Sodium-ion battery thermal runaway early warning method and system

Abstract

The invention discloses a sodium ion battery thermal runaway early warning method and system, and the method comprises the steps: obtaining the state parameters of a battery, and the state parameters comprise the voltage of the battery, the surface pressure of the battery, the temperature of the battery and the gas concentration; calculating a risk index of each state parameter based on the state parameters; calculating a thermal runaway risk comprehensive index of the sodium-ion battery based on the risk index; and judging a thermal runaway stage based on the thermal runaway risk comprehensive index, and sending out an early warning signal. According to the method, the state parameters including multiple parameters of the sodium ion battery are subjected to fusion analysis, and the multi-stage early warning strategy is constructed according to the thermal runaway risk comprehensive index, so that the system can take intervention measures in advance, the possibility of safety accidents is reduced, and the reliability of the energy storage system is improved.

Description

Technical Field

[0001] The present invention belongs to the field of battery technology, and in particular relates to a sodium ion battery thermal runaway early warning method and system. Background Art

[0002] Sodium-ion batteries, a promising energy storage device, are attracting increasing attention for their safety. Thermal runaway is a serious safety risk faced by sodium-ion batteries. Once it occurs, it can lead to dangerous situations such as battery fire and explosion, resulting in significant losses to personnel and property.

[0003] Despite significant breakthroughs in low-temperature performance, the mechanisms of performance degradation at high temperatures and under complex operating conditions remain unclear. High temperatures accelerate electrolyte decomposition and interfacial side reactions, while sodium deposition during cycling increases the risk of dendrite growth. Frequent charging and discharging can degrade sodium-ion battery stability, further contributing to the risk of thermal runaway.

[0004] In related technologies, early warning of sodium-ion battery failures mostly relies on temperature or voltage threshold monitoring. This traditional single parameter monitoring system is difficult to capture the complex changes under the coupling of multiple physical fields inside the battery, resulting in the system being unable to identify thermal runaway precursor signals at an early stage, unable to provide early warning to take intervention measures, and a high possibility of safety accidents. The reliability of sodium-ion batteries is poor. Summary of the Invention

[0005] The purpose of the present invention is to enable a sodium ion battery to issue a warning signal in time before thermal runaway occurs, so that corresponding measures can be taken to reduce the possibility of accidents and improve the reliability of the use of sodium ion batteries.

[0006] To achieve the above objectives, the present invention proposes a sodium-ion battery thermal runaway early warning method, comprising: obtaining battery state parameters, wherein the state parameters include battery voltage, battery surface pressure, battery temperature, and gas concentration; calculating a risk index for each state parameter based on the state parameters; calculating a comprehensive thermal runaway risk index for the sodium-ion battery based on the risk index; and judging the thermal runaway stage based on the comprehensive thermal runaway risk index, and issuing an early warning signal.

[0007] In an optional embodiment, calculating the risk index of each state parameter based on the state parameter specifically includes:

[0008] The pressure change rate is obtained based on the battery surface pressure; the temperature change rate is obtained based on the battery temperature; and the risk index is calculated based on the battery voltage, the gas concentration, the pressure change rate, and the temperature change rate.

[0009] In an optional embodiment, the calculation formula of the risk index is as follows:V =[VV safe ] + ;f F =[ΔF-ΔF safe ] + ;f T =[ΔT-ΔT safe ] + ;f G =[GG safe ] + ; Where V is the voltage value; ΔF is the pressure change rate; ΔT is the temperature change rate; G is the gas concentration; V safe , ΔF safe , ΔT safe and G safe are voltage threshold, pressure change rate threshold, temperature change rate threshold and gas concentration threshold respectively; f V is the voltage risk index, f F is the pressure risk indicator, f T is the temperature risk index, f G is a gas concentration risk indicator; [·] + Indicates that only non-negative values are taken. When the value in the brackets is less than 0, it is taken as 0.

[0010] In an optional embodiment, the risk indicators include a voltage risk indicator, a pressure risk indicator, a temperature risk indicator and a gas concentration risk indicator, and a comprehensive thermal runaway risk indicator of the sodium ion battery is calculated based on the risk indicators, specifically including: weighting the risk information of the voltage risk indicator, the pressure risk indicator, the temperature risk indicator and the gas concentration risk indicator respectively to obtain weights; and calculating the comprehensive thermal runaway risk indicator based on the weights.

[0011] The weight is calculated using an anti-entropy weight method.

[0012] In an optional embodiment, the thermal runaway risk comprehensive index is obtained based on the weight calculation, specifically including: obtaining the risk index from multiple test samples; and normalizing the risk index to obtain a normalized risk index, and the calculation formula is as follows: Where x ij is the original value of risk index j in the i-th sample, including voltage risk index, pressure risk index, temperature risk index and gas concentration risk index; min(x j ) and max(x j ) are the minimum and maximum values of risk index j in all samples; a ij is the normalized risk index in the i-th sample;

[0013] Based on the normalized risk index and weight, the thermal runaway risk comprehensive index is calculated, and the calculation formula is as follows: R = 1-(w v f′ v +w F f′ F +w T f′ T +w G f′ G );W V 、w F 、w T 、w G are the weights corresponding to voltage, pressure, temperature and gas concentration respectively; ai j Including f′ V 、f′ F 、f′ T and f′ G , f′ V 、f′ F 、f′ T and f′ G They are the normalized voltage risk index, the normalized pressure risk index, the normalized temperature risk index and the normalized gas concentration risk index.

[0014] In an optional embodiment, the thermal runaway risk comprehensive index is calculated based on the normalized risk index and the weight, specifically comprising: based on the normalized risk index, for each index j, calculating the ratio of the normalized values of the respective samples, and the calculation formula is as follows: Where pi j is the proportion of the normalized value of risk indicator j in the i-th sample; a ij is the normalized risk index in the i-th sample; n is the number of samples;

[0015] The entropy value of each risk indicator j is calculated based on the ratio of the normalized values. The calculation formula is as follows: Where pi j is the ratio of the normalized values of risk indicator j in the i-th sample; k = 1 / ln(n) is used to normalize the entropy value to the interval [0,1]; based on the entropy value of the risk indicator j, the coefficient of difference is calculated for each risk indicator, and the calculation formula is as follows: j =1-E j Where, E j is the entropy value of risk indicator j; the weight of each risk indicator is determined based on the difference coefficient, and the calculation formula is as follows: Where, d j is the coefficient of variation of indicator j.

[0016] In an optional embodiment, the thermal runaway risk comprehensive index is obtained based on the weight calculation, specifically comprising: for each sample i, constructing a normalized comprehensive index according to the normalized risk index and the corresponding weight, and the calculation formula is as follows: Where a ij is the normalized risk index in the i-th sample; w j is the weight of index j; based on the normalized comprehensive index, the thermal runaway risk comprehensive index of the sodium ion battery under sample i is obtained, and the calculation formula is as follows: S i =1-R i Where R i To create a normalized comprehensive index, S i It is the comprehensive index of thermal runaway risk of sodium-ion battery under sample i.

[0017] In an optional embodiment, the thermal runaway stage is judged based on the comprehensive thermal runaway risk index and a warning signal is issued, specifically including: if 0.9≤R≤1, it is judged to be a first-level warning stage, and a first-level warning signal is issued; if 0.8≤R≤0.9, it is judged to be a second-level warning stage, and a second-level warning signal is issued; if 0.7≤R≤0.8, it is judged to be a third-level warning stage, and a third-level warning signal is issued.

[0018] The present invention also provides a sodium ion battery thermal runaway early warning system, comprising: a voltage detection module for acquiring battery voltage; a pressure collector attached to the surface of the sodium ion battery housing to acquire battery surface pressure; a temperature sensor disposed inside the sodium ion battery, the temperature sensor being suitable for acquiring battery temperature; a gas detector disposed inside the sodium ion battery, the gas detector being suitable for acquiring gas concentration inside the sodium ion battery; a data processing module; and a data collector communicatively connected to the voltage detection module, the pressure collector, the temperature sensor, the gas detector, and the data processing module, respectively. Battery state parameters include the battery voltage, the battery surface pressure, the battery temperature, and the gas concentration. The data collector sends the state parameters acquired in real time to the data processing module. The data processing module calculates a risk index for each state parameter based on the state parameters, calculates a comprehensive thermal runaway risk index for the sodium ion battery based on the risk index, and determines the thermal runaway stage based on the comprehensive thermal runaway risk index, thereby issuing an early warning signal.

[0019] The beneficial effects of the present invention are:

[0020] 1. The present invention integrates and analyzes the state parameters of sodium-ion batteries, including multiple parameters, and constructs a multi-level early warning strategy based on the comprehensive thermal runaway risk index, enabling the system to take intervention measures in advance, reducing the possibility of safety accidents and improving the reliability of the energy storage system.

[0021] 2. The present invention adopts the anti-entropy weight method to adaptively adjust the weight of each monitoring parameter according to the parameter change trend in different stages, ensuring that the most representative parameters can obtain higher weights in each stage. The dynamic weighting method enables the early warning model to dynamically adapt to the characteristic changes in different stages, effectively avoiding erroneous or delayed alarms, and improving sensitivity and real-time performance.

[0022] 3. The present invention simultaneously collects four key parameters: voltage change, surface temperature, pressure rate, and gas concentration. Through data normalization, risk assessment, and dynamic weight calculation, it achieves all-round, multi-dimensional monitoring, improves the early identification capability of thermal runaway, and prevents the battery from entering an uncontrollable state. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a sodium ion battery thermal runaway early warning method provided for the implementation of the present invention;

[0024] Figure 2 A block diagram of a sodium-ion battery thermal runaway warning system provided in an embodiment of the present invention.

[0025] Description of reference numerals:

[0026] 110. Voltage detection module; 120. Pressure collector; 131. First temperature sensor; 132. Second temperature sensor; 141. Carbon monoxide sensor; 142. Hydrogen sensor; 150. Data collector; 160. Data processing center. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1 As shown, according to an embodiment of the present invention, on the one hand, a sodium ion battery thermal runaway early warning method is provided, comprising the following steps:

[0029] Step S101: obtaining battery state parameters, which include battery voltage, battery surface pressure, battery temperature and gas concentration.

[0030] Step S103: Calculate the risk index of each state parameter based on the state parameter.

[0031] Step S105: Calculate a comprehensive thermal runaway risk index for the sodium-ion battery based on the risk index.

[0032] Step S107: Determine the thermal runaway stage based on the comprehensive thermal runaway risk index and issue a warning signal.

[0033] Battery voltage is a key parameter reflecting the state of electrochemical reactions within a battery. High-precision voltage sensors are used to obtain real-time voltage readings of sodium-ion batteries. Under normal operating conditions, the battery voltage exhibits a specific pattern of change during the charge and discharge process. However, when abnormal reactions occur within the battery, such as short circuits, overcharge, or over-discharge, the voltage may fluctuate abnormally or deviate from the normal range. For example, in the event of overcharge, the battery voltage may continue to rise, exceeding its rated voltage range. A voltage risk assessment model is established based on the acquired battery voltage data. This model calculates voltage risk indicators based on factors such as the rate of change of voltage over time and the degree to which the voltage deviates from the normal range.

[0034] During operation, sodium-ion batteries may generate internal gases, causing changes in surface pressure. Using a pressure sensor installed on the battery surface, the surface pressure can be accurately measured. An abnormal increase in surface pressure may indicate an accelerated rate of internal gas generation, which may be related to thermal runaway reactions within the battery. For example, when a decomposition reaction occurs within the battery, gas is generated, increasing internal pressure and, in turn, increasing surface pressure. For battery surface pressure, pressure data is used to develop a pressure risk assessment model. This model considers factors such as the pressure trend over time and the extent to which the pressure exceeds the safety threshold.

[0035] During normal operation, sodium-ion batteries generate a certain amount of heat during the charge and discharge process, but when thermal runaway occurs within the battery, the temperature can rise dramatically. For example, when a short circuit occurs within the battery, the current increases dramatically, rapidly increasing Joule heating within the battery and causing a rapid temperature rise. This abnormal temperature change is an important basis for thermal runaway early warning. A temperature risk assessment model is constructed based on battery temperature data. This temperature risk assessment model comprehensively considers factors such as the absolute value of temperature, the rate of temperature change, and the temperature gradient.

[0036] When gas concentrations exceed a certain threshold, it may indicate that thermal runaway has occurred within the battery. For example, the production of hydrogen may be a product of electrolyte decomposition within the battery, and an abnormally high concentration of hydrogen is a key warning sign of thermal runaway. A gas concentration risk assessment model is developed based on gas concentration data. The gas concentration risk assessment model calculates gas concentration risk indicators based on factors such as gas concentration thresholds and gas concentration change rates.

[0037] This sodium-ion battery thermal runaway early warning method divides the thermal runaway process of sodium-ion batteries into different stages according to the size range of the comprehensive thermal runaway risk index, and issues early warning signals of corresponding levels according to the judged thermal runaway stage. It can comprehensively consider the impact of multiple factors on the thermal runaway risk, realize early warning of thermal runaway of sodium-ion batteries, and provide strong protection for the safe use of sodium-ion batteries.

[0038] By integrating multi-source data such as voltage, temperature, gas concentration, and pressure in real time, combining the unique failure modes of sodium-ion batteries, designing a multi-parameter fusion algorithm, and constructing a dynamic risk assessment model, the system reliability and safety can be improved.

[0039] Furthermore, step S103, calculating the risk index of each state parameter based on the state parameter, specifically includes the following steps:

[0040] Step S1031: Obtaining the pressure change rate based on the battery surface pressure.

[0041] Step S1033: Obtaining the temperature change rate based on the battery temperature.

[0042] Step S1035: Calculate a risk index based on the battery voltage, gas concentration, pressure change rate, and temperature change rate.

[0043] In this embodiment, the collected pressure data are arranged in chronological order to form a pressure time series, and the pressure change rate is calculated, for example, by using a differential method, to analyze the trend and abnormality of the pressure change.

[0044] The collected temperature data are arranged in chronological order to form a temperature time series, and the temperature change rate is calculated to analyze the trend and abnormal conditions of temperature changes.

[0045] Based on the battery voltage V, gas concentration, pressure change rate ΔF and temperature change rate ΔT, a risk index calculation model can be constructed. By substituting the preprocessed data into the risk index calculation model, the risk index can be calculated in real time.

[0046] Specifically, the calculation formula of the risk indicator is as follows:

[0047] f V =[VV safe ] + ;

[0048] f F =[ΔF-ΔF safe ] + ;

[0049] f T =[ΔT-ΔT safe ]+ ;

[0050] f G =[GG safe ] + ;

[0051] Where V is the voltage value; ΔF is the pressure change rate; ΔT is the temperature change rate; G is the gas concentration; V safe , ΔF safe , ΔT safe and G safe are voltage threshold, pressure change rate threshold, temperature change rate threshold and gas concentration threshold respectively; f V is the voltage risk index, f F is the pressure risk indicator, f T is the temperature risk index, f G is a gas concentration risk indicator; [·] + Indicates that only non-negative values are taken. When the value in the brackets is less than 0, it is taken as 0.

[0052] This calculation method ensures that the risk index will not be negative, thereby more reasonably reflecting the battery's thermal runaway risk. Through these risk indicators, the thermal runaway risk of sodium-ion batteries can be comprehensively assessed, and corresponding early warning and control measures can be taken according to the size of the risk index.

[0053] The risk indicators include a voltage risk indicator, a pressure risk indicator, a temperature risk indicator, and a gas concentration risk indicator. In step 105, a comprehensive thermal runaway risk indicator of the sodium ion battery is calculated based on the risk indicators, specifically including the following indicators:

[0054] Step 1051: weighting the risk information of the voltage risk index, the pressure risk index, the temperature risk index, and the gas concentration risk index respectively to obtain weights.

[0055] Step 1053: Obtain a comprehensive thermal runaway risk index based on weight calculation.

[0056] Because different state parameters contribute to the thermal runaway risk of sodium-ion batteries to varying degrees, it is necessary to assign appropriate weights to each risk indicator. Weights are determined through experimental research, expert experience, or data-driven approaches. These weights reflect the importance of each factor in thermal runaway risk assessment. For example, if the temperature change rate is likely to have the greatest impact on thermal runaway, a higher weight can be assigned; if the gas concentration is likely to have a relatively small impact, a lower weight can be assigned.

[0057] Assume that after analysis and calculation, the following weight distribution is obtained: Voltage risk indicator weight w v =0.3; Pressure risk indicator weight w F=0.2; Temperature risk index weight w T =0.3; Gas concentration risk index weight w G = 0.2. Based on the assigned weights, a weighted summation method is used to calculate the comprehensive thermal runaway risk index for sodium-ion batteries. The sodium-ion battery thermal runaway early warning method calculates a comprehensive risk index based on the weights of each risk index. Risk assessment and early warning are then performed based on the value of the comprehensive index, thereby more effectively preventing and controlling the occurrence of thermal runaway accidents.

[0058] The anti-entropy weighting method is used to calculate weights, which change in real time over time, enabling real-time detection of thermal runaway states in sodium-ion batteries. During the operation of sodium-ion batteries, various risk factors may change. The anti-entropy weighting method automatically adjusts weights based on these changes, enabling the system to better cope with complex and changing situations and adapt to different operating conditions and environments, thereby improving the system's flexibility and adaptability. This helps to improve the reliability and effectiveness of the thermal runaway warning system.

[0059] Based on step 1053, a comprehensive thermal runaway risk index is obtained based on weighted calculation, specifically including the following steps:

[0060] Step 10531: Obtain risk indicators from multiple test samples.

[0061] Step 10533: In order to ensure the accuracy of the analysis results, the data must be preprocessed to remove outliers and ensure data integrity, and the risk indicators must be normalized to obtain normalized risk indicators.

[0062] The calculation formula is as follows:

[0063]

[0064] Where a ij is the normalized risk index in the i-th sample; x ij is the original value of risk index j in the i-th sample, including voltage risk index, pressure risk index, temperature risk index and gas concentration risk index; min(x j ) and max(x j ) are the minimum and maximum values of risk index j in all samples respectively.

[0065] Step 10535: Based on the normalized risk index and weight, calculate the comprehensive thermal runaway risk index. The calculation formula is as follows:

[0066] R=1-(w v f′ v +w F f′ F +w T f′T +w G f′ G );

[0067] Where w V 、w F 、w T 、w G are the weights corresponding to voltage, pressure, temperature and gas concentration respectively; aij includes f′ V 、f′ F 、f′ T and f′ G , f′ V 、f′ F 、f′ T and f′ G They are the normalized voltage risk index, the normalized pressure risk index, the normalized temperature risk index and the normalized gas concentration risk index.

[0068] R is a comprehensive indicator of sodium-ion battery failure risk, reflecting the combined impact of various indicators on the battery thermal runaway risk. The closer R is to 1, the safer the battery is. Ultimately, this coefficient can be used to comprehensively evaluate the safety risk of the entire thermal runaway process.

[0069] As thermal runaway progresses, more and more parameters are affected, indicating that the degree of thermal runaway is becoming more and more serious. Therefore, the comprehensive indicators for evaluating the failure risk of sodium-ion batteries can reflect the thermal runaway process to a certain extent.

[0070] The anti-entropy weight method is used to objectively weight the risk information of the four indicators of voltage, pressure, temperature and gas concentration, and then a comprehensive risk score is constructed and converted into a safety factor.

[0071] Step 10535, based on the normalized risk index and weight, calculates a comprehensive thermal runaway risk index, specifically including the following steps:

[0072] Step 105351: Based on the normalized risk index, for each index j, calculate the ratio of the normalized values of each sample. The calculation formula is as follows:

[0073]

[0074] Where p ij is the proportion of the normalized value of risk indicator j in the i-th sample; a ij is the normalized risk index in the i-th sample; n is the number of samples.

[0075] The purpose of this step is to reflect the relative distribution of each sample on a certain indicator.

[0076] Step 105352: Calculate the entropy value of each risk indicator j based on the ratio of the normalized values. The entropy value reflects the degree of information dispersion of the indicator. The calculation formula is as follows:

[0077]

[0078] Where p ij is the ratio of the normalized values of risk indicator j in the i-th sample; k = 1 / ln(n) is used to normalize the entropy value to the interval [0, 1]. If the data is highly dispersed, the entropy value is low, indicating that the information utility of the indicator is high; if the data is less concentrated, the entropy value is high and the information utility is low.

[0079] Step 105353: Based on the entropy value of risk indicator j, calculate the coefficient of variation for each risk indicator. The calculation formula is as follows:

[0080] d j =1-E j ;

[0081] Where, E j is the entropy value of risk indicator j. The larger the coefficient of difference, the stronger the ability of the indicator to distinguish between samples.

[0082] Step 105354: Determine the weight of each risk indicator based on the coefficient of variation. The calculation formula is as follows:

[0083]

[0084] Where, d j is the coefficient of difference of index j. In this way, the weights of voltage, pressure, temperature and gas concentration in the comprehensive evaluation can be obtained.

[0085] Step S105355: For each sample i, construct a normalized comprehensive index based on the normalized risk index and the corresponding weight. The calculation formula is as follows:

[0086]

[0087] Where a ij is the normalized risk index in the i-th sample, which reflects the comprehensive impact of each index on the battery thermal runaway risk after weighting. j is the weight of indicator j.

[0088] Step S105356: Based on the normalized comprehensive index, the comprehensive index of thermal runaway risk of the sodium-ion battery under sample i is obtained. The calculation formula is as follows:

[0089] S i =1-R i .

[0090] Where R i To create a normalized comprehensive index, S i It is a comprehensive indicator of thermal runaway risk of sodium-ion batteries under sample i. The higher the value, the higher the safety.

[0091] The anti-entropy weighting method determines weights based on the distribution differences in the degree of dispersion of indicator data at each stage, avoiding the uncertainty associated with subjective weighting. This is particularly important across different stages, as the changing patterns of indicators such as pressure, temperature, and gas concentration can vary significantly. Objective weighting more accurately reflects the risk profile of each indicator at the current stage. During battery thermal runaway, the key characteristics of each stage differ. In the early stages, pressure changes may be more pronounced, while changes in temperature and gas concentration lag behind. By calculating the entropy value and weighting of each indicator at each stage, the anti-entropy weighting method automatically adjusts the importance of each indicator, ensuring that the safety early warning model can promptly capture critical changes. As measurement data is continuously updated, the anti-entropy weighting method recalculates indicator weights in real time. This data-driven approach enables the early warning model to dynamically reflect actual on-site conditions, providing timely warnings when abnormal risks are discovered, and enhancing the responsiveness of overall safety management.

[0092] This paper proposes a three-level early warning strategy for thermal runaway in sodium-ion batteries based on a multi-parameter coupling criterion. This strategy implements a hierarchical approach to thermal runaway prevention and control through collaborative analysis of multi-dimensional fault characteristics, with three stages: the first, second, and third warning stages. The details are as follows.

[0093] Based on step S107, the thermal runaway stage is determined in combination with the comprehensive thermal runaway risk index, and a warning signal is issued, which specifically includes the following steps:

[0094] Step S1071: If 0.9≤R≤1, it is determined to be a first-level warning stage, and a first-level warning signal is issued.

[0095] Step S1073: If 0.8≤R≤0.9, it is determined to be the second-level warning stage, and a second-level warning signal is issued.

[0096] Step S1075: If 0.7≤R≤0.8, it is determined to be a level 3 warning stage, and a level 3 warning signal is issued.

[0097] Among them, in the first-level warning stage, the weight of the battery surface pressure is the largest; in the second-level warning stage, the weight of the gas concentration is the largest.

[0098] When 0.9≤R≤1, it is the first-level warning stage, which mainly detects the early expansion monitoring stage of the battery. During the overcharging process, excessive sodium ions escape from the positive electrode, causing the electrode structure to collapse and releasing a large amount of heat and oxygen. The presence of oxygen will accelerate the decomposition of the electrolyte, producing a large amount of CO, CO2 and other gases, causing the internal pressure of the battery to continue to rise, causing the battery to gradually expand; in this process, the voltage also gradually increases. The internal stress accumulation effect caused by the continuous growth of the SEI film inside the battery induces mechanical deformation, so the pressure weight is the largest in the first-level warning. Use the pressure collector 120 to detect the pressure on the battery surface. When the pressure change rate exceeds the threshold, it indicates that the risk index of sodium dendrites piercing the diaphragm exceeds the safety limit. The pressure signal can provide a warning time of more than 1 hour. Thermal runaway can be avoided by cutting off the power supply and replacing the battery in advance.

[0099] When 0.8≤R≤0.9, it is the second-level warning stage, which is mainly the mid-term gas production identification stage. When it is detected that the internal pressure and voltage changes of the sodium-ion battery slow down, while the temperature changes gradually accelerate, it enters the second-level warning stage. By using an integrated fiber-optic gas sensor in the battery module, by detecting characteristic gases, including the threshold value of sudden changes in CO and H2 concentrations, the internal short circuit and gas escape caused by diaphragm melting are identified, and the potential risk of thermal runaway is discovered in advance. When the gas concentration gradient change rate exceeds the critical value, CO>200ppm / s, H2>50ppm / s, the system starts a mid-term warning 20 minutes in advance. By early exhaust of combustible gases and temperature control intervention, further thermal diffusion of the battery can be avoided.

[0100] When 0.7≤R≤0.8, the system enters the Level 3 warning stage, primarily a critical failure management phase. During this stage, various parameters fluctuate rapidly. As the diaphragm completely decomposes, an internal short circuit occurs in the battery. The electrolyte is directly exposed to air, triggering a more violent chemical reaction and releasing more gas, leading to a secondary gas release and a rapid increase in pressure. At this point, the voltage increases dramatically, and the rate of temperature rise accelerates dramatically, signaling an impending thermal runaway. By monitoring various parameters, the highest level of alarm can be triggered minutes before thermal runaway occurs. At this point, thermal runaway and heat spread are inevitable, necessitating timely fire prevention measures to minimize the impact of heat spread on the overall safety of the energy storage system and prevent loss of personnel and equipment.

[0101] By dividing these three warning stages and combining voltage, pressure, temperature, and gas concentration indicators, real-time warnings of sodium-ion battery state changes can be obtained, promptly identifying potential safety risks. The risk indicators designed for each stage are designed to capture subtle changes in the battery's state. The anti-entropy weight method is used in different stages to ensure that the data collection and processing methods in each stage can truly reflect the changing characteristics of each indicator, thereby providing multiple guarantees for the safe operation of the battery and giving users sufficient time to take emergency measures to avoid the more serious consequences of thermal runaway.

[0102] like Figure 2 As shown, the present invention also proposes a sodium ion battery thermal runaway early warning system, comprising: a voltage detection module 110 for acquiring battery voltage; a pressure collector 120 attached to the surface of the sodium ion battery housing to acquire battery surface pressure; a temperature sensor disposed inside the sodium ion battery, the temperature sensor being suitable for acquiring battery temperature; a gas detector disposed inside the sodium ion battery, the gas detector being suitable for acquiring gas concentration inside the sodium ion battery; a data processing center 160; and a data collector 150 communicatively connected to the voltage detection module 110, the pressure collector 120, the temperature sensor, the gas detector, and the data processing center 160, respectively. Battery state parameters include battery voltage, battery surface pressure, battery temperature, and gas concentration. The data collector 150 sends the state parameters acquired in real time to the data processing center 160. The data processing center 160 calculates a risk index for each state parameter based on the state parameters, and calculates a comprehensive thermal runaway risk index for the sodium ion battery based on the risk index. The data processing center 160 determines the thermal runaway stage based on the comprehensive thermal runaway risk index and issues an early warning signal.

[0103] In this embodiment, the sodium-ion battery thermal runaway warning system includes a pressure collector 120, a digital temperature sensor, a gas detector, a voltage detection module 110, a data collector 150, and a data processing center 160. The system can monitor each of the pressure collector 120, temperature sensor, gas detector, voltage detection module 110, etc. in real time, and then transmit the data to the data processing center 160 via the data collector 150 to obtain voltage data, surface pressure data, temperature data, gas concentration increase efficiency, etc., to determine whether the battery has a thermal runaway fault and determine the warning level.

[0104] In this embodiment, three temperature sensors are provided, namely, a first temperature sensor 131 located at the positive electrode and the negative electrode of the sodium ion battery, and a second temperature sensor 132 located near the center of the battery. In order to prevent the battery liquid from corroding the sensor, the sensor housing is encapsulated with corrosion-resistant materials to ensure long-term stable operation. In addition, during the thermal runaway of the sodium ion battery, the temperature rise causes a chemical reaction to produce gas, and the measurement of gas is also an important means of detecting thermal runaway. The gas detector in the present invention adopts an integrated fiber optic gas sensor. The fiber optic sensor has excellent anti-electromagnetic interference ability, high temperature resistance and high pressure resistance. It can still maintain stable performance in the high temperature and high pressure environment brought about by the thermal runaway of the sodium ion battery, thereby ensuring the reliability of the test results. The gas detector mainly includes a carbon monoxide sensor 141 and a hydrogen sensor 142.

[0105] In this embodiment, the pressure collector 120 is attached to the surface of the battery to monitor the pressure changes on the battery surface. When the battery expands, it means that the battery pressure increases. The pressure change will be captured by the sensor and converted into an electrical signal, thereby reflecting the degree of battery expansion.

[0106] The present invention utilizes temperature, gas concentration, pressure, and voltage to detect thermal runaway in sodium-ion batteries under overcharge conditions. To overcome the problem of untimely thermal runaway detection in the prior art, which can lead to explosions and fires, a multi-stage overcharge thermal runaway detection system is employed. This system effectively identifies the severity of the thermal runaway, allowing for appropriate measures to be taken based on the characteristics of each stage, thereby improving detection accuracy. The present invention designs different detection processes based on the different stages of thermal runaway. Each test step is interdependent and indispensable, making the entire system more scientific and effective.

[0107] The present invention can be widely used in fields such as electric vehicles, grid energy storage, and consumer electronics, providing reliable protection for the safe application of sodium-ion batteries.

[0108] This invention proposes a four-stage early warning system, with pressure as the core parameter and incorporating voltage, temperature, and gas concentration. The system sets critical thresholds based on pressure changes at each warning stage. By monitoring the rise and fall rates and abnormal fluctuations in battery surface pressure in real time, it accurately captures key signals such as internal gas accumulation, shell expansion, and structural stress changes. This allows for timely detection of potential danger signals and ensures that the battery operates in normal working order.

[0109] The above embodiments are provided for illustrative purposes only and are not intended to limit the scope of implementation. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to provide an exhaustive list of all implementations. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A sodium ion battery thermal runaway early warning method, characterized in that: The sodium ion battery thermal runaway early warning method comprises: Acquiring battery state parameters, including battery voltage, battery surface pressure, battery temperature, and gas concentration; Calculating a risk index for each state parameter based on the state parameter; Calculate a comprehensive thermal runaway risk index for the sodium ion battery based on the risk index; The thermal runaway stage is judged based on the comprehensive thermal runaway risk index and an early warning signal is issued.

2. The sodium ion battery thermal runaway early warning method according to claim 1, characterized in that: Calculating the risk index of each state parameter based on the state parameter specifically includes: obtaining a pressure change rate based on the battery surface pressure; obtaining a temperature change rate based on the battery temperature; The risk index is calculated based on the battery voltage, the gas concentration, the pressure change rate, and the temperature change rate.

3. The sodium ion battery thermal runaway early warning method according to claim 2, characterized in that: The calculation formula of the risk index is as follows: f V =[V-V safe ] + ; f F =[ΔF-ΔF safe ] + ; f T =[ΔT-ΔT safe ] + ; f G =[G-G safe ] + ; Where V is the voltage value; ΔF is the pressure change rate; ΔT is the temperature change rate; G is the gas concentration; V safe , ΔF safe , ΔT safe and G safe are voltage threshold, pressure change rate threshold, temperature change rate threshold and gas concentration threshold respectively; f V is the voltage risk index, f F is the pressure risk indicator, f T is the temperature risk index, f G is a gas concentration risk indicator; [·] + Indicates that only non-negative values are taken. When the value in the brackets is less than 0, it is taken as 0.

4. The sodium ion battery thermal runaway early warning method according to any one of claims 1 to 3, characterized in that: The risk indicators include voltage risk indicator, pressure risk indicator, temperature risk indicator and gas concentration risk indicator. Based on the risk indicators, the comprehensive thermal runaway risk indicator of the sodium ion battery is calculated, specifically including: weighting the risk information of the voltage risk index, the pressure risk index, the temperature risk index, and the gas concentration risk index respectively to obtain weights; The thermal runaway risk comprehensive index is obtained based on the weight calculation.

5. The sodium ion battery thermal runaway early warning method according to claim 4, characterized in that: The weight is calculated using an anti-entropy weight method.

6. The sodium ion battery thermal runaway early warning method according to claim 5, characterized in that: The thermal runaway risk comprehensive index is obtained based on the weight calculation, specifically including: Obtaining the risk indicators from a plurality of test samples; The risk index is normalized to obtain a normalized risk index, and the calculation formula is as follows: Where x ij is the original value of risk index j in the i-th sample, including voltage risk index, pressure risk index, temperature risk index and gas concentration risk index; min(x j ) and max(x j ) are the minimum and maximum values of risk index j in all samples; a ij is the normalized risk index in the i-th sample; Based on the normalized risk index and weight, the thermal runaway risk comprehensive index is calculated, and the calculation formula is as follows: R=1-(w v f′ v +w F f′ F +w T f′ T +w G f′ G ); Where w V 、w F 、w T 、w G are the weights corresponding to voltage, pressure, temperature and gas concentration respectively; a ij Including f′ V 、f′ F 、f′ T and f′ G , f′ V 、f′ F 、f′ T and f′ G They are the normalized voltage risk index, the normalized pressure risk index, the normalized temperature risk index and the normalized gas concentration risk index.

7. The sodium ion battery thermal runaway early warning method according to claim 6, characterized in that: Based on the normalized risk index and weight, the thermal runaway risk comprehensive index is calculated, specifically including: Based on the normalized risk index, for each index j, the ratio of the normalized values of each sample is calculated. The calculation formula is as follows: Where pi j is the proportion of the normalized value of risk indicator j in the i-th sample; a ij is the normalized risk index in the i-th sample; n is the number of samples; The entropy value of each risk indicator j is calculated based on the ratio of the normalized values. The calculation formula is as follows: Where pi j is the ratio of the normalized values of risk indicator j in the i-th sample; k = 1 / ln(n) is used to normalize the entropy value to the interval [0,1]; Based on the entropy value of the risk indicator j, the difference coefficient is calculated for each risk indicator, and the calculation formula is as follows: d j =1-E j ; Where, E j is the entropy value of risk indicator j; The weight of each risk indicator is determined based on the difference coefficient, and the calculation formula is as follows: Where, d j is the coefficient of variation of indicator j.

8. The sodium ion battery thermal runaway early warning method according to claim 5, characterized in that: The thermal runaway risk comprehensive index is obtained based on the weight calculation, specifically including: For each sample i, a normalized comprehensive index is constructed based on the normalized risk index and the corresponding weight. The calculation formula is as follows: Where a ij is the normalized risk index in the i-th sample; w j is the weight of indicator j; Based on the normalized comprehensive index, the thermal runaway risk comprehensive index of the sodium ion battery under sample i is obtained, and the calculation formula is as follows: S i =1-R i Where R i To create a normalized comprehensive index, S i It is the comprehensive index of thermal runaway risk of sodium-ion battery under sample i.

9. The sodium ion battery thermal runaway early warning method according to claim 6, characterized in that: Based on the comprehensive thermal runaway risk index, the thermal runaway stage is judged and an early warning signal is issued, specifically including: If 0.9≤R≤1, it is judged to be the first-level warning stage and a first-level warning signal is issued; If 0.8≤R≤0.9, it is judged to be the second-level warning stage and a second-level warning signal is issued; If 0.7≤R≤0.8, it is judged to be the third-level warning stage and a third-level warning signal is issued.

10. A sodium ion battery thermal runaway warning system, characterized in that: include: Voltage detection module, obtains battery voltage; A pressure collector is attached to the surface of the sodium ion battery shell to obtain the battery surface pressure; a temperature sensor, disposed inside the sodium-ion battery, adapted to obtain the battery temperature; a gas detector, disposed inside the sodium ion battery, the gas detector being adapted to obtain a gas concentration inside the sodium ion battery; Data processing module; a data collector, communicatively connected to the voltage detection module, the pressure collector, the temperature sensor, the gas detector, and the data processing module; Among them, the battery state parameters include the battery voltage, the battery surface pressure, the battery temperature and the gas concentration. The data collector sends the state parameters collected in real time to the data processing module. The data processing module calculates the risk index of each state parameter based on the state parameters, and obtains the comprehensive thermal runaway risk index of the sodium-ion battery based on the risk index. The thermal runaway stage is judged based on the comprehensive thermal runaway risk index and an early warning signal is issued.

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