Heat management method and device of soft package battery

Abstract

The invention discloses a thermal management method and device for a soft package battery, and belongs to the technical field of battery thermal management, and the method comprises the steps: constructing an equivalent circuit model and a two-dimensional thermal model of the soft package battery; performing two-dimensional coupling on the equivalent circuit model and the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft package battery; according to the two-dimensional thermoelectric coupling model, combining a Kalman filtering algorithm to predict temperature values of a plurality of areas of the soft package battery in real time; performing thermal management on the soft package battery according to the temperature value; the equivalent circuit model of the soft package battery and the two-dimensional thermal model are subjected to two-dimensional coupling to obtain the two-dimensional thermoelectric coupling model, direct thermoelectric association is realized, the thermal parameter can be directly obtained according to the electric parameter, and introduction of an additional sensor is avoided; the temperature value is predicted in real time through the Kalman filtering algorithm, accurate temperature prediction is achieved, then thermal management is conducted according to the temperature value, and accurate temperature control is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal management, and in particular to a thermal management method and device for a soft-pack battery. Background Art

[0002] Advances in lithium-ion battery technology have played an integral role in the commercialization of electric vehicles as a power source for vehicles. Lithium-ion batteries are considered the best candidate for electric vehicles due to their superior energy density, higher specific power, slower self-discharge rate, low weight, and longer cycle life than other rechargeable battery chemistries.

[0003] Soft-pack batteries are a type of lithium-ion battery whose performance, lifespan, and safety are easily affected by their operating temperature and the temperature gradient in the battery. In particular, as the energy density of the electrolyte in soft-pack batteries increases, the charge transfer reaction and film penetration process become more difficult, the resistance of lithium ions crossing the positive and negative electrodes increases, the interface impedance increases, and more heat is generated during charging and discharging, which leads to heat accumulation in the soft-pack batteries and uneven temperature distribution. In the prior art, in order to avoid excessive battery temperature, thermoelectric cooling is used to perform thermal management of the battery pack, which can quickly cool down the entire battery. However, thermoelectric cooling technology requires the addition of a large number of temperature sensors, which increases the complexity of the battery pack circuit and makes it difficult to manage accurately.

[0004] Therefore, in the process of thermal management of soft-pack batteries in the prior art, there is a problem that the complexity of the battery is increased due to the need to introduce a large number of sensors, and it is difficult to perform precise management. Summary of the invention

[0005] In view of this, it is necessary to provide a thermal management method and device for soft-pack batteries to solve the problem in the prior art that in the process of thermal management of soft-pack batteries, a large number of sensors need to be introduced, which increases the complexity of the battery.

[0006] In order to solve the above problems, the present invention provides a thermal management method for a soft-pack battery, comprising: Construct an equivalent circuit model and a two-dimensional thermal model of a soft-pack battery; The equivalent circuit model is coupled with the two-dimensional thermal model in two dimensions to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; Based on the two-dimensional thermoelectric coupling model, combined with the Kalman filter algorithm, the temperature values ​​of multiple areas of the soft-pack battery are predicted in real time; Thermal management of soft-pack batteries is performed based on temperature values.

[0007] In one possible implementation, an equivalent circuit model of a soft-pack battery is constructed, including: The electrochemical impedance spectroscopy analysis of the soft-pack battery was performed to obtain the equivalent circuit model.

[0008] In one possible implementation, a two-dimensional thermal model of a soft-pack battery is constructed, including: Construct the coordinate system of the soft-pack battery along the battery plane; Get the thermal conductivity equation of the soft pack battery; The thermal conduction equation is coupled based on the coordinates of the soft-pack battery to obtain a two-dimensional thermal model of the soft-pack battery; Among them, the battery plane is the surface with the largest area of ​​the soft-pack battery.

[0009] In one possible implementation, the two-dimensional thermal model is:

[0010] in, is the density of the soft pack battery, is the specific heat capacity of the soft pack battery, T for T(x,y,t) Abbreviation of T is the coordinates and time of the soft pack battery t The function of for x Anisotropic thermal conductivity in the direction, for y Anisotropic thermal conductivity in the direction, is the total heat generation of the soft pack battery, is the volume of the soft pack battery, is the convective heat transfer coefficient, is the ambient temperature, is the thickness of the soft pack battery.

[0011] In a possible implementation, the equivalent circuit model is two-dimensionally coupled with the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery, including: Obtain the electrothermal parameters of the equivalent circuit model; The equivalent relationship between the equivalent circuit model and the two-dimensional thermal model is constructed according to the electrothermal parameters to obtain a two-dimensional thermoelectric coupling model.

[0012] In one possible implementation, the temperature values ​​of multiple regions of the soft-pack battery are predicted in real time based on the two-dimensional thermoelectric coupling model and combined with the Kalman filter algorithm, including: According to the two-dimensional thermoelectric coupling model, the heat generation of multiple areas of the soft-pack battery is obtained; According to the heat generation, the temperature value of the corresponding area is predicted in real time in combination with the Kalman filter algorithm.

[0013] In a possible implementation, thermal management of the soft pack battery is performed according to the temperature value, including: Set the temperature threshold range and determine the maximum and minimum temperature thresholds; Cool down the battery area whose temperature value is greater than the maximum temperature threshold, and heat up the battery area whose temperature value is less than the minimum temperature threshold; When the temperature values ​​are all within the temperature threshold range, the average temperature value of the soft-pack battery is calculated and the temperature standard deviation is set; The temperature values ​​are subtracted from the average temperature values ​​in turn, and the large deviation temperature values ​​whose absolute values ​​of the differences are greater than the temperature standard deviation are screened out. The battery area with large deviation temperature values ​​higher than the average temperature value is cooled, and the battery area with large deviation temperature values ​​lower than the average temperature value is heated.

[0014] In a possible implementation, after predicting the temperature values ​​of multiple regions of the soft-pack battery in real time according to the two-dimensional thermoelectric coupling model and the Kalman filter algorithm, the following is further included: Dividing the soft-pack battery into a plurality of test areas along the battery plane, and respectively setting a thermocouple temperature sensor in each test area to obtain a first temperature of the test area; Based on the two-dimensional thermoelectric coupling model, the second temperature of the test area is predicted in real time in combination with the Kalman filter algorithm; The first temperature and the second temperature are compared to verify the applicability of the two-dimensional thermoelectric coupling model.

[0015] In order to solve the above problems, the present invention also provides a thermal management device for a soft-pack battery, comprising: Model building module, used to build equivalent circuit model and two-dimensional thermal model of soft-pack battery; The model two-dimensional coupling module is used to couple the equivalent circuit model with the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; The temperature value prediction module is used to predict the temperature values ​​of multiple areas of the soft-pack battery in real time based on the two-dimensional thermoelectric coupling model combined with the Kalman filter algorithm; The battery thermal management module is used to perform thermal management on the soft-pack battery according to the temperature value.

[0016] In a possible implementation, a thermoelectric module is further included, and the thermoelectric module is used to heat or cool the soft-pack battery according to the control instruction; Among them, the control instructions include heating instructions, cooling instructions and sleep instructions; When the temperature value is greater than the maximum temperature threshold, or the absolute value of the difference between the temperature value and the average temperature value is greater than the temperature standard deviation and the temperature value is higher than the average temperature value, a heating instruction is generated; When the temperature value is less than the minimum temperature threshold, or the absolute value of the difference between the temperature value and the average temperature value is greater than the temperature standard deviation and the temperature value is lower than the average temperature value, a cooling instruction is generated; When the temperature value is within the temperature threshold interval and the absolute value of the difference between the temperature value and the average temperature value is smaller than the temperature standard deviation, a sleep instruction is generated.

[0017] The beneficial effect of adopting the above embodiment is as follows: the present invention provides a thermal management method for soft-pack batteries. First, by constructing an equivalent circuit model and a two-dimensional thermal model of the soft-pack battery, the circuit relationship and the heat generation relationship of the soft-pack battery are sorted out; then, by two-dimensionally coupling the equivalent circuit model with the two-dimensional thermal model, the relationship between the electrical parameters and the thermal parameters is reconstructed, and a two-dimensional thermoelectric coupling model is obtained, which realizes direct thermoelectric correlation and can directly obtain thermal parameters based on electrical parameters, avoiding the introduction of additional sensors; finally, the temperature value is predicted in real time through the Kalman filtering algorithm, thereby realizing accurate temperature prediction, and then thermal management is performed according to the temperature value to realize accurate temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a process flow of an embodiment of a thermal management method for a soft-pack battery provided by the present invention; Figure 2 A schematic diagram of a process for constructing a two-dimensional thermal model of a soft-pack battery according to an embodiment of the present invention; Figure 3 A schematic diagram of the result of dividing the soft-pack battery area according to an embodiment of the present invention; Figure 4 A relationship diagram illustrating an embodiment of temperature prediction of a two-dimensional thermoelectric coupling model provided by the present invention; Figure 5 A reliability diagram of an embodiment of a comparative verification temperature prediction result provided by the present invention; Figure 6 A schematic diagram of a process for thermal management of a soft-pack battery according to a temperature value according to an embodiment of the present invention; Figure 7 A schematic diagram of the structure of an embodiment of a precise thermal management system for soft-pack batteries provided by the present invention; Figure 8 A structural block diagram of an embodiment of a thermal management device for a soft-pack battery provided by the present invention; Fig. 9 This is a structural block diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0020] The structure of soft-pack batteries mainly includes positive and negative electrodes, electrolytes, separators and aluminum-plastic film shells. The positive electrode material is usually a carbon electrode that can absorb lithium ions, and the negative electrode material is a carbon material or metal oxide with a potential close to the lithium potential. The electrolyte uses a mixed solvent system of LiPF6, and the separator is mainly a polyolefin microporous membrane such as PE, PP or their composite membranes. The shell is packaged with aluminum-plastic film, which not only protects the internal battery material but also ensures the safety of the battery.

[0021] As the energy density of the electrolyte in the soft-pack battery increases, the charge transfer reaction and the film penetration process are more difficult, the resistance of lithium ions crossing the positive and negative electrodes increases, the interface impedance increases, and more heat is generated during charging and discharging. In addition, the weak mechanical contact between the electrolyte and the cathode produces an SEI layer with poor thermal conductivity, resulting in different thermal resistances in different directions and positions, which is easy to produce regional heat concentration, resulting in hot spots with uneven temperature distribution. Simulation and experimental studies show that heating intensifies with increased cycle rate, and the temperature difference in the battery is 2K to nearly 30K. This non-uniformity of temperature changes the equilibrium electrode potential of the redox reaction, resulting in uneven distribution of the equilibrium potential of lithium plating and graphite intercalation on the anode, making the lithium plating phenomenon in the high-temperature area more serious, and precipitating local metal lithium dendrites, thereby triggering violent side reactions and causing battery thermal runaway. In addition, studies have shown that the temperature dependence of the equilibrium potential will cause capacity decay of lithium-ion batteries, and this decay will become more and more serious under fast charging conditions. If this phenomenon is not controlled, it will seriously affect the cycle life, charge and discharge performance and thermal safety performance of the battery, and greatly increase the risk of thermal runaway. Therefore, thermal management of soft-pack batteries is required.

[0022] In the prior art, in order to prevent the battery temperature from being too high, thermoelectric cooling is used to manage the thermal state of the battery pack, which can quickly cool down the entire battery. However, thermoelectric cooling technology requires a large number of temperature sensors, which increases the complexity of the battery pack circuit and makes it difficult to manage accurately.

[0023] Therefore, in the process of thermal management of soft-pack batteries in the prior art, there is a problem that the complexity of the battery is increased due to the need to introduce a large number of sensors, and it is difficult to perform precise management.

[0024] In order to solve the above problems, the present invention provides a thermal management method and device for a soft-pack battery, which are described in detail below.

[0025] Figure 1 A schematic diagram of a process flow of an embodiment of a thermal management method for a soft-pack battery provided by the present invention, as shown in FIG. Figure 1 As shown, the thermal management methods for soft-pack batteries include: S101: Construct the equivalent circuit model and two-dimensional thermal model of the soft-pack battery; S102: performing two-dimensional coupling of the equivalent circuit model and the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; S103: Predicting the temperature values ​​of multiple areas of the soft-pack battery in real time based on the two-dimensional thermoelectric coupling model and the Kalman filter algorithm; S104: Perform thermal management on the soft-pack battery according to the temperature value.

[0026] In this embodiment, firstly, by constructing an equivalent circuit model and a two-dimensional thermal model of the soft-pack battery, the circuit relationship and the heat generation relationship of the soft-pack battery are sorted out; then, by two-dimensionally coupling the equivalent circuit model with the two-dimensional thermal model, the relationship between the electrical parameters and the thermal parameters is reconstructed, and a two-dimensional thermoelectric coupling model is obtained, which realizes the direct thermoelectric correlation and can directly obtain the thermal parameters according to the electrical parameters, avoiding the introduction of additional sensors; finally, the temperature value is predicted in real time by the Kalman filtering algorithm, and the temperature is accurately predicted, and then thermal management is performed according to the temperature value to achieve precise temperature control.

[0027] As a preferred embodiment, in S101, in order to construct an equivalent circuit model of the soft-pack battery, it is necessary to perform electrochemical impedance spectroscopy analysis on the soft-pack battery to obtain an equivalent circuit model.

[0028] In a specific embodiment, an electrochemical workstation is used to perform impedance spectrum analysis on a soft-pack battery at an AC frequency of 10 Hz-10 MHz to analyze the characteristics of different electrochemical links affected by the AC frequency; based on the results of the electrochemical impedance spectrum, an equivalent circuit is constructed to represent the separation phenomenon of the electrochemical links at different frequencies.

[0029] Specifically, the AC response of soft-pack batteries is divided into four impedance links: ohmic impedance, SEI interface impedance, charge transfer impedance, and diffusion impedance.

[0030] Among them, the ohmic impedance is represented by an equivalent DC resistance, which represents the inherent resistance of the soft-pack battery.

[0031] The SEI interface impedance is composed of a resistor and a phase angle element in parallel, representing the link of the SEI interface that is different from that of a square box and a cylindrical battery. The impedance response of the SEI interface at different frequencies is not a pure phase shift, but a composite effect of a phase shift and a frequency-related capacitance value, which cannot be represented by a single resistor or capacitor, so a phase angle Q element with angular frequency and charge is used in parallel with RS.

[0032] The charge transfer impedance is composed of a resistor and a phase angle element in parallel. Since the ionic conductivity of the SEI layer is smaller than that of the liquid electrolyte, the diffusion of lithium ions in the SEI layer becomes more difficult. This increases the concentration of lithium ions on the anode surface, reduces the rate at which lithium ions are removed from the anode during discharge, and ultimately increases the charge transfer impedance. Similarly, the charge transfer process with a similar time scale is also represented by Q and Rct in parallel.

[0033] The diffusion impedance is represented by the phase angle element, and the projection length on the Z-axis of the impedance spectrum is the diffusion impedance of lithium ions. It depends on the slope of the curve and the AC angle frequency in the EIS test and is insensitive to changes in SOC and temperature.

[0034] The time domain expression of the equivalent circuit model is:

[0035]

[0036] in, U t is the voltage across the battery, also called terminal voltage, which is the output parameter of the equivalent circuit model; I is the actual measured battery current, which is the input parameter of the equivalent circuit model; resistance R 0 is the equivalent ohmic internal resistance of the battery in the equivalent circuit; the first resistor R 1 and the first capacitor Q 1 represents the impedance and capacitance of the solid electrolyte interface, and the voltage across them is U Q1 Indicates; the second resistor R 2 and the second capacitor Q 2 represent the impedance and capacitance of the charge transfer process, and the voltage across them is expressed as U Q2 Indicates; the third capacitor Q 3 represents the capacitance of the electrolyte diffusion process in the battery, and the voltage across it is expressed as U Q3 express; U OCV The open circuit voltage of the battery is different from the terminal voltage. The open circuit voltage is the voltage after it is completely stationary and can reflect the actual electrochemical state. U OCV There is a correlation between the SOC (state of charge) and the battery. The corresponding relationship table between them can be obtained through the SOC-OCV calibration experiment, so that the open circuit voltage can be obtained according to the actual SOC size in the current state. U OCV The value of .

[0037] Specifically, the size of the state of charge in the current state is expressed by SOC(t) It can be obtained by the following ampere-hour integration method.

[0038]

[0039] In the formula, SOC(t 0 ） Indicates the initial SOC size, which is modified according to the actual size. C 0 is the rated capacity of the battery, I is the actual current of the battery.

[0040] Furthermore, the steps of the SOC-OCV calibration experiment are as follows: S1: Place the battery in a constant temperature box at 25°C for 2 hours; S2: First use 1C charging to the charging cut-off voltage of 4.2V, then use constant voltage charging. When the rate becomes 0.1C, the charging is completed, and the SOC is 100% at this time; S3: let stand for 2 hours; S4: Discharge at 1C rate until SOC decreases by 10%, and record the battery voltage at this time; S5: Repeat steps S3 and S4 until the battery is discharged to the discharge cut-off voltage and the SOC is 0%; To get the open circuit voltage U OCV After the relationship between U and SOC is obtained, U can be obtained by looking up the SOC table. OCV size.

[0041] In this embodiment, the state of charge SOC of the soft pack battery is obtained in real time. U OCV , the equivalent circuit model is obtained by converting the soft pack battery into an equivalent circuit model, combined with U OCV Get the required electrical parameter data.

[0042] It should be noted that the circuit link U Q1 , U Q2 , U Q3 It can be expressed as the product of input current and impedance as follows, R 0, R 1, R 2 resistance value and Q 1, Q 2, Q The capacitance value of 3 can be identified through experimental parameters.

[0043] Furthermore, in order to construct a two-dimensional thermal model of the soft-pack battery, as Figure 2 As shown, Figure 2 A schematic diagram of a process for constructing a two-dimensional thermal model of a soft-pack battery according to an embodiment of the present invention includes: S201: constructing a coordinate system of the soft-pack battery along the battery plane; S202: Obtaining a thermal conductivity equation of the soft-pack battery; S203: coupling the heat conduction equation based on the coordinates of the soft-pack battery to obtain a two-dimensional thermal model of the soft-pack battery; Among them, the battery plane is the surface with the largest area of ​​the soft-pack battery.

[0044] In this embodiment, firstly, the position management of the soft-pack battery is realized by constructing the coordinate system of the soft-pack battery along the battery plane; then, the heat conduction equation is coupled based on the coordinates of the soft-pack battery to obtain a two-dimensional thermal model of the soft-pack battery, thereby realizing the acquisition of heat generation at any precise position.

[0045] In a specific embodiment, the heat conduction equation is:

[0046]

[0047]

[0048] in, is the total heat generation of the soft pack battery, is the reversible heat of the soft pack battery, is the irreversible heat of the soft pack battery, T is the predicted temperature of the soft pack battery, where T for T(x,y,t) The abbreviation of the coordinates of the soft pack battery ( x,y ) and time t function.

[0049] It should be noted that since the calculation of irreversible heat requires continuous iteration, it is necessary to assign a value to the initial temperature of the soft-pack battery. Generally, the initial temperature of the battery is taken as .

[0050] Furthermore, in order to obtain a two-dimensional thermal model of the soft-pack battery, it is necessary to couple the coordinates of the soft-pack battery with the heat conduction equation, that is, to correspond the coordinates with the heat.

[0051] In a specific embodiment, the two-dimensional thermal model is:

[0052] in, is the density of the soft pack battery, is the specific heat capacity of the soft pack battery,T for T(x,y,t) The abbreviation of soft pack battery The predicted temperature at the time, T is the coordinates and time of the soft pack battery t The function of for x Anisotropic thermal conductivity in the direction, for y Anisotropic thermal conductivity in the direction, is the total heat generation of the soft pack battery, is the volume of the soft pack battery, is the convective heat transfer coefficient, is the ambient temperature, is the thickness of the soft pack battery.

[0053] In this embodiment, by coupling and transforming the coordinates of the soft-pack battery and the heat conduction equation, it is possible to obtain the heat corresponding to any position in real time.

[0054] As a preferred embodiment, in S103, since the charging and discharging process of the soft-pack battery changes in real time, in order to improve the data acquisition rate, the temperature of the soft-pack battery is monitored in real time, and the temperature values ​​of multiple areas of the soft-pack battery are predicted in real time based on the two-dimensional thermoelectric coupling model combined with the Kalman filter algorithm.

[0055] Specifically, the state matrix equation of the Kalman filter is as follows:

[0056] The input is u=[Q total ，1] T The output vector y is the temperature T1~T6 of the six areas on the battery surface, and the state variable vector x=[x00, x10…xn0, xn1,…xnn] T , matrix E represents the energy matrix of the system, matrix A represents the thermal conductivity matrix of the system, and matrix B and matrix C are constant matrices.

[0057] The matrix E is related to the thermal capacity and thermal conductivity of the battery and can be obtained by integrating the basis function with the weights as follows:

[0058]

[0059] in, and are two basis functions, and is the weight function, Represents the two-dimensional area of ​​the battery.

[0060] The matrix A represents the thermal conductivity matrix of the system, which involves heat conduction and diffusion effects. Its mathematical expression is expressed by integrating the second-order derivative of the basis function with the thermal conductivity of the material. Specifically, the matrix A is given by the following formula:

[0061] The matrix B relates the heat source, which is multiplied by the basis function and the corresponding weight function. Its mathematical form is as follows:

[0062] The matrix output matrix C is related to the position of the battery surface and is expressed as follows:

[0063] In this embodiment, the state equation of Kalman filtering is used to input the heat generation rate When the temperature of the six areas T1~T6 is directly obtained, the Kalman filter is recursive, which means that it can update its state estimate based on previous estimates and current measurements. It can better cope with large amounts of data streams, save computing resources and storage space, and effectively ensure the real-time nature of temperature prediction.

[0064] In a specific embodiment, the soft-pack battery is evenly divided into six regions for temperature estimation, and the temperature of the center point of the region is used to represent the temperature of the region, which greatly reduces the complexity of data processing. Figure 3 As shown, Figure 3 A schematic diagram of the result of dividing the soft-pack battery area according to an embodiment of the present invention.

[0065] In a specific embodiment, in order to clearly represent the internal structure relationship of the two-dimensional thermoelectric coupling model and clarify the temperature value prediction process, as shown in FIG. Figure 4 As shown, Figure 4 A relationship diagram of an embodiment of temperature prediction of a two-dimensional thermoelectric coupling model provided by the present invention. The two-dimensional thermoelectric coupling model of a soft-pack battery in the figure includes an equivalent circuit model and a two-dimensional thermal model of the soft-pack battery, wherein the two-dimensional thermal model divides the soft-pack battery evenly into six regions, and the parameters of the two-dimensional thermal model include thermal resistance and heat capacity between different regions; the parameters of the equivalent circuit model include equivalent ohmic internal resistance R 0. First resistor R 1. Second resistor R 2. The first capacitor Q 1. Second capacitor Q 2. The third capacitor Q 3. Open circuit voltage U OCV and terminal voltage U tThe two-dimensional temperature prediction based on Kalman filtering in the figure refers to the temperature prediction process. Specifically, the current and voltage of the soft-pack battery are input into the Kalman filter and the two-dimensional thermoelectric coupling model. The Kalman filter updates and corrects the state variables according to the open-circuit voltage, the temperature at the previous moment or the pre-assigned temperature, current data and thermal resistance and heat capacity data, and updates the measured variables in real time. After that, each parameter is corrected through noise covariance estimation to obtain the temperature estimate.

[0066] After collecting the current and voltage of the soft-pack battery cell, the soft-pack battery cell is tested for AC electrochemical impedance spectroscopy, and an equivalent circuit model of the soft-pack battery is established based on the separation of electrochemical links at different AC frequencies; The two-dimensional plane of the soft-pack battery cell is divided into six different thermal management areas. There is thermal resistance and heat capacity between each area, and a two-dimensional thermal model of the soft-pack battery cell is established; the equivalent circuit model of the soft-pack battery cell and the two-dimensional thermal model are coupled to obtain a two-dimensional electrothermal coupling model of the soft-pack battery cell, and the parameters are identified based on the test data set obtained under different working conditions of the soft-pack battery cell; based on the two-dimensional thermoelectric coupling model of the soft-pack battery cell, combined with the Kalman filter prediction algorithm, the current and voltage are used as input to output the predicted temperature of six different areas of the battery in real time.

[0067] Furthermore, in order to verify the estimation accuracy of the temperature predicted by the two-dimensional thermoelectric coupling model combined with the Kalman filter algorithm and the applicability of the temperature prediction method under different working conditions, a soft-pack lithium-ion battery was selected, and dynamic current tests were carried out on it under different working conditions under the experimental conditions of a constant temperature box and a charge and discharge tester to obtain a data set of the battery under dynamic conditions, including voltage, current, capacity, temperature of six areas on the battery surface, and ambient temperature.

[0068] Specifically, first, select the soft-pack lithium-ion battery to be tested, stick the thermocouple temperature sensor to the six temperature measurement areas on the battery surface with thermal conductive silicone, and construct a two-dimensional thermoelectric coupling model of the soft-pack lithium-ion battery to be tested to obtain the first temperature of the soft-pack lithium-ion battery to be tested; then, place the battery in a constant temperature chamber at 25°C for 2 hours; connect the positive and negative electrodes of the battery to the charge and discharge tester, use a constant current of 0.33C to charge the battery to a cut-off voltage of 4.2V, at which time the SOC is 100%, and leave it for 2 hours; use a constant current of 0.33C to charge the battery to a cut-off voltage of 4.2V, at which time the SOC is 100%, and leave it for 2 hours; Discharge the battery's SOC to 50% and let it stand for 1 hour; use a charge and discharge tester to load the battery with a suitable proportion of voltage and current under the DST dynamic working condition; record the voltage, current, ambient temperature of the constant temperature chamber, and the different temperatures of six areas on the battery surface under this condition, which are recorded as the second temperature; change the ambient temperature of the constant temperature chamber from 5°C to 40°C, and perform multiple cycles of dynamic current working conditions in the range of 20% to 80% SOC, collect typical test data of the soft-pack lithium-ion battery to be tested under different environments, and supplement the data volume of the second temperature.

[0069] In order to optimize the unknown parameters of the two-dimensional thermoelectric coupling model, the voltage and current of the battery under typical working conditions are used as input, and the temperature is used as output. The unknown parameters of the two-dimensional thermoelectric coupling model of the soft-pack battery are determined in combination with the parameter identification strategy based on the cuckoo algorithm.

[0070] The predicted first temperature and the tested second temperature are compared to verify the effectiveness and accuracy of the two-dimensional thermoelectric coupling model in predicting the temperature of the soft-pack battery.

[0071] like Figure 5 As shown, Figure 5 A reliability diagram of an embodiment of a comparative verification temperature prediction result provided by the present invention.

[0072] Specifically, by comparing the predicted temperature with the tested temperature under dynamic current conditions, the verification results show that the two-dimensional electrothermal coupling model of the soft-pack battery can perfectly reflect the electrothermal coupling performance of the soft-pack battery under actual working conditions. The temperature prediction method based on the two-dimensional thermoelectric coupling model can accurately estimate the thermal distribution of the soft-pack battery. The maximum error of the four temperature measurement points is 0.7°C, which can meet the requirements of two-dimensional thermal uniformity of the soft-pack battery.

[0073] As a preferred embodiment, in S104, after obtaining the temperature value of the soft-pack battery, in order to better control the soft-pack battery to operate in an ideal state, such as Figure 6 As shown, Figure 6 A schematic diagram of a process for thermal management of a soft-pack battery according to a temperature value provided by the present invention includes: S601: Setting a temperature threshold range, and determining a maximum temperature threshold and a minimum temperature threshold; S602: Cooling the battery region whose temperature value is greater than the maximum temperature threshold, and heating the battery region whose temperature value is less than the minimum temperature threshold; S603: When the temperature values ​​are all within the temperature threshold range, the average temperature value of the soft-pack battery is calculated, and the temperature standard deviation is set; S604: Subtract the temperature values ​​from the average temperature values ​​in turn, screen out large deviation temperature values ​​whose absolute values ​​of the differences are greater than the temperature standard deviation, cool down the battery area where the large deviation temperature value is higher than the average temperature value, and heat up the battery area where the large deviation temperature value is lower than the average temperature value.

[0074] It should be noted that the large deviation temperature value refers to a temperature value whose absolute value of the difference with the average temperature value is greater than the temperature standard deviation. The large deviation temperature value is essentially still a temperature value, which is obtained through screening and needs to be temperature controlled.

[0075] In this embodiment, first, the temperature values ​​are preliminarily screened based on the temperature threshold range, and the areas where the temperature is too high or too low are directly cooled or heated accordingly; then, the average temperature value is used as a reference benchmark, and the temperature standard deviation is used as the allowable fluctuation range to compare the temperature values ​​again, screen out the temperature values ​​corresponding to the large deviation temperature values, and perform cooling or heating treatment accordingly.

[0076] Through secondary data comparison, the predicted temperature of the soft-pack battery at different positions can be balanced, thereby improving the uniformity of the battery temperature.

[0077] In a specific embodiment, in order to accurately manage the thermal performance of the soft-pack battery, a self-breathing liquid cooling plate and a thermoelectric module are specially arranged on both sides of the battery plane of the soft-pack battery to form a precise thermal management system for the soft-pack battery. Figure 7 As shown, Figure 7 This is a structural schematic diagram of an embodiment of a precise thermal management system for soft-pack batteries provided by the present invention. The precise thermal management system 700 for soft-pack batteries includes a soft-pack battery group 701, a self-breathing liquid cooling plate 702 and a thermoelectric module 703.

[0078] The soft-pack battery pack 701 is composed of soft-pack lithium-ion cells, the surface of which is wrapped with aluminum-plastic film and arranged in a horizontal stacked manner to form a module structure.

[0079] The self-breathing liquid cooling plate 702 serves as a partition of the soft-pack battery and is arranged alternately with the battery intervals, thereby being able to simultaneously reduce the temperature of the batteries on both sides, effectively reducing the temperature difference between the battery cells.

[0080] The thermoelectric module 703 is inserted between the soft-pack battery cell and the liquid cooling plate and fixed by highly thermally conductive silicone, which improves the heat exchange performance between the soft-pack battery cell and the liquid cooling plate, can more effectively exert the heat transfer effect of the thermoelectric module 703, and effectively control the temperature of the battery pack.

[0081] Among them, the self-breathing liquid cooling plate 702 is composed of a flat pipe with certain buffering properties, which is used to absorb the mechanical deformation caused by expansion stress and thermal stress during the charging and discharging process of the soft-pack battery to a certain extent. As a buffer layer between individual battery cells, it can provide protection against mechanical collisions.

[0082] In the soft-pack battery pack 701, multiple thermoelectric modules 703 are arranged on the surface of each battery cell according to actual needs, and the temperatures of multiple different areas are controlled respectively, so as to achieve precise thermal management.

[0083] Thermoelectric module 703 is composed of multiple PN semiconductor thermocouples, divided into positive and negative ends, and has two connection modes; The first order connection is the cooling mode, and the input current can transfer the heat generated by the battery to the integrated liquid cooling plate to provide a cooling effect. The first order means: the positive pole of the power supply is connected to the positive pole of the thermoelectric module 703, and the negative pole of the power supply is connected to the negative pole of the thermoelectric module 703.

[0084] When connected in the second sequence as the heating mode, the input current can transfer the heat in the integrated liquid cooling plate to the battery surface, providing a warming preheating effect, wherein the second sequence means: the positive pole of the power supply is connected to the negative pole of the thermoelectric module 703, and the negative pole of the power supply is connected to the positive pole of the thermoelectric module 703.

[0085] Multiple thermoelectric modules 703 are connected to the BMS of the battery pack. After receiving the data transmitted by the vehicle control core, the BMS issues a control instruction to adjust the size and direction of the current input to the thermoelectric module 703.

[0086] The control instructions of the thermoelectric module 703 are divided into three types: heating, cooling, and sleep. In the sleep mode, the BMS will not input current to the thermoelectric module 703.

[0087] Thermoelectric module 703 produces different thermal management capabilities according to the magnitude of the current. The larger the current, the better the heating / cooling performance, and the smaller the current, the worse the heating / cooling performance. The current magnitude of thermoelectric module 703 is calculated by the vehicle control core based on the thermal demand.

[0088] In this embodiment, in order to accurately heat the soft-pack battery, a self-breathing buffer cold plate is specially provided to isolate the mechanical collision of the soft-pack battery and prevent heat spread; in addition, for the low temperature phenomenon of the battery pack in cold seasons, the self-breathing buffer cold plate can also achieve efficient preheating function while maintaining thermal balance.

[0089] Through the above method, firstly, by constructing an equivalent circuit model and a two-dimensional thermal model of the soft-pack battery, the circuit relationship and heat generation relationship of the soft-pack battery are sorted out; then, by two-dimensionally coupling the equivalent circuit model with the two-dimensional thermal model, the relationship between the electrical parameters and the thermal parameters is reconstructed, and a two-dimensional thermoelectric coupling model is obtained, which realizes the direct connection between thermoelectricity and can directly obtain the thermal parameters according to the electrical parameters, avoiding the introduction of additional sensors; finally, the temperature value is predicted in real time through the Kalman filter algorithm, and the temperature is accurately predicted. Then, thermal management is performed according to the temperature value, and precise temperature control is achieved, ensuring the stable operation of the soft-pack battery.

[0090] In order to solve the above problems, the present invention also provides a thermal management device for a soft pack battery, such as Figure 8 As shown, Figure 8 This is a structural block diagram of an embodiment of a thermal management device for a soft-pack battery provided by the present invention. The thermal management device 800 for a soft-pack battery includes: A model building module 801 is used to build an equivalent circuit model and a two-dimensional thermal model of a soft-pack battery; A model two-dimensional coupling module 802 is used to perform two-dimensional coupling of the equivalent circuit model and the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; The temperature value prediction module 803 is used to predict the temperature values ​​of multiple areas of the soft-pack battery in real time based on the two-dimensional thermoelectric coupling model combined with the Kalman filter algorithm; The battery thermal management module 804 is used to perform thermal management on the soft-pack battery according to the temperature value.

[0091] like Fig. 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902 and a display 903. Figure 6 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0092] In some embodiments, the processor 901 may be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 902, such as the thermal management method of the soft-pack battery in the present invention.

[0093] In some embodiments, the processor 901 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 901 may be local or remote. In some embodiments, the processor 901 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0094] In some embodiments, the memory 902 may be an internal storage unit of the electronic device 900, such as a hard disk or memory of the electronic device 900. In other embodiments, the memory 902 may also be an external storage device of the electronic device 900, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 900.

[0095] Furthermore, the memory 902 may include both an internal storage unit of the electronic device 900 and an external storage device. The memory 902 is used to store application software installed in the electronic device 900 and various data.

[0096] In some embodiments, the display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 903 is used to display information of the electronic device 900 and to display a visual user interface. The components 901-903 of the electronic device 900 communicate with each other through a system bus.

[0097] In one embodiment, when the processor 901 executes the thermal management program of the soft-pack battery in the memory 902, the following steps may be implemented: Construct an equivalent circuit model and a two-dimensional thermal model of a soft-pack battery; The equivalent circuit model is coupled with the two-dimensional thermal model in two dimensions to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; Based on the two-dimensional thermoelectric coupling model, combined with the Kalman filter algorithm, the temperature values ​​of multiple areas of the soft-pack battery are predicted in real time; Thermal management of soft-pack batteries is performed based on temperature values.

[0098] It should be understood that: when the processor 901 executes the thermal management program of the soft-pack battery in the memory 902, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0099] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 900 mentioned, and the electronic device 900 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 900 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0100] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the thermal management method of the soft-pack battery provided in the above-mentioned method embodiments can be implemented.

[0101] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0102] The thermal management method and device for the soft-pack battery provided by the present invention are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A thermal management method for a soft pack battery, characterized in that: include: Construct an equivalent circuit model and a two-dimensional thermal model of a soft-pack battery; Two-dimensionally coupling the equivalent circuit model with the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; According to the two-dimensional thermoelectric coupling model, combined with the Kalman filter algorithm, the temperature values ​​of multiple areas of the soft-pack battery are predicted in real time; Thermal management is performed on the soft-pack battery according to the temperature value.

2. The thermal management method of the soft pack battery according to claim 1, characterized in that: The construction of an equivalent circuit model of a soft-pack battery includes: The soft-pack battery is subjected to electrochemical impedance spectroscopy analysis to obtain the equivalent circuit model.

3. The thermal management method of the soft pack battery according to claim 1, characterized in that: Construct a 2D thermal model of a soft-pack battery, including: Constructing a coordinate system of the soft-pack battery along the battery plane; Obtaining a thermal conductivity equation of the soft-pack battery; The heat conduction equation is coupled based on the coordinates of the soft-pack battery to obtain a two-dimensional thermal model of the soft-pack battery; The battery plane is the surface with the largest area of ​​the soft-pack battery.

4. The thermal management method of the soft pack battery according to claim 3, characterized in that: The two-dimensional thermal model is: in, is the density of the soft pack battery, is the specific heat capacity of the soft pack battery, T for T(x,y,t) Abbreviation of T are the coordinates and time of the soft pack battery t The function of for x Anisotropic thermal conductivity in the direction, for y Anisotropic thermal conductivity in the direction, is the total heat generation of the soft pack battery, is the volume of the soft pack battery, is the convective heat transfer coefficient, is the ambient temperature, is the thickness of the soft pack battery.

5. The thermal management method of the soft pack battery according to claim 1, characterized in that: The two-dimensional coupling of the equivalent circuit model and the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery includes: Obtaining electrothermal parameters of the equivalent circuit model; The equivalent relationship between the equivalent circuit model and the two-dimensional thermal model is constructed according to the electrothermal parameters to obtain the two-dimensional thermoelectric coupling model.

6. The thermal management method of the soft pack battery according to claim 1, characterized in that: The method of predicting the temperature values ​​of multiple regions of the soft-pack battery in real time based on the two-dimensional thermoelectric coupling model and in combination with the Kalman filter algorithm includes: According to the two-dimensional thermoelectric coupling model, obtaining the heat generation of multiple regions of the soft-pack battery; According to the heat generation, the temperature value of the corresponding area is predicted in real time in combination with the Kalman filter algorithm.

7. The thermal management method of the soft pack battery according to claim 1, characterized in that: The thermal management of the soft pack battery according to the temperature value includes: Set the temperature threshold range and determine the maximum and minimum temperature thresholds; Cooling down the battery area whose temperature value is greater than the maximum temperature threshold, and heating up the battery area whose temperature value is less than the minimum temperature threshold; When the temperature values ​​are all within the temperature threshold range, calculating the average temperature value of the soft-pack battery and setting the temperature standard deviation; The temperature values ​​are subtracted from the average temperature values ​​in turn, and large deviation temperature values ​​whose absolute values ​​of the differences are greater than the temperature standard deviation are screened out. The battery area where the large deviation temperature value is higher than the average temperature value is cooled, and the battery area where the large deviation temperature value is lower than the average temperature value is heated.

8. The thermal management method of the soft pack battery according to claim 3, characterized in that: According to the two-dimensional thermoelectric coupling model, combined with the Kalman filter algorithm, the temperature values ​​of multiple areas of the soft-pack battery are predicted in real time, and then the method further includes: Dividing the soft-pack battery into a plurality of test areas along the battery plane, and respectively setting a thermocouple temperature sensor in each test area to obtain a first temperature of the test area; Based on the two-dimensional thermoelectric coupling model, a Kalman filter algorithm is used to predict the second temperature of the test area in real time; The first temperature and the second temperature are compared to verify the applicability of the two-dimensional thermoelectric coupling model.

9. A thermal management device for a soft pack battery, characterized in that: include: Model building module, used to build equivalent circuit model and two-dimensional thermal model of soft-pack battery; A model two-dimensional coupling module, used for two-dimensionally coupling the equivalent circuit model with the two-dimensional thermal model to obtain a two-dimensional thermoelectric coupling model of the soft-pack battery; A temperature value prediction module, used to predict the temperature values ​​of multiple areas of the soft-pack battery in real time based on the two-dimensional thermoelectric coupling model in combination with a Kalman filter algorithm; A battery thermal management module is used to perform thermal management on the soft-pack battery according to the temperature value.

10. The thermal management device for a soft pack battery according to claim 9, characterized in that: Also included is a thermoelectric module, which is used to heat or cool the soft-pack battery according to a control instruction; Wherein, the control instructions include heating instructions, cooling instructions and sleep instructions; When the temperature value is greater than the maximum temperature threshold, or the absolute value of the difference between the temperature value and the average temperature value is greater than the temperature standard deviation and the temperature value is higher than the average temperature value, the heating instruction is generated; When the temperature value is less than the minimum temperature threshold, or the absolute value of the difference between the temperature value and the average temperature value is greater than the temperature standard deviation and the temperature value is lower than the average temperature value, generating the cooling instruction; When the temperature value is within the temperature threshold interval and the absolute value of the difference between the temperature value and the average temperature value is smaller than the temperature standard deviation, the sleep instruction is generated.

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