Battery pack temperature monitoring method and system

Abstract

The invention discloses a battery pack temperature monitoring method and system, and belongs to the technical field of batteries, and the method comprises the steps: obtaining the surface temperature of the surfaces of a plurality of single batteries; screening the abnormal surface temperature as an outlier temperature through an aging model and an influence index, and obtaining a target single battery corresponding to the outlier temperature; based on the surface temperature and a pre-established battery thermal model, acquiring the internal temperature of the target battery monomer; the battery thermal model is configured to perform prediction based on Kalman filtering, and the Kalman filtering is configured to perform correction based on a genetic algorithm; based on the outlier temperature and the internal temperature, obtaining the temperature difference of the target battery monomer; and in response to the temperature difference exceeding the preset threshold value, sending out early warning information of temperature abnormity of the target battery monomer. Therefore, the surface temperature and the internal temperature of the battery monomer are respectively obtained, and whether the temperature of the battery monomer is abnormal is judged based on the temperature difference between the surface temperature and the internal temperature, so that the temperature monitoring precision of the battery pack is improved, and the operation safety of the battery pack is guaranteed.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of battery technology, and in particular to a method and system for monitoring battery pack temperature. Background Art

[0002] Temperature is one of the important factors affecting the normal operation of the battery pack. Therefore, the battery management system is required to monitor the temperature of the battery cells in the battery pack.

[0003] At present, battery management systems usually perform temperature monitoring based on temperature sensors. However, temperature sensors can only obtain the surface temperature of battery cells, but cannot obtain the internal temperature with a higher temperature value, resulting in low temperature monitoring accuracy of battery packs, affecting the safety of battery packs. Summary of the invention

[0004] An embodiment of the present application provides a method for monitoring the temperature of a battery pack to solve the technical problem in the prior art that it is impossible to obtain an internal temperature with a higher temperature value, resulting in low accuracy in temperature monitoring of the battery pack and affecting the safety of the battery pack; an embodiment of the present application also provides a system for monitoring the temperature of a battery pack.

[0005] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0006] In a first aspect, a method for monitoring the temperature of a battery pack is provided, for monitoring the temperatures of a plurality of battery cells in a battery pack, the monitoring method comprising:

[0007] Acquiring the surface temperature of the surfaces of the plurality of battery cells;

[0008] The abnormal surface temperature is screened as an outlier temperature through a pre-established aging model and an influence index, and a target battery cell corresponding to the outlier temperature is obtained;

[0009] Based on the surface temperature and a pre-established battery thermal model, an internal temperature of the target battery cell is acquired; the battery thermal model is configured to make predictions based on a Kalman filter, and the Kalman filter is configured to make corrections based on a genetic algorithm;

[0010] Based on the outlier temperature and the internal temperature, obtaining a temperature difference of the target battery cell;

[0011] In response to the temperature difference exceeding a preset threshold, a warning message indicating that the temperature of the target battery cell is abnormal is issued.

[0012] In combination with the first aspect, the step of screening the abnormal surface temperature as an outlier temperature through a pre-established aging model and an influence index, and obtaining a target battery cell corresponding to the outlier temperature includes:

[0013] Eliminating invalid values ​​in the surface temperature;

[0014] Based on the aging model and the impact index, obtaining a temperature threshold;

[0015] In response to the surface temperature being higher than the temperature threshold, the surface temperature is calibrated as the outlier temperature, and the battery cell corresponding to the outlier temperature is determined as the target battery cell.

[0016] In combination with the first aspect, the aging model is established based on the following formula:

[0017]

[0018] Among them, Q loss is the battery capacity loss rate, in %; E a is the activation energy of the battery cell, in J*mol -1 ; exp() is the natural exponential function; η is the compensation coefficient; is the power law factor; I c is the discharge rate, in h -1 ; R gas is the gas constant, in J*(mol*K) -1 ; α and β are fitting constants; T s is the surface temperature; SOC is the state of charge of the battery cell; AH is the ampere-hour throughput.

[0019] In combination with the first aspect, the impact index is established based on the following formula:

[0020]

[0021] Where, Γ is the actual ampere-hour throughput; Γ nom is the actual battery life; I nom is the battery current under the calibration conditions, in A; EOL is the time when the battery cell reaches the end of its life, in h; I is the battery current, in A; T s is the surface temperature; SOC is the state of charge of the battery cell.

[0022] In combination with the first aspect, the step of removing invalid values ​​from the surface temperature includes:

[0023] Obtaining an initial maximum value, an initial minimum value, and an initial average value among a plurality of the surface temperatures;

[0024] In response to a sampling failure of the temperature sensor for acquiring the surface temperature, discarding the surface temperature acquired by the temperature sensor;

[0025] In response to satisfying a first preset condition, starting a rejection step;

[0026] In response to satisfying a second preset condition, the corresponding invalid value is eliminated; the first preset condition and the second preset condition are set based on the initial maximum value, the initial minimum value and the aging model.

[0027] In combination with the first aspect, in response to satisfying the second preset condition, the step of removing the corresponding invalid value includes:

[0028] In response to the difference between the initial maximum value and the initial average value being greater than or equal to a first threshold, and the difference between the initial average value and the initial minimum value being less than or equal to a second threshold, or the difference between the initial maximum value and the initial average value being greater than a third threshold, removing the initial maximum value;

[0029] In response to the difference between the initial average value and the initial minimum value being greater than or equal to the first threshold, and the difference between the initial maximum value and the initial average value being less than or equal to the second threshold, or the difference between the initial average value and the initial minimum value being greater than the third threshold, eliminating the minimum value;

[0030] Obtaining a first difference between the surface temperature greater than or equal to a fourth threshold and the initial average value, and eliminating the surface temperature whose first difference is greater than or equal to the first threshold;

[0031] A second difference between the surface temperature that is less than or equal to a fifth threshold and the initial average value is obtained, and the surface temperature whose second difference is greater than or equal to the first threshold is eliminated.

[0032] In combination with the first aspect, the step of acquiring the internal temperature of the target battery cell based on the surface temperature and a pre-established battery thermal model includes:

[0033] Establishing the battery thermal model based on the heat generation process and heat transfer process of the battery cell;

[0034] Iterating the genetic algorithm to update the genetic algorithm;

[0035] Correcting filtering parameters of the Kalman filter based on the genetic algorithm;

[0036] Based on the outlier temperature and the Kalman filter, the battery thermal model is predicted to obtain the internal temperature.

[0037] In combination with the first aspect, the step of correcting the filtering parameters of the Kalman filter based on the genetic algorithm includes:

[0038] Using the filtering parameters as the genotype of the genetic algorithm to define parameter coding;

[0039] Initializing the genetic algorithm to create an initial population, wherein the initial population includes a plurality of groups of configurations of the filtering parameters;

[0040] defining a fitness function, and evaluating the effectiveness of the filtering parameters based on the fitness function;

[0041] Iterating the genetic algorithm based on the effectiveness;

[0042] Based on the iterative genetic algorithm, the filtering parameters are corrected.

[0043] In combination with the first aspect, the battery thermal model is pre-established based on the following formula:

[0044]

[0045] Among them, C s is the shell specific heat capacity of the battery cell; is the change of the surface temperature over time; T s is the surface temperature; T c is the internal temperature; T amb R is the ambient temperature of the battery cell; s is the equivalent thermal resistance of the part of the shell in contact with the air; R c is the internal equivalent thermal resistance of the battery cell.

[0046] In a second aspect, a battery pack temperature monitoring system is provided, which is used to execute the battery pack temperature monitoring method as described in any one of the first aspects to monitor the temperatures of multiple battery cells in the battery pack, and the monitoring system includes:

[0047] A first acquisition module is configured to acquire surface temperatures of surfaces of the plurality of battery cells;

[0048] A screening module is configured to screen the abnormal surface temperature as an outlier temperature through a pre-established aging model and an influence index, and obtain a target battery cell corresponding to the outlier temperature;

[0049] a second acquisition module, configured to acquire the internal temperature of the target battery cell based on the surface temperature and a pre-established battery thermal model; the battery thermal model is configured to perform prediction based on a Kalman filter, and the Kalman filter is configured to perform correction based on a genetic algorithm;

[0050] a third acquisition module configured to acquire a temperature difference of the target battery cell based on the outlier temperature and the internal temperature;

[0051] The early warning module is configured to issue an early warning message indicating that the temperature of the target battery cell is abnormal in response to the temperature difference exceeding a preset threshold.

[0052] One of the above technical solutions has the following advantages or beneficial effects:

[0053] Compared with the prior art, the battery pack temperature monitoring method provided by the present application is used to monitor the temperature of multiple battery cells in the battery pack, and the monitoring method includes: obtaining the surface temperature of the surfaces of multiple battery cells; screening the abnormal surface temperature as the outlier temperature through the pre-established aging model and influence index, and obtaining the target battery cell corresponding to the outlier temperature; obtaining the internal temperature of the target battery cell based on the surface temperature and the pre-established battery thermal model; the battery thermal model is configured to predict based on the Kalman filter, and the Kalman filter is configured to correct based on the genetic algorithm; based on the outlier temperature and the internal temperature, the temperature difference of the target battery cell is obtained; in response to the temperature difference exceeding the preset threshold, an early warning message of abnormal temperature of the target battery cell is issued. In this way, by respectively obtaining the surface temperature and the internal temperature of the battery cell, and judging whether the temperature of the battery cell is abnormal based on the temperature difference between the two, the temperature monitoring accuracy of the battery pack is improved, and the operation safety of the battery pack is guaranteed.

[0054] The battery pack temperature monitoring system provided in the present application has all the technical features and beneficial effects of the above-mentioned battery pack temperature monitoring method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0056] Figure 1 A schematic diagram of a first process flow of a method for monitoring battery pack temperature provided in an embodiment of the present application;

[0057] Figure 2 A second flow chart of the method for monitoring the temperature of a battery pack provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the influence index of the battery pack temperature monitoring method provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the relationship between the starting temperature and the thermal runaway triggering temperature of the battery pack temperature monitoring method provided in an embodiment of the present application;

[0060] Figure 5 A schematic diagram of obtaining outlier temperature of a battery pack temperature monitoring method provided in an embodiment of the present application;

[0061] Figure 6 A schematic diagram of the structure of a battery pack temperature monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. In the description of the present application, it is necessary to understand that the orientation or position relationship indicated by the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0063] The applicant has found that, currently, the accuracy of the electrochemical model depends on the accuracy of the simulation model, so it has many calibration parameters and low confidence. At the same time, the simulation calculation process is cumbersome and inefficient, which is not conducive to rapid polarization analysis of batteries of different lengths.

[0064] To this end, the present application establishes a battery polarization analysis model based on the target battery. When designing a new battery, it is only necessary to bring the length information of the new battery into the battery polarization analysis model to perform polarization analysis on the new battery, so as to improve the efficiency of polarization analysis of batteries of different lengths.

[0065] The specific implementation methods of the present application are described below by way of examples:

[0066] See also Figure 1 and Figure 2 , Figure 1 A first flow chart of a method for monitoring battery pack temperature provided in an embodiment of the present application is illustrated; Figure 2 The second flow chart of the method for monitoring the temperature of a battery pack provided in an embodiment of the present application is shown. The present application provides a method for monitoring the temperature of a battery pack, which is used to monitor the temperature of multiple battery cells in a battery pack. The monitoring method includes the following steps 101 to 105 .

[0067] Step 101, obtaining the surface temperature of the surfaces of multiple battery cells. In the embodiment of the present application, the surface temperature refers to the temperature of the surface of the battery cell, which can be obtained by a temperature sensor, so as to timely detect the heat accumulation of the battery cell.

[0068] Step 102, the abnormal surface temperature is screened as an outlier temperature through the pre-established aging model and influence index, and the target battery cell corresponding to the outlier temperature is obtained. In the present application, the normal temperature change is distinguished from the abnormal temperature change by establishing an aging model and an influence index. The abnormal surface temperature indicates that the corresponding battery cell may have a fault or aging problem. Therefore, the present application first screens the outlier temperature and obtains the corresponding target battery cell, quickly detects and diagnoses the target battery cell that may have problems, and then further judges it in combination with the internal temperature, and then clarifies the battery cell that does have problems among the target battery cells, so as to take corresponding maintenance or replacement measures to improve the reliability and stability of the battery pack.

[0069] In one embodiment, the step 102 of screening abnormal surface temperatures as outlier temperatures through a pre-established aging model and influence index, and obtaining the target battery cell corresponding to the outlier temperature, includes: removing invalid values ​​from the surface temperature; obtaining a temperature threshold based on the aging model and the influence index; in response to the surface temperature being higher than the temperature threshold, calibrating the surface temperature as an outlier temperature, and determining the battery cell corresponding to the outlier temperature as the target battery cell. Specifically, temperature sensor failure, data acquisition error or other reasons can lead to abnormal data, i.e., invalid values. Therefore, before screening outlier temperatures, the present application needs to first remove invalid values ​​to eliminate the interference of invalid values ​​and ensure the accuracy of subsequent surface temperature analysis and judgment. Secondly, the aging model and influence index are established based on the characteristics and historical data of the battery cell. The obtained temperature threshold is used to determine whether the surface temperature is abnormal. Once the surface temperature is higher than the preset temperature threshold, it means that the surface temperature exceeds the normal range, and the corresponding battery cell may have a fault or aging problem. In this case, the surface temperature is calibrated as the outlier temperature, and the battery cells corresponding to the outlier temperature are determined as target battery cells. Further internal temperature evaluation is then performed on these target battery cells. In this way, misjudgment caused by invalid values ​​or normal surface temperatures can be avoided, thereby improving the accuracy of battery pack temperature monitoring in this application.

[0070] In one embodiment, the aging mechanism of the battery cell is very complex. In the embodiment of the present application, the cycle aging of the battery cell when the electrical equipment is working is considered, and the aging model is established based on the following formula:

[0071]

[0072] Among them, Q lossis the battery capacity loss rate, in %; E a is the activation energy of the battery cell, in J*mol -1 ; exp() is the natural exponential function; η is the compensation coefficient; is the power law factor; I c is the discharge rate, in h -1 ; R gas is the gas constant, in J*(mol*K) -1 ; α and β are fitting constants; T s is the surface temperature; SOC is the state of charge of the battery cell; AH is the ampere-hour throughput.

[0073] In one embodiment, the impact index is established based on the following formula:

[0074]

[0075] Among them, σ(I,T s , SOC) is the impact index, Γ is the actual ampere-hour throughput; Γ nom is the actual battery life; I nom is the battery current under the calibration conditions, in A; EOL is the time when the battery cell reaches the end of its life, in h; I is the battery current, in A; T s is the surface temperature; SOC is the state of charge of the battery cell.

[0076] The influence of the impression factor and the aging model on battery aging in this application is specifically described below.

[0077] It should be noted that, in the embodiment of the present application, the influencing factor and the ampere-hour flux that quantifies the degree of battery aging can also be used to determine the starting temperature for the surface temperature to begin to eliminate invalid values, so as to determine whether it is necessary to take measures such as power reduction to maintain the operation of the battery pack. For example, when the capacity loss of a battery cell reaches 20%, the battery cell life reaches the end. Therefore, the total ampere-hour that can be passed in the life cycle of the battery cell can be expressed as:

[0078]

[0079] Among them, I nim is the battery current under the calibration conditions, in A; EOL is the time when the battery cell reaches the end of its life, in h; Γ nim For actual battery life.

[0080] At this time, the standard working condition of the battery is: I c,nom =2.5C, SOC nom =0.35, T h,nom=25℃, EOL is the time when the battery reaches the end of its life, defined as the time when the capacity loss reaches 20% under nominal conditions. According to the battery SOC, I c , T s , the actual battery life can be expressed as:

[0081]

[0082] Where, Γ is the actual ampere-hour throughput; Γ nom is the actual battery life; I nom is the battery current under the calibration conditions, in A; EOL is the time when the battery cell reaches the end of its life, in h; I is the battery current, in A; T s is the surface temperature; SOC is the state of charge of the battery cell; α and β are fitting constants; R gas is the gas constant.

[0083] On this basis, taking into account the loss of effective life of the battery cell, the effective ampere-hour throughput AH eff (t) is expressed as:

[0084]

[0085] It can be seen that the effective ampere-hour throughput of a battery cell is directly related to the life of the battery cell, so it can be used to evaluate the degree of aging of the battery cell. Therefore, the impact index in this application is a function of current, temperature, and state of charge, as shown in the following figure, where the horizontal axis is the state of charge and current, and the vertical axis is the impact index. When the current and state of charge are determined, the higher the temperature, the higher the impact index, that is, the faster the battery cell ages, and the more likely it is to have safety hazards.

[0086] In one embodiment, the step of eliminating invalid values ​​in the surface temperature includes: obtaining an initial maximum value, an initial minimum value, and an initial average value from a plurality of surface temperatures; in response to a sampling failure of a temperature sensor for obtaining the surface temperature, eliminating the surface temperature collected by the temperature sensor; in response to satisfying a first preset condition, starting the elimination step; in response to satisfying a second preset condition, eliminating the corresponding invalid value; the first preset condition and the second preset condition are set based on the initial maximum value, the initial minimum value, and the aging model. It should be noted that the initial average value is the average value of the remaining surface temperatures after eliminating the initial maximum value and the initial minimum value.

[0087] In one embodiment, the first preset condition is: the temperature change of the battery cell is greater than a third threshold value, or the temperature change of the battery cell is greater than the difference between the initial maximum value and the initial average value; or, the initial maximum value is greater than or equal to the first threshold value, and the first threshold value is set based on the initial maximum value, the initial minimum value and the aging model; or, the initial minimum value is less than or equal to the second threshold value, and the second threshold value is based on the performance and charging efficiency of the battery cell at low temperature. Experimental measurement.

[0088] See also Figure 3 , Figure 4 and Figure 5 , Figure 3 A schematic diagram illustrating an influence index of a method for monitoring battery pack temperature provided in an embodiment of the present application; Figure 4 A schematic diagram illustrating the relationship between the starting temperature and the thermal runaway triggering temperature of the battery pack temperature monitoring method provided in an embodiment of the present application; Figure 5 The schematic diagram of obtaining outlier temperatures of the method for monitoring the temperature of a battery pack provided in an embodiment of the present application is illustrated. It can be understood that since the battery cells are aged faster due to cycling at higher temperatures, the electrolyte undergoes redox reactions and generates gas. The consumption of the electrolyte further causes the electrodes to dry up locally and the gas changes the distance between the electrodes. During the recharging process of the battery cells, the current is uneven, lithium precipitation occurs, and the thermal stability of the anode is reduced. In addition, the dissolution of transition metals and the release of lattice oxygen reduce the thermal stability of the cathode. Therefore, as the battery cells age, the thermal stability of the battery cells will decrease, which is reflected in the significant decrease in the self-heating starting temperature and the thermal runaway trigger temperature, such as Figure 3 As shown, Figure 3 (a) is a schematic diagram of the exponential function relationship at 35°C. Figure 3 (b) is a schematic diagram of the exponential function relationship at 45°C. Figure 3 (c) is a schematic diagram of the exponential function relationship at 55° C. Therefore, the present application needs to adjust the temperature threshold for starting the rejection step downward as the battery cell ages.

[0089] In one embodiment, in response to satisfying the second preset condition, the step of eliminating the corresponding invalid value includes: in response to the difference between the initial maximum value and the initial average value being greater than or equal to the first threshold, and the difference between the initial average value and the initial minimum value being less than or equal to the second threshold, or the difference between the initial maximum value and the initial average value being greater than the third threshold, eliminating the initial maximum value; in response to the difference between the initial average value and the initial minimum value being greater than or equal to the first threshold, and the difference between the initial maximum value and the initial average value being less than or equal to the second threshold, or the difference between the initial average value and the initial minimum value being greater than the third threshold, eliminating the minimum value; obtaining a first difference between the surface temperature greater than or equal to the fourth threshold and the initial average value, eliminating the surface temperature whose first difference is greater than or equal to the first threshold; obtaining a second difference between the surface temperature less than or equal to the fifth threshold and the initial average value, eliminating the surface temperature whose second difference is greater than or equal to the first threshold. Wherein, the above thresholds can be adaptively selected according to specific circumstances, for example, the first threshold can be 10°C, the second threshold can be 2°C, the third threshold can be 15°C, the fourth threshold can be 50°C, and the sixth threshold can be -20°C.

[0090] In addition, in order to further improve the accuracy of obtaining surface temperature and outlier temperature, the present application does not restore invalid value pairs before all surface temperatures do not meet the elimination conditions. Secondly, record the abnormal temperature in the above steps and the location information of the corresponding temperature sensor, and further evaluate the internal temperature of the battery cells in the area. If the internal temperature difference of the battery cells in the area is not large, the temperature is considered to be outlier; otherwise, the area has safety hazards and the temperature needs to be used for fault diagnosis and thermal management strategy execution. In addition, the eliminated temperature sensors (regardless of whether they are sampling line failures or temperature sensors corresponding to outlier temperatures) are not used for the diagnosis of over-temperature and under-temperature faults and thermal management logic; but the surface temperatures collected by these temperature sensors can still be sent to the CAN line for viewing. Finally, the battery management system obtains the initial maximum value, initial minimum value and initial average value among multiple surface temperatures based on the effective surface temperature, that is, the surface temperature after the elimination step is completed.

[0091] Step 103, based on the surface temperature and the pre-established battery thermal model, the internal temperature of the target battery cell is obtained; the battery thermal model is configured to make predictions based on the Kalman filter, and the Kalman filter is configured to make corrections based on the genetic algorithm. Specifically, the battery thermal model of the present application takes into account the heat generation and heat transfer processes of the battery cell, and can more accurately estimate the changes in the internal temperature. The prediction of the internal temperature can be further improved through the prediction of the Kalman filter. The accuracy and stability of the prediction can be further improved. At the same time, by using a genetic algorithm to correct the filter parameters of the Kalman filter, the genetic algorithm can search for the optimal combination of filter parameters, improve the adaptability and accuracy of the filter algorithm, and thus optimize the filter algorithm to adapt to the characteristics and actual conditions of the battery cell, reduce the filter error, and improve the accuracy and stability of the internal temperature prediction.

[0092] In one embodiment, step 103 of obtaining the internal temperature of the target battery cell based on the surface temperature and a pre-established battery thermal model includes: establishing a battery thermal model based on the heat generation and heat transfer processes of the battery cell; correcting the filter parameters of the Kalman filter based on a genetic algorithm; and predicting the battery thermal model based on the outlier temperature and the Kalman filter to obtain the internal temperature. Specifically, the present application predicts the internal temperature of the target battery cell by analyzing the heat generation and heat transfer processes of the battery cell, establishing a battery thermal model and using a Kalman filter, so as to combine the surface temperature and the internal temperature and monitor the heat accumulation of the battery in real time.

[0093] The process of establishing the battery thermal model is described below.

[0094] First, the total heat source of the battery cell is expressed as:

[0095] Q a =Q r +Q p +Q s +Q j (6);

[0096] Among them, Q a is the total heat source, Q r is the electrochemical polarization, Q p is concentration polarization, Q s Q is the energy loss caused by electrolyte decomposition and self-discharge side reactions, j It is Ohmic polarization.

[0097] For the theoretical calculation of heat generation, this application assumes that the battery cells are uniform and heat is generated and distributed uniformly in the battery cells. Based on this, a typical battery cell heat generation rate model is established:

[0098]

[0099] Where q is the heat generated by the battery cell; I is the charge and discharge current of the battery cell, which is positive when charging and negative when discharging; U is the battery cell voltage, OCV is the open circuit voltage of the battery cell; T s is the surface temperature, is the temperature coefficient; I×(U-OCV) is the polarization heat part, which is the sum of ohmic polarization and concentration polarization; the second half represents the reversible reaction heat part, which is the entropy change heat. The side reaction heat can be ignored in the calculation of heat generation under cycle conditions because the heat generation is very small.

[0100] Considering the present application, the heat transfer of battery cells is divided into: heat conduction, heat convection and heat radiation.

[0101] Among them, for heat conduction, the calculation formula for conduction thermal resistance can be obtained from the heat transfer calculation formula:

[0102] R = Δx / kA (8);

[0103] Where R is the conduction thermal resistance; Δx is the material thickness; k is the thermal conductivity coefficient; and A is the material area.

[0104] For thermal radiation, the general calculation formula for thermal radiation is:

[0105]

[0106] Among them, q radiation is thermal radiation, σ is the Stefan-Boltzmann constant, A1 is the area of ​​the radiating surface, ∈1 is the emissivity of the material, T1 is the surface temperature of the heat source, and T2 is the ambient temperature.

[0107] For thermal convection, the convection thermal resistance calculation formula is:

[0108] R = 1 / hA(10);

[0109] Wherein, R is the convection thermal resistance; h is the convection heat transfer coefficient; A is the surface area of ​​the battery cell.

[0110] On this basis, this application reduces the dimension of the battery cell according to the Biot number calculation rule, and the Biot number formula is:

[0111]

[0112] Among them, Bi is the Biot number, h is the convection heat transfer coefficient; V is the volume of the battery cell; (V / A) is a characteristic constant that characterizes the size of the solid; k is the equivalent thermal conductivity coefficient of the battery cell.

[0113] Taking a cylindrical battery cell as an example, the internal heat conduction rate of the battery cell is much greater than the heat dissipated by surface convection, that is, the temperature difference inside the battery cell is basically negligible, and the battery cell can be equated with a lumped mass model. At the same time, since the Biot number of the cylinder in the height and arc direction is much smaller than the radial Biot number, that is, the heat transfer capacity in the other two directions is much greater than the radial heat transfer, the three-dimensional cylindrical battery cell can be simplified to two dimensions, with heat transfer only in the radial direction.

[0114] According to the law of conservation of energy, the temperature rise inside the battery cell is equal to the difference between the heat generated by the battery cell at this moment and the heat transferred to the shell. The temperature change of the battery cell shell is equal to the difference between the heat transferred from the battery cell to the shell and the heat dissipated between the shell and the environment.

[0115]

[0116] Among them, C s is the specific heat capacity of the battery cell shell; is the change of battery surface temperature over time; T s is the surface temperature; T c is the internal temperature; T amb is the ambient temperature of the battery cell; R s is the equivalent thermal resistance of the battery cell shell in contact with the air; R c The equivalent thermal resistance inside the battery cell (the sum of the thermal resistances of all layers of materials in series). The following is a calculation for the unknown thermal resistance:

[0117] According to the laws of thermodynamics, the equivalent heat transfer coefficient of the battery cell is the parallel connection of the heat transfer coefficients of the various materials inside the battery cell:

[0118]

[0119] Among them, L i is the thickness of each layer of material in the battery cell; k T,i is the thermal conductivity of different materials; after rational analysis, the equivalent thermal resistance inside the battery cell is obtained as follows:

[0120]

[0121] Among them, R batt is the equivalent thermal resistance, L is the height of the battery cell, r2 is the outer diameter of the battery cell, and r1 is the inner diameter of the battery cell; k T,i is the thermal conductivity of different materials; similarly, the thermal resistance of the shell is:

[0122]

[0123] Among them, R shellis the thermal resistance of the shell; r2 is the outer diameter of the battery cell, r3 is the outer diameter of the shell; k shell is the thermal conductivity of the shell, and L is the height of the battery cell.

[0124] The calculation formula for the cell density and overall specific heat capacity of the battery cell is:

[0125]

[0126] Among them, ρ batt is the monomer density, C p,batt is the overall specific heat capacity, L i is the thickness of each layer of material, ρ i is the density of each layer of material, C p,i is the specific heat capacity of each layer of material.

[0127] In one embodiment, the steps of correcting the filtering parameters of the Kalman filter based on a genetic algorithm include: using the filtering parameters as the genotype of the genetic algorithm and defining the parameter encoding; initializing the genetic algorithm and creating an initial population, wherein the initial population includes multiple groups of filtering parameter configurations; defining a fitness function and evaluating the effectiveness of the filtering parameters based on the fitness function; iterating the genetic algorithm based on the effectiveness; and correcting the filtering parameters based on the iterated genetic algorithm.

[0128] The correction process of the genetic algorithm of the present application is described below.

[0129] First, the filter parameters are used as the genotype of the genetic algorithm to define the parameter encoding. Specifically, according to the characteristics of the genetic algorithm, the filter parameters of the Kalman filter are used as the genotype of the genetic algorithm, including: the covariance matrix Q of the process noise and the covariance matrix R of the observation noise. In addition, the unknown thermal parameters in the thermodynamic model can be used as state variables, including: the internal temperature T of the battery cell C , the surface temperature of the battery cell T S , the ambient temperature Tamb of the battery cell, the equivalent thermal resistance R of the part of the battery cell shell in contact with the air S , Equivalent thermal resistance R inside the battery cell C .

[0130] Therefore, the battery thermal model has 4 state variables and 1 output variable, and the process noise covariance matrix Q k and the observed sound covariance matrix R k They are recorded as:

[0131]

[0132] In order to simplify the genetic optimization calculation, it is assumed that the diagonal elements of the process noise matrix are equal, that is, q 11 =q 22 =q33 =944, then the parameter vector to be optimized can be expressed as x=(Q k , R k ) T .

[0133] The performance indicators constitute the corresponding fitness values. Based on the genetic algorithm, the digital string representing the filter parameters can be randomly selected for global optimization to achieve the purpose of obtaining the optimal system noise matrix and observation noise matrix.

[0134] Second, initialize the genetic algorithm and create an initial population. The initial population includes multiple groups of filter parameter configurations. Specifically, set the parameters of the genetic algorithm and create an initial population. The parameters include population size, genetic generation, crossover probability, and mutation probability. Each individual in the initial population represents a possible configuration of a set of Kalman filter parameters. For example, the battery pack is divided into N regions, that is, N populations are divided, and the size of each population is M, that is, M groups of solutions are randomly generated for each region, and each group of solutions has a different fitness for the battery thermal model, among which there is a group of solutions with the best fitness. Encode the characteristic traits of each possible configuration, and each coding group is a chromosome, that is, x=(Q k , R k ) T .

[0135] Third, define a fitness function and evaluate the effectiveness of the filter parameters based on the fitness function. Specifically, the purpose of optimizing the noise matrix of the Kalman filter in this application is to make the actual sampled data y(x3, k) and the filter output The mean square error is the smallest, and the objective function is defined as:

[0136]

[0137] in, is the mean square error between the calculated and estimated temperature values ​​of the i-th individual when inherited to the g-th generation; is the identification value of the i-th individual when inherited to the g-th generation; k is the heat conduction coefficient; M is the estimated length, that is, the population size. The larger M is, the wider the search range is, but the longer the genetic operation time of each generation is, and vice versa.

[0138] Fourth, based on effectiveness, the genetic algorithm is iterated. Specifically, first, a selection operation is performed to select better individuals to enter the next generation based on the fitness value of the individuals. Roulette selection, tournament selection and other methods can be used. Secondly, a crossover operation is performed to select a certain proportion of individuals and generate new offspring according to preset crossover rules, such as single-point crossover rules or multi-point crossover rules. Thirdly, a mutation operation is performed to randomly mutate some offspring to introduce new genetic diversity and prevent premature convergence. Finally, the filter parameters are updated to set the filter parameters corresponding to the individuals with the highest fitness in each generation as the current filter parameter values ​​of the Kalman filter.

[0139] Fifth, based on the iterative genetic algorithm, the filter parameters are corrected. Specifically, the Kalman filter structure based on genetic algorithm optimization is shown in the figure, which consists of two parts: extended Kalman filter and genetic optimization. k represents the battery thermal model noise covariance matrix, R k The Kalman filter is based on the measured values, inputs and selected filter parameters of the battery thermal model at each moment. k , R k The value is updated in time and measured to obtain the a posteriori estimate of the battery thermal model state at each moment. Then, according to the current filter parameter value and objective function value, the genetic algorithm is used to optimize the filter parameter to obtain the modified filter parameter Q k , R k The value is used as the input parameter of the Kalman filter, and then the Kalman filter is used for parameter estimation to finally obtain the optimized state estimate. That is, before each Kalman filter performs parameter correction, a genetic algorithm selection is run, the Kalman filter is configured according to the new filter parameters optimized by the genetic algorithm, and then the prediction and correction steps of the Kalman filter are performed. In this way, the parameter adjustment of the Kalman filter is no longer static, but dynamically optimized over time.

[0140] It is understandable that the present application may also set performance monitoring and termination conditions for the genetic algorithm. Specifically, the present application sets the termination conditions for the genetic algorithm selection, which may be reaching a predetermined genetic generation, fitness improvement less than a certain threshold, or reaching an ideal prediction performance. At the same time, the prediction performance of the Kalman filter is continuously monitored to ensure that the entire system is stable and effective.

[0141] Through the above steps, the fusion of genetic algorithm and Kalman filter not only improves the intelligence level of parameter optimization, but also enhances the adaptability and prediction accuracy of the battery thermal model to different working conditions.

[0142] In one embodiment, the step of obtaining the internal temperature of the target battery cell based on the surface temperature and the pre-established battery thermal model further includes: before correcting the filter parameters of the Kalman filter based on the genetic algorithm, iterating the genetic algorithm and updating the genetic algorithm. In this way, by iterating and updating the genetic algorithm, the genetic algorithm can be adjusted and optimized according to previous monitoring results and experience to better adapt to the characteristics and actual conditions of the battery cell, thereby improving the search capability and convergence speed of the genetic algorithm and more accurately finding the optimal combination of filter parameters.

[0143] Step 104, based on the outlier temperature and the internal temperature, obtain the temperature difference of the target battery cell. It should be noted that when the battery pack leaves the factory, the internal battery cells can maintain good consistency, but as the use time increases, the short board effect becomes more obvious, and the specificity between the battery cells will increase. The battery management system needs to continuously monitor the temperature difference of the battery cells, that is, the temperature difference between the surface temperature and the internal temperature, to perform iterative learning of the data, and then predict and judge the current state and service life of the battery pack.

[0144] Step 105, in response to the temperature difference exceeding the preset threshold, an early warning message of abnormal temperature of the target battery cell is issued. When the temperature difference exceeds the preset threshold, the present application promptly issues an early warning message to remind the battery management system to take corresponding measures, such as stopping charging and discharging, increasing heat dissipation measures, etc., to prevent overheating and failure, and improve the safety and reliability of the battery pack.

[0145] The battery pack temperature monitoring method provided in the present application is used to monitor the temperature of multiple battery cells in the battery pack, and the monitoring method includes: obtaining the surface temperature of the surfaces of multiple battery cells; screening the abnormal surface temperature as the outlier temperature through the pre-established aging model and influence index, and obtaining the target battery cell corresponding to the outlier temperature; obtaining the internal temperature of the target battery cell based on the surface temperature and the pre-established battery thermal model; the battery thermal model is configured to predict based on the Kalman filter, and the Kalman filter is configured to correct based on the genetic algorithm; based on the outlier temperature and the internal temperature, the temperature difference of the target battery cell is obtained; in response to the temperature difference exceeding the preset threshold, an early warning message of abnormal temperature of the target battery cell is issued. In this way, by respectively obtaining the surface temperature and the internal temperature of the battery cell, and judging whether the temperature of the battery cell is abnormal based on the temperature difference between the two, the temperature monitoring accuracy of the battery pack is improved, and the operation safety of the battery pack is guaranteed.

[0146] See also Figure 6 , Figure 6The structure diagram of the monitoring system of the battery pack temperature provided by the embodiment of the present application is illustrated. Accordingly, the embodiment of the present application provides a monitoring system of the battery pack temperature, which is used to execute the monitoring method of the battery pack temperature as any one of the above embodiments to monitor the temperature of multiple battery cells in the battery pack, and the monitoring system includes: a first acquisition module, a screening module, a second acquisition module, a third acquisition module and an early warning module. Among them, the first acquisition module is configured to obtain the surface temperature of the surface of multiple battery cells; the screening module is configured to screen the abnormal surface temperature as an outlier temperature through a pre-established aging model and an influence index, and obtain the target battery cell corresponding to the outlier temperature; the second acquisition module is configured to obtain the internal temperature of the target battery cell based on the surface temperature and the pre-established battery thermal model; the battery thermal model is configured to predict based on the Kalman filter, and the Kalman filter is configured to correct based on the genetic algorithm; the third acquisition module is configured to obtain the temperature difference of the target battery cell based on the outlier temperature and the internal temperature; the early warning module is configured to issue an early warning message of abnormal temperature of the target battery cell in response to the temperature difference exceeding the preset threshold.

[0147] The battery pack temperature monitoring system provided in the present application has all the technical features and beneficial effects of the above-mentioned battery pack temperature monitoring method, which will not be repeated here.

[0148] The above is a detailed introduction to a battery pack temperature monitoring method and system provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the technical solution and its core idea of ​​the present application. Ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring battery pack temperature, characterized in that: For monitoring the temperature of multiple battery cells in a battery pack, the monitoring method comprises: Acquiring the surface temperature of the surfaces of the plurality of battery cells; The abnormal surface temperature is screened as an outlier temperature through a pre-established aging model and an influence index, and a target battery cell corresponding to the outlier temperature is obtained; Based on the surface temperature and a pre-established battery thermal model, an internal temperature of the target battery cell is acquired; the battery thermal model is configured to make predictions based on a Kalman filter, and the Kalman filter is configured to make corrections based on a genetic algorithm; Based on the outlier temperature and the internal temperature, obtaining a temperature difference of the target battery cell; In response to the temperature difference exceeding a preset threshold, a warning message indicating that the temperature of the target battery cell is abnormal is issued.

2. The method for monitoring the battery pack temperature according to claim 1, characterized in that: The step of screening the abnormal surface temperature as an outlier temperature through a pre-established aging model and an influence index, and obtaining a target battery cell corresponding to the outlier temperature, comprises: Eliminating invalid values ​​in the surface temperature; Based on the aging model and the impact index, obtaining a temperature threshold; In response to the surface temperature being higher than the temperature threshold, the surface temperature is calibrated as the outlier temperature, and the battery cell corresponding to the outlier temperature is determined as the target battery cell.

3. The method for monitoring the battery pack temperature according to claim 1, wherein: The aging model is established based on the following formula: Among them, Q loss is the battery capacity loss rate, in %; E a is the activation energy of the battery cell, in J*mol -1 ; exp() is the natural exponential function; η is the compensation coefficient; is the power law factor; I c is the discharge rate, in h -1 ; R gas is the gas constant, in J*(mol*K) -1 ; α and β are fitting constants; T s is the surface temperature; SOC is the state of charge of the battery cell; AH is the ampere-hour throughput.

4. The method for monitoring the battery pack temperature according to claim 1, wherein: The impact index is established based on the following formula: Where, Γ is the actual ampere-hour throughput; Γ nom is the actual battery life; I nom is the battery current under the calibration conditions, in A; EOL is the time when the battery cell reaches the end of its life, in h; I is the battery current, in A; T s is the surface temperature; SOC is the state of charge of the battery cell.

5. The method for monitoring the battery pack temperature as claimed in claim 2, characterized in that: The step of removing invalid values ​​from the surface temperature comprises: Obtaining an initial maximum value, an initial minimum value, and an initial average value among a plurality of the surface temperatures; In response to a sampling failure of the temperature sensor for acquiring the surface temperature, discarding the surface temperature acquired by the temperature sensor; In response to satisfying a first preset condition, starting a rejection step; In response to satisfying a second preset condition, the corresponding invalid value is eliminated; the first preset condition and the second preset condition are set based on the initial maximum value, the initial minimum value and the aging model.

6. The method for monitoring the battery pack temperature as claimed in claim 5, characterized in that: In response to satisfying the second preset condition, the step of removing the corresponding invalid value comprises: In response to the difference between the initial maximum value and the initial average value being greater than or equal to a first threshold, and the difference between the initial average value and the initial minimum value being less than or equal to a second threshold, or the difference between the initial maximum value and the initial average value being greater than a third threshold, removing the initial maximum value; In response to the difference between the initial average value and the initial minimum value being greater than or equal to the first threshold, and the difference between the initial maximum value and the initial average value being less than or equal to the second threshold, or the difference between the initial average value and the initial minimum value being greater than the third threshold, eliminating the minimum value; Obtaining a first difference between the surface temperature greater than or equal to a fourth threshold and the initial average value, and eliminating the surface temperature whose first difference is greater than or equal to the first threshold; A second difference between the surface temperature that is less than or equal to a fifth threshold and the initial average value is obtained, and the surface temperature whose second difference is greater than or equal to the first threshold is eliminated.

7. The method for monitoring the battery pack temperature as claimed in claim 1, characterized in that: The step of acquiring the internal temperature of the target battery cell based on the surface temperature and a pre-established battery thermal model comprises: Establishing the battery thermal model based on the heat generation process and heat transfer process of the battery cell; Iterating the genetic algorithm to update the genetic algorithm; Correcting filtering parameters of the Kalman filter based on the genetic algorithm; Based on the outlier temperature and the Kalman filter, the battery thermal model is predicted to obtain the internal temperature.

8. The method for monitoring the battery pack temperature as claimed in claim 7, characterized in that: The step of correcting the filter parameters of the Kalman filter based on the genetic algorithm comprises: Using the filtering parameters as the genotype of the genetic algorithm to define parameter coding; Initializing the genetic algorithm to create an initial population, wherein the initial population includes a plurality of groups of configurations of the filtering parameters; defining a fitness function, and evaluating the effectiveness of the filtering parameters based on the fitness function; Iterating the genetic algorithm based on the effectiveness; Based on the iterative genetic algorithm, the filtering parameters are corrected.

9. The method for monitoring the battery pack temperature as claimed in claim 1, characterized in that: The battery thermal model is pre-established based on the following formula: Among them, C s is the shell specific heat capacity of the battery cell; is the change of the surface temperature over time; T s is the surface temperature; T c is the internal temperature; T amb is the ambient temperature of the battery cell; R s is the equivalent thermal resistance of the part of the shell in contact with the air; R c is the internal equivalent thermal resistance of the battery cell.

10. A battery pack temperature monitoring system, characterized in that: A method for monitoring the temperature of a battery pack according to any one of claims 1 to 9, to monitor the temperatures of a plurality of battery cells in a battery pack, the monitoring system comprising: A first acquisition module is configured to acquire surface temperatures of surfaces of the plurality of battery cells; A screening module is configured to screen the abnormal surface temperature as an outlier temperature through a pre-established aging model and an influence index, and obtain a target battery cell corresponding to the outlier temperature; a second acquisition module, configured to acquire the internal temperature of the target battery cell based on the surface temperature and a pre-established battery thermal model; the battery thermal model is configured to perform prediction based on a Kalman filter, and the Kalman filter is configured to perform correction based on a genetic algorithm; a third acquisition module configured to acquire a temperature difference of the target battery cell based on the outlier temperature and the internal temperature; The early warning module is configured to issue an early warning message indicating that the temperature of the target battery cell is abnormal in response to the temperature difference exceeding a preset threshold.

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