Early warning method, boundary and rate curve optimization method, system, equipment and medium for the entire battery life cycle

Abstract

The present invention discloses a battery lifecycle early warning method, boundary and rate curve optimization method, system, device, and medium, belonging to the field of battery technology. The early warning method includes: obtaining sampled data of a battery cell, the sampled data including at least temperature data; obtaining charge and discharge current operating condition data under different operating conditions; generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different operating conditions based on the obtained sampled data and the charge and discharge current operating condition data; predicting the temperature rise safety boundary of the battery cell based on the temperature rise rate fitting curve; recording the number of times the battery cell exceeds the temperature rise safety boundary; and determining whether to issue an early warning based on the temperature rise rate fitting curve and the recorded number of times the battery cell exceeds the temperature rise safety boundary. The present invention can dynamically change the early warning parameters, realize monitoring of the battery's entire lifecycle, and improve the accuracy of the early warning.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery life cycle early warning method, boundary and rate curve optimization method, system, equipment and medium. Background Art

[0002] With the rapid development of new energy battery technology, battery safety issues are increasingly attracting widespread attention from all walks of life. Among them, battery thermal runaway is a key issue in the field of new energy battery safety. The prevention and early warning of thermal runaway are of vital importance to preventing battery safety accidents.

[0003] Currently, the primary method for warning battery thermal runaway is to monitor battery temperature rise and voltage drop. When the internal battery temperature or pressure exceeds a preset threshold, the system issues a warning signal. However, this approach has the following issues:

[0004] 1. Fixed warning thresholds: Traditional warning systems typically use fixed thresholds, which are often set based on the battery's factory condition. However, battery performance changes over time due to factors such as the number of charge and discharge cycles, ambient temperature, and usage time. Therefore, fixed warning thresholds often fail to accurately reflect the battery's actual condition, leading to false or missed warnings.

[0005] 2. Insufficient warning accuracy: Due to the fixed nature of the warning threshold, traditional warning systems are difficult to adapt to changes in battery performance and are therefore unable to provide accurate warning information.

[0006] This may not only result in loss of customer property, but also cause unnecessary panic. Summary of the Invention

[0007] To address the above-mentioned problems in the prior art, the present invention provides a warning method, boundary and rate curve optimization method, system, device, and medium, aiming to address the problem of poor warning accuracy caused by the inability of existing battery thermal runaway warning methods to dynamically change warning parameters over the battery life cycle. To achieve the above-mentioned objectives, the present invention provides the following technical solutions:

[0008] A thermal runaway early warning method for the entire life cycle of a battery, comprising:

[0009] Acquiring sampling data of the battery cell, wherein the sampling data at least includes temperature data;

[0010] Obtain charging and discharging current data under different working conditions;

[0011] Generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired sampling data and charge and discharge current working condition data;

[0012] Predicting the temperature rise safety margin of the battery cell according to a temperature rise rate fitting curve;

[0013] Record the number of times the battery cell exceeds the temperature rise safety limit;

[0014] Whether to issue an early warning is determined based on the temperature rise rate fitting curve and the recorded number of times the battery cell exceeds the temperature rise safety boundary.

[0015] Furthermore, the sampled data includes temperature data and voltage data, which are acquired through a cell sampling chip disposed between two adjacent cell cells.

[0016] Furthermore, the charge and discharge current operating condition data includes current data corresponding to the SOC and temperature of the charging and discharging operating conditions.

[0017] Furthermore, generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired temperature data and charge and discharge current working condition data includes:

[0018] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds;

[0019] By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC , Represents the SOC value.

[0020] Furthermore, the temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

[0021] Furthermore, the temperature rise safety margin of the battery cell is predicted based on the temperature rise rate fitting curve, including: fitting the obtained temperature rise rate curve Improved to the corresponding derivative curve, that is, the second temperature rise rate fitting curve ; According to the second temperature rise rate fitting curve , estimate the internal resistance of the battery cell, and obtain the maximum and minimum heating values of the battery cell during its normal life cycle through the internal resistance value, thereby obtaining the temperature rise safety margin of the battery cell.

[0022] Furthermore, determining whether to issue an early warning based on the temperature rise rate fitting curve and the recorded number of times the battery cell exceeds the temperature rise safety limit includes:

[0023] The frequency of the battery cell exceeding the temperature rise safety boundary is screened based on the second temperature rise rate fitting curve and the recorded number of times the battery cell exceeds the temperature rise safety boundary. ; and compare the voltage data of the battery cell, when the frequency When the voltage exceeds the preset threshold F or the voltage data exceeds the safety limit, a thermal runaway warning signal is issued.

[0024] A method for dynamically optimizing the temperature rise safety margin of a battery cell throughout its entire life cycle, comprising:

[0025] Acquiring sampling data of the battery cell, wherein the sampling data at least includes temperature data;

[0026] Obtain charging and discharging current data under different working conditions;

[0027] Generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired sampling data and charge and discharge current working condition data;

[0028] The above steps are repeated multiple times, and the temperature rise rate fitting curves are classified and sorted to determine the temperature rise safety boundary value of the battery cell.

[0029] Furthermore, the sampled data includes temperature data and voltage data, which are acquired through a cell sampling chip disposed between two adjacent cell cells.

[0030] Furthermore, the charge and discharge current operating condition data includes current data corresponding to the SOC and temperature of the charging and discharging operating conditions.

[0031] Furthermore, generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired temperature data and charge and discharge current working condition data includes:

[0032] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds;

[0033] By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC , x Represents the SOC value.

[0034] Furthermore, the temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

[0035] Furthermore, the method for classifying and sorting the temperature rise rate fitting curve includes at least one of the following: a neural network algorithm and a machine learning algorithm.

[0036] A method for optimizing a temperature rise rate curve of a battery cell during its entire life cycle, comprising:

[0037] Acquiring sampling data of the battery cell, wherein the sampling data at least includes temperature data;

[0038] Obtain charging and discharging current data under different working conditions;

[0039] The temperature rise rate of the battery cell is obtained based on the acquired sampling data and charge and discharge current condition data;

[0040] The temperature rise rate and SOC data are fitted to obtain a temperature rise rate fitting curve.

[0041] Furthermore, the sampled data includes temperature data and voltage data, which are acquired through a cell sampling chip disposed between two adjacent cell cells.

[0042] Furthermore, the charge and discharge current operating condition data includes current data corresponding to the SOC and temperature of the charging and discharging operating conditions.

[0043] Furthermore, generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired temperature data and charge and discharge current working condition data includes:

[0044] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds;

[0045] By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC , x Represents the SOC value.

[0046] Furthermore, the temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

[0047] Furthermore, the data fitting method includes at least one of the following: a linear regression method and a polynomial regression method.

[0048] A thermal runaway early warning system for the entire battery life cycle, including:

[0049] A judgment module, used to judge the direction of current;

[0050] Data storage module, storing charging and discharging current data under different working conditions;

[0051] A sampling module, configured to obtain sampling data of the battery cell, wherein the sampling data includes at least temperature data;

[0052] A data analysis module is used to generate a temperature rise rate fitting curve of the battery cell under different working conditions based on the acquired sampling data and charge and discharge current working condition data;

[0053] A prediction calculation module, configured to predict the temperature rise safety margin of the battery cell based on a temperature rise rate fitting curve;

[0054] A counting module, used to record the number of times the battery cell exceeds the temperature rise safety limit;

[0055] The early warning module is used to determine whether to issue an early warning based on the temperature rise rate fitting curve and the data recorded by the counting module.

[0056] A dynamic optimization system for the temperature rise safety margin of battery cells throughout the battery life cycle, including:

[0057] Virtual battery module, simulating the temperature sensing position of battery and battery cell;

[0058] Data storage module, storing battery charge and discharge condition data;

[0059] A sampling module, configured to obtain sampling data of the battery cell, wherein the sampling data includes at least temperature data;

[0060] A data analysis module is used to obtain the temperature rise rate of the battery cell based on the acquired sampling data and the charge and discharge current operating condition data; and to fit the temperature rise rate and the SOC data to obtain a temperature rise rate fitting curve;

[0061] The prediction calculation module is used to predict the temperature rise safety margin of the battery cell according to the temperature rise rate fitting curve.

[0062] A system for optimizing the temperature rise rate curve of a battery cell during its entire life cycle, comprising:

[0063] Virtual battery module, simulating the temperature sensing position of battery and battery cell;

[0064] Data storage module, storing battery charge and discharge condition data;

[0065] A sampling module, configured to obtain sampling data of the battery cell, wherein the sampling data includes at least temperature data;

[0066] The data analysis module is used to obtain the temperature rise rate of the battery cell based on the acquired sampling data and charge and discharge current operating condition data; and to fit the temperature rise rate and SOC data to obtain a temperature rise rate fitting curve.

[0067] A computer device comprises one or more processors; and a memory for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the steps of the above-mentioned method for thermal runaway early warning over the entire life cycle of a battery, or the steps of the above-mentioned method for dynamically optimizing the temperature rise safety margin of a battery cell over the entire life cycle of a battery, or the steps of the above-mentioned method for optimizing the temperature rise rate curve of a battery cell over the entire life cycle of a battery.

[0068] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of the above-mentioned method for thermal runaway warning over the entire life cycle of a battery, or the steps of the above-mentioned method for dynamically optimizing the temperature rise safety margin of a battery cell over the entire life cycle of the battery, or the steps of the above-mentioned method for optimizing the temperature rise rate curve of a battery cell over the entire life cycle of the battery.

[0069] The beneficial effects of the present invention are:

[0070] The present invention provides a battery life cycle early warning method, boundary and rate curve optimization method, system, equipment and medium. By calculating the temperature rise rate fitting curve under different lifespans, different capacities and different operating conditions, the maximum temperature rise safety boundary is obtained, and the temperature rise safety boundary is dynamically adjusted to achieve a dynamic optimization process of the thermal runaway warning value, thereby improving the accuracy of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of a thermal runaway early warning method for the entire life cycle of a battery provided by the present invention;

[0072] Figure 2 This is a schematic diagram of the sampling of the battery cell sampling chip provided by the present invention;

[0073] Figure 3 This is a temperature rise curve diagram of charging conditions over a period of time provided by the present invention;

[0074] Figure 4This is a temperature rise curve diagram of the discharge condition within a period of time provided by the present invention;

[0075] Figure 5 This is a flowchart of a thermal runaway early warning method for a battery throughout its life cycle provided by the present invention;

[0076] Figure 6 This is a flow chart for calculating the temperature rise safety margin provided by the present invention. DETAILED DESCRIPTION

[0077] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited to the following embodiments.

[0078] Example 1:

[0079] See attached Figure 1-6 This embodiment provides a thermal runaway warning method for the entire life cycle of a battery. Figure 1 Shown, including:

[0080] S1, obtaining sampling data of the battery cell, the sampling data including at least temperature data;

[0081] Specifically, a cell sampling chip is arranged between two adjacent cells, and the cell sampling chip can measure the temperature data and voltage data of the cell at that location. Then the temperature data of the cell sampling chip is collected through the sampling system. The sampling system is as follows: Figure 2 After the sampling system is working, each cell sampling chip is numbered and sorted. For example, the cell sampling chip position numbers are .

[0082] S2, obtaining charging and discharging current operating condition data under different operating conditions;

[0083] Specifically, pre-enter the charge and discharge current data for different operating conditions. This data includes the current data corresponding to the SOC and temperature for both the charging and discharging conditions. This data can be provided as a charging condition table and a discharging condition table. The condition tables represent the current at different SOCs and temperatures. Table 1 shows a partial charging condition table, and Table 2 shows a discharging condition table.

[0084] Table 1 Charging Conditions

[0085]

[0086] Table 2 Discharge Conditions

[0087]

[0088] S3, generating a temperature rise rate fitting curve of the battery cell under different operating conditions based on the acquired sampling data and the charge and discharge current operating condition data;

[0089] Specifically, the sampling system can present the temperature rise curve of the battery cell under different charging and discharging conditions, such as Figure 3 and Figure 4 As shown, Figure 3 and Figure 4 The temperature rise curves of three battery sample chips under charging and discharging conditions are shown respectively, where: Figure 3 and Figure 4 The five curves are the SOC curve, current curve, temperature rise curve of battery cell sampling chip one, temperature rise curve of battery cell sampling chip two and temperature rise curve of battery cell sampling chip three from top to bottom, where the horizontal axis is time, and the vertical axes of the five curves are real-time SOC value, real-time current value, temperature value of battery cell sampling chip one, temperature value of battery cell sampling chip two and temperature value of battery cell sampling chip three.

[0090] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds; among them, the temperature rise rate The calculation formula is:

[0091]

[0092] in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 at different SOC; the internal resistance of each battery cell is different at different SOC, and will change with life attenuation, so it can be used To characterize the internal resistance of the battery cell at the corresponding sampling chip position.

[0093] Will As part of the basic data, based on the charge and discharge current condition data called, the temperature rise rate of the battery cell at different SOC in the range of 0%~100% can be obtained. .

[0094] By obtaining several temperature rise rate values, a continuous temperature rise rate fitting curve corresponding to SOC can be fitted. , Represents the SOC value. Temperature rise rate fitting curve The calculation formula is: ,in, 、 、 、 represents the unknown coefficient, which can be obtained by the least square method; Represents a fixed constant, representing the initial internal resistance of the battery when it leaves the factory. 、 、 、 Expressed as SOC value, this formula can be used to fit the temperature rise rate The relationship between it and SOC.

[0095] Furthermore, according to the relationship between current and temperature, different current values can be obtained. For example, at 25°C, the discharge current is 100A in the SOC range of 20% to 80%, and the continuous charging time is 30 minutes. At this time, ∆SOC is 60%. According to the heat formula , due to the derivation between heat and time, the temperature rise rate can be further fitted into the curve Improved to the corresponding derivative curve, which is called the second temperature rise rate fitting curve , so that the corresponding second temperature rise rate can be obtained at any SOC In particular, in each cycle, a corresponding second temperature rise rate fitting curve can be formed. , forming the second temperature rise rate fitting curve for each cycle of the battery life cycle data records.

[0096] S4, fitting curve according to the second temperature rise rate Predict the temperature rise safety margin of the battery cell;

[0097] Thus, during the battery life cycle, the internal resistance of the SOC range of 0-100% in each cycle is changing, combined with the obtained second temperature rise rate fitting curve , we can estimate the corresponding internal resistance value, and use the internal resistance value to get the maximum and minimum heat generation during the normal life cycle of the battery, and predict the temperature rise safety margin of the sampling chip location. Specifically, The calculation method is: ,in, is the SOC value, and I is the current value under the corresponding SOC.

[0098] Repeat the above steps and the second temperature rise rate fitting curve of each sampling chip is obtained. , Characterize the corresponding battery cell sampling chip number, , can be calculated for different SOC and different cells ,Will Bring it into the software and use SQL statements to find the corresponding temperature rise safety boundary.

[0099] SQL statements can be defined by artificially defining the number of times used as a condition. For example, define the battery fully charged and discharged 10 times, 100 times, 1000 times, and 10,000 times as boundary conditions, classify and sort the data in the database based on SQL, and define every 10 times, every 100 times, etc. The temperature value corresponding to the maximum value is A, and the temperature value corresponding to the minimum value is B. Since the second temperature rise rate fitting curve of each cycle is The boundary value may change dynamically with the second temperature rise rate fitting curve recorded. Changes in the battery lifecycle also produce corresponding changes. That is, the boundary values are naturally updated and iterated over the battery lifecycle, facilitating full lifecycle monitoring of the battery. It should be understood that the defined conditions can be dynamically adjusted and set based on big data. For example, the maximum value A and minimum value B can be adjusted based on seasonal ambient temperature changes, battery life, industry mandatory standards, national safety regulations, and other factors.

[0100] S5, records the number of times the battery cell temperature exceeds the temperature rise safety limit;

[0101] Specifically, the temperature rise safety margins of the battery cells at different positions are input, and each time the temperature rise safety margin is exceeded, the number of times is increased by one.

[0102] S6: Determine whether to issue a warning based on the second temperature rise rate fitting curve and the recorded number of times the battery cell exceeds the temperature rise safety limit.

[0103] Specifically, according to the second temperature rise rate fitting curve The data of each battery cell exceeding the temperature rise safety limit can be recorded, and the frequency of battery cells at different locations exceeding the temperature rise safety limit can be filtered through the Pandas algorithm. In the Pandas algorithm, data screening can be achieved through conditional indexing. For example, the DataFrame and Series objects provided in Pandas both support Boolean indexing. And compare the collected battery cell voltage data. When the frequency When the voltage exceeds the preset threshold F or exceeds the safety limit, a thermal runaway warning signal is issued. Among them, F is a custom threshold. For example, the total number of sampling times of the current battery sampling chip is 100. If the abnormal state during the period If it exceeds 10%, it is defined as abnormal; the safety margin of the above battery cell voltage can be the factory setting value.

[0104] Example 2:

[0105] See attached Figure 1-6 Based on the first embodiment, this embodiment further provides a method for dynamically optimizing the temperature rise safety margin of a battery cell during its entire life cycle, including:

[0106] Acquiring sampling data of the battery cell, wherein the sampling data at least includes temperature data;

[0107] Obtain charging and discharging current data under different working conditions;

[0108] Generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the acquired sampling data and charge and discharge current working condition data;

[0109] Repeat the above steps multiple times, and classify and sort the second temperature rise rate fitting curve to determine the temperature rise safety boundary value of the battery cell.

[0110] In some embodiments, the sampled data includes temperature data and voltage data, which are acquired by a cell sampling chip disposed between two adjacent cell cells.

[0111] In some embodiments, the charge and discharge current condition data includes current data corresponding to the SOC and temperature of the charging condition and the discharging condition.

[0112] In some embodiments, generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different operating conditions based on the acquired temperature data and charge and discharge current operating condition data includes:

[0113] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds;

[0114] By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC , x Represents the SOC value.

[0115] In some embodiments, the temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

[0116] In some embodiments, the method for classifying and sorting the temperature rise rate fitting curve includes at least one of the following: a neural network algorithm and a machine learning algorithm.

[0117] Example 3:

[0118] See attached Figure 1-6Based on the second embodiment, this embodiment further provides a method for optimizing the temperature rise rate curve of a battery cell during its entire life cycle, including:

[0119] Acquiring sampling data of the battery cell, wherein the sampling data at least includes temperature data;

[0120] Obtain charging and discharging current data under different working conditions;

[0121] The temperature rise rate of the battery cell is obtained based on the acquired sampling data and charge and discharge current condition data;

[0122] The temperature rise rate and SOC data are fitted to obtain a temperature rise rate fitting curve.

[0123] In some embodiments, the sampled data includes temperature data and voltage data, which are acquired by a cell sampling chip disposed between two adjacent cell cells.

[0124] In some embodiments, the charge and discharge current condition data includes current data corresponding to the SOC and temperature of the charging condition and the discharging condition.

[0125] In some embodiments, generating a temperature rise rate fitting curve of the charge and discharge of the battery cell under different operating conditions based on the acquired temperature data and charge and discharge current operating condition data includes:

[0126] According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Represents different moments in seconds;

[0127] By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC , x Represents the SOC value.

[0128] The temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

[0129] In some embodiments, the data fitting method includes at least one of the following: a linear regression method and a polynomial regression method.

[0130] Example 4:

[0131] See attached Figure 1-6 Based on the first embodiment, this embodiment further provides a thermal runaway warning system for the entire life cycle of a battery, including:

[0132] A judgment module, used to judge the direction of current;

[0133] Data storage module, storing charging and discharging current data under different working conditions;

[0134] A sampling module is used to obtain sampling data of the battery cell, where the sampling data includes at least temperature data;

[0135] The data analysis module is used to generate a temperature rise rate fitting curve of the battery cell under different working conditions through software self-learning based on the acquired sampling data and charge and discharge current working condition data;

[0136] Prediction calculation module, used to predict the temperature rise safety margin of the battery cell based on the temperature rise rate fitting curve;

[0137] A counting module is used to record the number of times the battery cell exceeds the temperature rise safety limit;

[0138] The early warning module is used to determine whether to issue an early warning based on the temperature rise rate fitting curve and the data recorded by the counting module.

[0139] like Figure 5 As shown, the process of using the thermal runaway warning system provided by this embodiment to provide warning is as follows:

[0140] The system works and uses the MCU (microcontroller unit) to determine the direction of the current in real time and whether the current battery cell is in a discharge condition or a charge condition. If it is in a discharge condition, the discharge parameters are retrieved; if it is in a charge condition, the charging parameters are retrieved.

[0141] At the same time, the system continuously collects temperature-rise data through the data sampling module. This data is then sent to the software's self-learning module for internal analysis and processing. During the software self-learning phase, the system uses this data to predict the safe temperature-rise margin under the current operating conditions and stores this information in the MCU.

[0142] The system also records the number of times each data point exceeds the temperature rise safety limit. When the system detects a data point more frequently than a preset threshold F or the voltage data exceeds the safety limit, a thermal runaway warning signal is issued.

[0143] To sum up, the early warning system realizes real-time monitoring and early warning of the system security status through real-time data collection, software self-learning, big data prediction and trend analysis, which can greatly improve the stability and reliability of the system and ensure the safety of users.

[0144] Embodiment 5:

[0145] See attached Figure 1-6 Based on the second embodiment, this embodiment further provides a dynamic optimization system for the temperature rise safety margin of the battery cell during the entire life cycle of the battery, including:

[0146] Virtual battery module, simulating the temperature sensing position of battery and battery cell;

[0147] Data storage module, storing battery charge and discharge condition data;

[0148] A sampling module, configured to obtain sampling data of the battery cell, wherein the sampling data includes at least temperature data;

[0149] A data analysis module is used to obtain the temperature rise rate of the battery cell based on the acquired sampling data and the charge and discharge current operating condition data; and to fit the temperature rise rate and the SOC data to obtain a temperature rise rate fitting curve;

[0150] The prediction calculation module is used to predict the temperature rise safety margin of the battery cell according to the temperature rise rate fitting curve.

[0151] Example 6:

[0152] See attached Figure 1-6 Based on the third embodiment, this embodiment further provides a system for optimizing the temperature rise rate curve of a battery cell during its entire life cycle, including:

[0153] Virtual battery module, simulating the temperature sensing position of battery and battery cell;

[0154] Data storage module, storing battery charge and discharge condition data;

[0155] A sampling module, configured to obtain sampling data of the battery cell, wherein the sampling data includes at least temperature data;

[0156] The data analysis module is used to obtain the temperature rise rate of the battery cell based on the acquired sampling data and charge and discharge current operating condition data; and to fit the temperature rise rate and SOC data to obtain a temperature rise rate fitting curve.

[0157] Embodiment seven:

[0158] See attached Figure 1-6 This embodiment also provides a computer device, comprising one or more processors; a memory for storing one or more programs, which, when executed by the one or more processors, causes the one or more processors to execute the steps of the thermal runaway early warning method for the entire life cycle of a battery in any embodiment of the present invention, or the steps of the method for dynamically optimizing the temperature rise safety margin of a battery cell during the entire life cycle of a battery, or the steps of the method for optimizing the temperature rise rate curve of a battery cell during the entire life cycle of a battery.

[0159] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can implement the steps of the thermal runaway warning method for the entire life cycle of a battery in any embodiment of the present invention, or the steps of the dynamic optimization method for the temperature rise safety boundary of a battery cell during the entire life cycle of the battery, or the steps of the optimization method for the temperature rise rate curve of a battery cell during the entire life cycle of the battery.

[0160] Specifically, all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it executes the steps of the above-mentioned method embodiment; and the storage medium may include: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0161] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A thermal runaway early warning method for the entire life cycle of a battery, characterized in that: include: Enter the charge and discharge current and temperature data of the battery cells under different working conditions in advance; Generate a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the temperature data and the charge and discharge current working condition data , Represents the SOC value; Fitting curve according to temperature rise rate , get the second temperature rise rate fitting curve ; In each charge and discharge cycle of the battery cell's entire life cycle, a corresponding second temperature rise rate fitting curve is formed , as data record; according to the second temperature rise rate fitting curve ,get , is the current value under the corresponding SOC; Define the number of charge and discharge cycles of the battery as a condition, divide the entire life cycle of the battery into multiple intervals according to the preset number of times, classify and sort the data in the database based on SQL, and obtain the number of cycles in each interval. The maximum and minimum values of the temperature rise rate are defined as the temperature rise safety boundary of each interval; the temperature rise safety boundary is fitted with the second temperature rise rate curve Changes in the battery cell's life cycle; Obtain sampling data of the battery cell, the sampling data including at least temperature data, record the number of times the temperature of the battery cell exceeds the temperature rise safety boundary, and fit the curve according to the second temperature rise rate of the battery cell The number of times the battery cell exceeds the temperature rise safety limit is recorded to determine whether to issue an early warning.

2. The thermal runaway early warning method for the entire life cycle of a battery according to claim 1, characterized in that: The sampled data includes temperature data and voltage data, which are obtained through a cell sampling chip arranged between two adjacent cell cells.

3. The thermal runaway early warning method for the entire life cycle of a battery according to claim 2, characterized in that: The charge and discharge current operating condition data includes the SOC of the charging condition and the discharging condition, and the current data corresponding to the ambient temperature.

4. The method for early warning of thermal runaway of a battery throughout its life cycle according to claim 3, characterized in that: Generate a temperature rise rate fitting curve of the charge and discharge of the battery cell under different working conditions based on the temperature data and the charge and discharge current working condition data ,include: According to the acquired temperature data, the temperature rise rate of the battery cell at different times and different SOCs is calculated. , Different moments, in seconds; By calling the charge and discharge current operating condition data, the temperature rise rate of the battery cell at different SOC is calculated , and fitted into a continuous temperature rise rate fitting curve corresponding to SOC .

5. The method for early warning of thermal runaway of a battery throughout its life cycle according to claim 4, characterized in that: The temperature rise rate The calculation formula is: ,in, and Represent different moments. and Respectively represent the temperature of the battery cell corresponding to SOC1 and SOC2 under different SOCs.

6. The method for early warning of thermal runaway of a battery throughout its life cycle according to claim 5, characterized in that: The second temperature rise rate fitting curve of the battery cell The number of times the battery cell exceeds the temperature rise safety limit is recorded to determine whether to issue an early warning, including: According to the second temperature rise rate fitting curve The number of times the temperature of the battery cell exceeds the temperature rise safety boundary is recorded, and the frequency of the temperature of the battery cell exceeding the temperature rise safety boundary is screened When the frequency When it is greater than the preset threshold F, a thermal runaway warning signal is issued.

7. A computer device, characterized in that: including one or more processors; A memory for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to perform the steps of the battery full life cycle thermal runaway warning method as described in claim 1.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the battery full life cycle thermal runaway early warning method according to claim 1 are implemented.

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