I(t) and temperature T(t) of the battery cell in step 1 are determined by comparing the actual measured values ​​with the preset threshold values. If the actual measured values ​​of the voltage V(t), current I(t) and temperature T(t) exceed the preset threshold values, they are determined to be abnormal values.

4. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 2, characterized in that: The calculation formula of the growth rate in step 1 is as follows: Where y represents the data type collected, including voltage, current or temperature, and σ y is the real-time growth rate of data y, y(i) is the real-time value of data type y at the i-th moment, and y(i+1) is the real-time value of data type y at the i+1-th moment; The growth rate thresholds of voltage, current and temperature are preset. When any of the real-time growth rates of voltage, current or temperature exceeds the preset growth rate threshold of the corresponding voltage, current or temperature, it is determined to be an abnormal growth rate.

5. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 1, characterized in that: The calculation formula of the mean deviation in step 2 is as follows: Among them, V i is the battery cell voltage at the i-th time point, is the mean of all voltages, N is the number of data points; The calculation formula of information entropy is as follows: Among them, P(V j ) is the probability distribution of the voltage value of the j-th monomer, k is the number of categories of voltage values, Based on the statistical analysis of historical data, the judgment standard values ​​of mean deviation and information entropy deviation are determined. When any measured value of mean deviation or information entropy deviation is greater than the corresponding judgment standard value of mean deviation or information entropy deviation, the second type of warning characteristic parameters are generated.

6. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 1, characterized in that: In step 3, the lithium ion concentration C is selected Li+ and the equivalent conductivity of the diaphragm σ sep As an electrochemical parameter to characterize the internal state of the battery, the lithium ion concentration C Li+ and the equivalent conductivity of the diaphragm σ sep The change of directly reflects the potential fault characteristics of short circuit and diaphragm failure in the battery. The specific process of electrochemical parameter identification is: combining the electrochemical model with the particle swarm optimization algorithm to conduct real-time monitoring, and dynamically invert C by collecting the voltage and current external signals during battery operation. Li+ and σ sep ; let the simulated voltage fit the collected voltage, that is, the loss function of parameter identification, calculate the difference between the simulated voltage and the measured voltage, and when the difference between the simulated voltage and the measured voltage is the smallest, the value of the objective function is minimized, thereby obtaining the electrochemical parameter value in the electrochemical model; The particle swarm optimization algorithm takes the minimization of the root mean square error between the simulated voltage and the measured voltage of the lithium-ion battery as the objective function and iteratively optimizes the model parameters to obtain high-precision real-time C Li+ and σ sep Electrochemical parameters, Among them, V sim (i) is the simulated voltage at the i-th time point, V meas (i) is the measured voltage at the i-th time point, and N is the number of data points.

7. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 6, characterized in that: The process of acquiring the simulated voltage is as follows: voltage data of a battery cell is collected during the operation of the battery, and the simulated voltage of the battery is obtained through electrochemical model simulation.

8. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 6, characterized in that: In the combined early warning mechanism of the third type of electrochemical parameters in step 3, based on the monitored lithium ion concentration C Li+ and the equivalent conductivity of the diaphragm σ se Electrochemical parameters, set the combined warning boundary, when the lithium ion concentration C Li+ and the equivalent conductivity of the diaphragm σ sep When the electrochemical parameter combination meets certain conditions, the thermal runaway warning is triggered. The specific conditions are: in It is the equivalent conductivity threshold of the diaphragm dynamically adjusted according to the lithium ion concentration. The formula is as follows: Where σ0 is the equivalent conductivity threshold of the basic diaphragm, k is the adjustment coefficient, which is used to represent the effect of lithium ion concentration on the conductivity of the diaphragm, C ref is the reference lithium ion concentration.

9. The lithium-ion battery thermal runaway early warning method based on multi-parameter monitoring according to claim 8, characterized in that: In order to adapt to the high-load or low-temperature operating environment, the combined warning boundary is dynamically adjusted by combining real-time data and historical data. When the battery is in a high-load state for a long time, the combined warning boundary is reduced. Thereby, an early warning signal is issued in advance. Once the combined warning conditions are triggered, the system will alert the user and immediately stop using the battery; it will automatically record the relevant data that triggers the warning, including abnormal parameters and operating conditions information when triggered.

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