Battery temperature acquisition method, device, storage medium and energy storage system

Abstract

A battery temperature acquisition method, device, storage medium, and energy storage system. The first internal resistance of a battery is determined based on multiple current values and multiple first voltage values corresponding to each of the multiple current values; the multiple current values include multiple actual current values and M set current values collected for the battery at multiple moments; the multiple voltage values include multiple actual voltage values and M equivalent voltages collected corresponding to each of the multiple actual current values; the state of charge (SOC) of the battery at multiple moments is determined; and the battery temperature is acquired based on a battery thermal network model based on the first internal resistance and the battery state of charge. By including M set current values in the multiple current values and M equivalent voltages in the multiple voltage values, the first internal resistance and SOC of the battery can be accurately determined in real time, thereby accurately acquiring the battery temperature in real time, improving the safety of battery use.

Description

Technical Field

[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a battery temperature acquisition method, device, storage medium, and energy storage system. Background Art

[0002] Lithium-ion batteries are high-energy-density secondary batteries. Compared to traditional lead-acid and nickel-metal hydride batteries, lithium-ion batteries offer significant advantages and are currently widely used in electric vehicles and the energy storage industry as a power source or energy storage medium. However, due to their high discharge power, they also present numerous safety risks, including battery thermal abuse and thermal runaway. In the future, lithium-ion batteries are expected to develop towards larger and more modular designs. Simultaneously, the requirements for their maximum sustained discharge rate are increasing. At this point, the internal temperature rise of lithium-ion batteries will be significant, making it more likely that the battery will reach its thermal runaway limit.

[0003] The internal temperature of lithium-ion batteries is closely related to battery safety management, but obtaining real-time internal temperature without an internal thermocouple is difficult. Embedding a thermocouple inside the battery can easily damage the internal structure of the cell, thereby affecting the overall battery performance.

[0004] Therefore, there is currently no corresponding solution for how to obtain the battery temperature in real time and accurately without configuring an internal thermocouple to improve the safety of battery use. Summary of the Invention

[0005] The present application provides a battery temperature acquisition method, device, storage medium and energy storage system to obtain the battery temperature in real time and accurately, thereby improving the safety of battery use.

[0006] In a first aspect, a battery temperature acquisition method is provided, comprising: determining a first internal resistance of a battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values comprising a plurality of actual current values of the battery collected at a plurality of moments, the plurality of voltage values comprising a plurality of actual voltage values corresponding to each of the plurality of actual current values, the plurality of moments being within a set time window, and the start time of the set time window being t; the plurality of current values further comprising M set current values, and the plurality of first voltage values further comprising M equivalent voltages V eff , the M equivalent voltages V effThere is a one-to-one correspondence between the M set current values, where the set current value is 0; determining the battery state of charge of the battery at the multiple moments; and obtaining the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge, wherein the battery thermal network model is used to characterize the heat generation at the battery tab and / or the heat generation of the battery body.

[0007] In this aspect, there is no need to set a thermocouple in the battery. By including M set current values in the multiple current values and including M equivalent voltages in the multiple voltage values, the first internal resistance of the battery and the battery state of charge at multiple moments can be determined in real time and accurately, so that the battery temperature can be obtained in real time and accurately, thereby improving the safety of battery use.

[0008] In a possible implementation, the method further includes: determining a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix for the battery including direct test quantities and noise error parameters, and the parameter matrix to be identified is a parameter set including the inherent physical properties of the battery itself and the weights of the noise error parameters; determining the first internal resistance of the battery based on multiple current values and multiple first voltage values corresponding to each current value in the multiple current values, including: determining multiple second voltage values corresponding to the current value at each of the multiple moments according to the preliminary identification results of the system matrix and the parameter matrix to be identified at each of the multiple moments, and the multiple second voltage values corresponding to the current value at each of the multiple moments in the multiple moments. The preliminary identification result of the parameter matrix to be identified at each moment is obtained by transforming the final identification result of the parameter matrix to be identified at the moment before any moment; according to the correction matrix of the parameter matrix to be identified at each moment in the multiple moments, the multiple first voltage values, the multiple second voltage values and the preliminary identification result of the parameter matrix to be identified at each moment in the multiple moments, the final identification result of the parameter matrix to be identified at each moment in the multiple moments is obtained, and the final identification result of the parameter matrix to be identified at each moment in the multiple moments includes multiple second internal resistance values of the battery; and according to the multiple second internal resistance values of the battery, the first internal resistance value of the battery is obtained.

[0009] In this implementation, based on the system matrix and the parameter matrix to be identified of the battery, as well as the preliminary identification results of the correction matrix of the parameter matrix to be identified at each of the multiple moments, multiple first voltage values, multiple second voltage values, and the parameter matrix to be identified at each of the multiple moments, the internal resistance of the battery can be accurately obtained.

[0010] In another possible implementation, the first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

[0011] In another possible implementation, determining the battery state of charge of the battery at the multiple moments includes: obtaining the initial battery state of charge of each of the multiple moments based on the current value of the previous moment of each of the multiple moments and the battery state of charge of the previous moment of each of the multiple moments; and correcting the initial battery state of charge of each of the multiple moments based on a first voltage value corresponding to the current value of each of the multiple moments and a second voltage value corresponding to the current value of each of the multiple moments, to obtain the corrected battery state of charge of each of the multiple moments.

[0012] In this implementation, the battery state of charge at each of multiple moments can be accurately obtained.

[0013] In another possible implementation, the obtaining of the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge of the battery includes: obtaining the first internal resistance value of each of the one or more nodes of the battery according to the first internal resistance value of the battery; obtaining multiple current values flowing through each of the one or more nodes of the battery according to the multiple current values; obtaining the entropy thermal coefficient of the battery according to the battery state of charge at each of the multiple moments of the battery; and obtaining the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each of the one or more nodes of the battery, the multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0014] In this implementation, based on the thermal network model, the temperature of the battery can be accurately obtained according to one or more of the first internal resistance value of each of one or more nodes of the battery, multiple current values flowing through each of one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0015] In a second aspect, a battery temperature acquisition device is provided, comprising: a first determination unit, configured to determine a first internal resistance of a battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values comprising a plurality of actual current values of the battery collected at a plurality of moments, the plurality of voltage values comprising a plurality of actual voltage values corresponding to each of the plurality of actual current values, the plurality of moments being within a set time window, and the start time of the set time window being t; the plurality of current values further comprising M set current values, and the plurality of first voltage values further comprising M equivalent voltages V eff , the M equivalent voltages V effOne-to-one correspondence with the M set current values, the set current value is 0; a second determination unit, used to determine the battery state of charge of the battery at the multiple moments; and a first acquisition unit, used to obtain the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge of the battery, wherein the battery thermal network model is used to characterize the heat generation at the battery tab and / or the heat generation of the battery body.

[0016] In a possible implementation, the device further includes: a third determination unit, configured to determine a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix including direct test quantities and noise error parameters for the battery, and the parameter matrix to be identified is a parameter set including inherent physical properties of the battery itself and weights of the noise error parameters; the first determination unit includes: a fourth determination unit, configured to determine, based on the system matrix and preliminary identification results of the parameter matrix to be identified at each of the multiple moments, a plurality of second voltage values corresponding to the current value at each of the multiple moments, and a preliminary identification result of the parameter matrix to be identified at each of the multiple moments. The result is obtained by transforming the final identification result of the parameter matrix to be identified at the previous moment based on any moment; a second acquisition unit is used to obtain the final identification result of the parameter matrix to be identified at each of the multiple moments according to the correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values and the preliminary identification result of the parameter matrix to be identified at each of the multiple moments, and the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; and a third acquisition unit is used to obtain the first internal resistance value of the battery according to the multiple second internal resistance values of the battery.

[0017] In another possible implementation, the first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

[0018] In another possible implementation, the second determination unit includes: a fourth acquisition unit, used to obtain the initial battery state of charge of each of the multiple moments based on the current value of the previous moment of each of the multiple moments and the battery state of charge of the previous moment of each of the multiple moments; and a correction unit, used to correct the initial battery state of charge of each of the multiple moments based on a first voltage value corresponding to the current value of each of the multiple moments and a second voltage value corresponding to the current value of each of the multiple moments, to obtain the corrected battery state of charge of each of the multiple moments.

[0019] In another possible implementation, the first acquisition unit includes: a fifth acquisition unit, used to obtain the first internal resistance value of each of the one or more nodes of the battery based on the first internal resistance value of the battery; a sixth acquisition unit, used to obtain multiple current values flowing through each of the one or more nodes of the battery based on the multiple current values; a seventh acquisition unit, used to obtain the entropy thermal coefficient of the battery based on the battery state of charge at each of the multiple moments of the battery; and an eighth acquisition unit, used to obtain the temperature of the battery based on the battery thermal network model based on one or more of the first internal resistance value of each node in the one or more nodes of the battery, the multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0020] In a third aspect, a battery temperature acquisition device is provided, comprising: a processor, a memory, an input device, and an output device, wherein the memory stores instructions, and the processor executes the following instructions when running:

[0021] Determine a first internal resistance of the battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values include a plurality of actual current values of the battery collected at a plurality of moments, the plurality of voltage values include a plurality of actual voltage values corresponding to each of the plurality of actual current values, the plurality of moments are within a set time window, and the starting time of the set time window is t; the plurality of current values also include M set current values, and the plurality of first voltage values also include M equivalent voltages V eff , the M equivalent voltages V eff There is a one-to-one correspondence between the M set current values, where the set current value is 0; determining the battery state of charge of the battery at the multiple moments; and obtaining the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge, wherein the battery thermal network model is used to characterize the heat generation at the battery tab and / or the heat generation of the battery body.

[0022] In a possible implementation, the processor further executes the following instructions: determining a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix for the battery including direct test quantities and noise error parameters, and the parameter matrix to be identified is a parameter set including inherent physical properties of the battery itself and weights of the noise error parameters; the processor executes the instruction to determine the first internal resistance of the battery based on multiple current values and multiple first voltage values corresponding to each of the multiple current values, including: determining multiple second voltage values corresponding to the current value at each of the multiple moments based on the preliminary identification results of the system matrix and the parameter matrix to be identified at each of the multiple moments, The preliminary identification result of the parameter matrix to be identified at each of the multiple moments is obtained by transforming the final identification result of the parameter matrix to be identified at the previous moment based on any of the moments; the final identification result of the parameter matrix to be identified at each of the multiple moments is obtained based on the correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values, and the preliminary identification result of the parameter matrix to be identified at each of the multiple moments, the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; and the first internal resistance value of the battery is obtained based on the multiple second internal resistance values of the battery.

[0023] In another possible implementation, the first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

[0024] In another possible implementation, the processor executes the instruction for determining the battery state of charge of the battery at the multiple moments, including: obtaining the initial battery state of charge of each of the multiple moments based on the current value of the previous moment of each of the multiple moments and the battery state of charge of the previous moment of each of the multiple moments; and correcting the initial battery state of charge of each of the multiple moments based on the first voltage value corresponding to the current value of each of the multiple moments and the second voltage value corresponding to the current value of each of the multiple moments, to obtain the corrected battery state of charge of each of the multiple moments.

[0025] In another possible implementation, the processor executes the instruction of obtaining the temperature of the battery based on the battery thermal network model according to the first internal resistance value and the battery state of charge of the battery, including: obtaining the first internal resistance value of each of the one or more nodes of the battery according to the first internal resistance value of the battery; obtaining multiple current values flowing through each of the one or more nodes of the battery according to the multiple current values; obtaining the entropy thermal coefficient of the battery according to the battery state of charge at each of the multiple moments of the battery; and obtaining the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each node of the one or more nodes of the battery, the multiple current values flowing through each node of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0026] In a fourth aspect, a computer-readable storage medium is provided, in which a computer program or instruction is stored. When the computer program or instruction is executed by a battery temperature acquisition device, the method described in the first aspect or any one of the implementations of the first aspect is implemented.

[0027] According to a fifth aspect, a computer program product is provided, which, when executed on a computing device, enables the method described in the first aspect or any implementation of the first aspect to be executed.

[0028] In a sixth aspect, an energy storage system is provided, comprising a plurality of batteries and the battery temperature acquisition device implemented in the second aspect or any one of the second aspects, wherein the battery temperature acquisition device is used to acquire the temperature of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a battery temperature acquisition method provided in an embodiment of the present application;

[0030] Figure 2 For Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S101;

[0031] Figure 3 A schematic diagram of a virtual shutdown and internal resistance identification process provided in an embodiment of the present application;

[0032] Figure 4 For Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S102;

[0033] Figure 5 A schematic diagram showing the relationship between the open circuit voltage and the state of charge of a battery provided in an embodiment of the present application;

[0034] Figure 6A schematic diagram of a process for establishing a battery thermal network model provided in an embodiment of the present application;

[0035] Figure 7 A schematic diagram of a multi-level thermal network model of a battery with a tab structure provided in an embodiment of the present application;

[0036] Figure 8 A schematic diagram of a multi-level thermal network model of a battery with a tab structure, provided as a specific example in an embodiment of the present application;

[0037] Figure 9 For Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S103; FIG.

[0038] Figure 10 This is a schematic diagram of the temperature output comparison results of an example of an embodiment of the present application;

[0039] Figure 11 A schematic diagram of the structure of a battery temperature acquisition device provided in an embodiment of the present application;

[0040] Figure 12 A schematic structural diagram of another battery temperature acquisition device provided in an embodiment of the present application;

[0041] Figure 13 A schematic diagram of the structure of another battery temperature acquisition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0043] In response to the problems raised in the background technology, the present application provides a battery temperature estimation solution. By including M set current values in multiple current values and including M equivalent voltages in multiple voltage values, the first internal resistance of the battery and the battery state of charge at multiple moments can be determined in real time and accurately, so that the battery temperature can be obtained in real time and accurately, thereby improving the safety of battery use.

[0044] like Figure 1 FIG. 1 is a flow chart of a method for obtaining battery temperature according to an embodiment of the present application. For example, the method may include the following steps:

[0045] S101. Determine a first internal resistance of a battery according to a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values.

[0046] This embodiment aims to obtain the battery temperature in real time. The battery temperature includes heat generated by the battery body and heat generated at the battery tabs. The battery body heat generation includes heat generated by the battery's internal resistance and reversible heat generated by the battery's state of charge (SOC).

[0047] The heat generated by the battery body and the heat generated at the battery tabs can be obtained through time iteration.

[0048] Among them, the heat generation of the battery body is associated with parameters such as the battery current, the battery internal resistance value, the battery SOC, the equivalent contact internal resistance at the battery tab, and the battery physical parameters. Therefore, in order to obtain the battery temperature in real time, it is necessary to obtain multiple current values of the battery, the first internal resistance value of the battery, and the battery SOC in real time. This embodiment divides the constant current discharge process of the battery into multiple short time windows to obtain multiple current values of the battery, the first internal resistance value of the battery, and the battery SOC within each short time window.

[0049] In this embodiment, the process of obtaining the first internal resistance value of the battery can also be called identifying the battery internal resistance.

[0050] The method for identifying the battery's internal resistance at the current moment is easily affected by historical data. That is, the internal resistance estimate within the set time window (t, t+Δτ) will be affected by the first internal resistance value within the time window (t-Δτ, t). Therefore, it is necessary to derive relatively independent internal resistance identification methods within different time windows.

[0051] When entering the time window (t, t+Δτ) from the time window (t-Δτ, t) at time t, for a truly independent internal resistance identification process, the battery needs to be left alone for a sufficient time after power failure to ensure that the effect of the battery's polarization internal resistance completely disappears, and then the voltage is applied again. After the voltage returns to the level at time t, the data of the time window (t, t+Δτ) is introduced.

[0052] However, considering that the battery cannot complete the above shutdown process during operation, and the reloading of voltage will change the internal state of the battery, it is necessary to include M set current values in the multiple current values obtained in the set time window, and include M equivalent voltages V in the multiple first voltage values corresponding to each current value in the multiple current values. eff For example, the above M set current values and M equivalent voltages V can be obtained by algorithmic operation. eff During this process, the current suddenly changes to 0 (i.e. the set current value is 0), the parameters of the internal resistance identification algorithm are restored to the initial values, and the voltage rises back to the equivalent voltage V eff Among them, M equivalent voltages V eff There is a one-to-one correspondence with M set current values. M is a positive integer.

[0053] Therefore, this embodiment obtains multiple current values and multiple first voltage values corresponding to the multiple current values. The multiple current values include multiple actual current values of the battery collected at multiple moments, and the multiple voltage values include multiple actual voltage values collected corresponding to each of the multiple actual current values. The multiple moments are within a set time window, and the start time of the set time window is t; the multiple current values also include the above-mentioned M set current values, and the multiple first voltage values also include the above-mentioned M equivalent voltages V eff .

[0054] Then, the first internal resistance of the battery may be determined according to the multiple current values and the multiple first voltage values corresponding to the multiple current values.

[0055] S102. Determine the battery state of charge at multiple moments.

[0056] The heat generated by the battery body also includes reversible heat generation corresponding to the SOC, so the battery state of charge at multiple moments can also be obtained. The multiple moments are the same as the multiple moments in step S101.

[0057] S103 . Obtain the temperature of the battery according to the first internal resistance value of the battery and the battery state of charge, based on a battery thermal network model.

[0058] After obtaining the battery's first internal resistance and the battery's state of charge at multiple moments, these values can be input into a battery thermal network model to obtain the battery body temperature and the battery tab temperature. The battery thermal network model is used to characterize heat generation at the battery tab and / or heat generation within the battery body.

[0059] For example, before the above step S101, the following steps may be further included:

[0060] Determine the battery system matrix and the parameter matrix to be identified.

[0061] Exemplarily, a first-order RC model of the battery is established to derive the system matrix and the parameter matrix to be identified of the battery.

[0062] Among them, the system matrix of the battery is as follows:

[0063]

[0064] in, is the estimated value of the system matrix. The so-called system matrix refers to a matrix composed of directly testable quantities and noise error parameters for a certain battery or battery module. In the derivation formula, the first four items represent the current value that can be directly obtained through testing and the first voltage value corresponding to the current value. It is the estimated value of the residual of the voltage estimation at each moment. I(t) represents the current value passing through the battery at time t, V t (t) represents the first voltage value applied to both ends of the battery at time t.

[0065] In this embodiment, the voltage and current related parameters in the system matrix can be calculated based on the actual input data, and the initial value of the residual in the system matrix can be 0, that is:

[0066]

[0067] Among them, the parameter matrix to be identified of the battery is as follows:

[0068]

[0069] Where, Θ(t)=[OCV-(R o +R 1P )-R o R 1P C 1P R 1P C 1P ]

[0070] Among them, θ(t) represents the parameter matrix to be identified, which is generally a parameter set consisting of the inherent physical properties of the battery itself and the noise error weight. Among them, the inherent physical properties of the battery itself are: OCV represents the open circuit voltage of the battery, R o Represents the ohmic internal resistance of the battery, R 1p Represents the polarization internal resistance of the battery, C 1p Represents the battery polarization capacitance. Θ(t) summarizes all the physical parameters in the above θ(t). c t-1 (t) to It has no actual physical meaning and can be understood as the residual estimate at time t-1 At time tn d The residual estimate of The weight coefficient of .

[0071] In this embodiment, obtaining the first internal resistance of the battery mainly refers to obtaining the ohmic internal resistance R of the battery. o and the battery's polarization internal resistance R 1p .

[0072] In this embodiment, the initial value of the parameter matrix to be identified can be a smaller value:

[0073] θ(1)=θ(2)=…=θ(n d )=θ0=[10 -6 10 -6 10-6 10 -6 10 -6 … 10 -6 ]

[0074] The following are detailed descriptions of the above steps:

[0075] like Figure 2 As shown, for Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S101. For example, step S101 includes the following steps:

[0076] S1011. Determine multiple second voltage values corresponding to the current value at each of the multiple moments based on the system matrix and the preliminary identification results of the parameter matrix to be identified at each of the multiple moments. The preliminary identification result of the parameter matrix to be identified at each of the multiple moments is obtained by transforming the final identification result of the parameter matrix to be identified at the previous moment based on any moment.

[0077] This example uses the internal resistance identification of a lithium iron phosphate (LFP) lithium-ion battery as an example, assuming a nominal voltage of 3.2V and a battery capacity C of 27Ah. The battery is charged to the cutoff voltage using a constant current charge (0.5C). After standing for one hour, the battery is fully charged. The battery is then discharged at a constant current of 100A until the voltage drops to the discharge cutoff voltage.

[0078] In this embodiment, the constant current discharge process is divided into a plurality of short time windows to identify the internal resistance of the battery within each short time window.

[0079] The internal resistance identification method of the battery at the current moment is easily affected by historical data. That is, the internal resistance estimation value within the time window (t, t+Δτ) will be affected by the internal resistance within the time window (t-Δτ, t). Therefore, it is necessary to derive relatively independent internal resistance identification methods within different time windows.

[0080] When entering the time window (t, t+Δτ) from the time window (t-Δτ, t) at time t, for a truly independent internal resistance identification process, the battery needs to be left alone for a sufficient time after power failure to ensure that the effect of the battery's polarization internal resistance completely disappears, and then the voltage is applied again. After the voltage returns to the level at time t, the data of the time window (t, t+Δτ) is introduced.

[0081] However, considering that the battery cannot complete the above shutdown process during operation, and the reloading of voltage will change the internal state of the battery, it is necessary to include M set current values in the multiple current values obtained in the set time window, and include M equivalent voltages V in the multiple first voltage values corresponding to each current value in the multiple current values. effFor example, the above M set current values and M equivalent voltages V can be obtained by algorithmic operation. eff During this process, the current suddenly changes to 0 (i.e. the set current value is 0), the parameters of the internal resistance identification algorithm are restored to the initial values, and the voltage rises back to the equivalent voltage V eff Among them, M equivalent voltages V eff There are M set current values corresponding to each other. M is a positive integer. The equivalent voltage is calculated as follows:

[0082]

[0083] Since this moment is not the time recorded by the system during the actual operation of the battery, the time is expressed as express. is the system matrix of the battery during this operation.

[0084] The M set current values may correspond to the current values at M moments, and the M equivalent voltages V eff It may be M voltage values corresponding to each current value in the current values at M moments.

[0085] Here, M can be set according to specific computing requirements. For example, for the above charge and discharge process, the time window Δτ can be selected as 20 seconds, where M is set to 10 seconds. Therefore, the total duration is 30 seconds.

[0086] The current value at these M moments is 0, and the voltage value at these M moments is the equivalent voltage V eff .

[0087] Starting from the start time t, a plurality of actual current values collected at a plurality of time moments within a set time window are acquired, and a plurality of actual voltage values corresponding to each of the plurality of actual current values are acquired.

[0088] In this embodiment, actual current values at multiple moments within the (t, t+Δτ) time window can be tested or collected in real time, and multiple actual voltage values corresponding to each of the multiple actual current values can be tested or collected in real time.

[0089] Then, multiple actual current values corresponding to the battery in the (t, t+Δτ) time window are spliced with M set current values, and multiple actual voltage values corresponding to each actual current value in the multiple actual current values are spliced with M equivalent voltage values respectively, to obtain: I t (t)=[0, 0,...0,I(t),I(t+1),...,I(t+Δτ)] and V t (t)=[V eff , V eff ,...V eff , V t(t), V t (t+1),...,V t The spliced current and voltage data are the current and voltage values actually used by the battery parameter matrix θ(t) to be identified within the time window (t, t+Δτ).

[0090] Then, based on the spliced current and voltage data obtained above, the parameter matrix of the battery to be identified at the previous time t-1 of the actual operation process of the battery is substituted into the system matrix at the current time t to obtain the second voltage value of the battery at the current time t

[0091]

[0092] Among them, θ (1) (t) represents the preliminary identification result of the parameter matrix to be identified of the battery at time t; θ (2) (t-1) represents the final identification result of the parameter matrix of the battery to be identified at the previous time t-1; A is the final identification result of the parameter matrix of the battery to be identified at the previous time t-1; (2) (t-1) to θ (1) (t) Transformed transfer matrix. In the absence of a clear recursive relationship, it is generally believed that the final identification result of the battery parameter matrix to be identified at the previous moment is the preliminary identification result of the parameter to be identified at the current moment, that is, A=1.

[0093] S1012. Obtain a final identification result of the parameter matrix to be identified at each of the multiple moments based on a correction matrix of the parameter matrix to be identified at each of the multiple moments, a plurality of first voltage values, a plurality of second voltage values, and a preliminary identification result of the parameter matrix to be identified at each of the multiple moments. The final identification result of the parameter matrix to be identified at each of the multiple moments includes a plurality of second internal resistance values of the battery.

[0094] First, calculate the covariance matrix of the internal resistance identification:

[0095]

[0096] Among them, P(t) represents the covariance matrix in the process of estimating the battery's parameter matrix to be identified using the least squares principle. Its original expression is The recursive form is used in this embodiment. λ represents the forgetting factor, which needs to be manually selected and is generally between 0.9 and 1.

[0097] Among them, the initial value of the covariance matrix can be set to a matrix with a larger value, that is:

[0098]

[0099] Among them, n d Refers to the number of virtual time steps before discharge begins, usually n d =5.

[0100] Then, the first voltage value of the battery at the current time t obtained in step S1011 is and the multiple first voltage values V obtained in step S1011 t (t), is converted into the preliminary identification result θ at the current time t (1) (t) and the corrected identification result θ at the current time t (2) The difference between (t):

[0101]

[0102] in, It can be regarded as a correction matrix of the parameter matrix to be identified.

[0103] According to the parameter matrix θ to be identified at the current moment (2) (t), the second internal resistance value of the battery at the current time t can be obtained.

[0104] Furthermore, the following steps may be included: calculating the system residual matrix at the current moment:

[0105]

[0106] The calculated system residual matrix at the current moment is used for the internal resistance identification process at the next moment.

[0107] S1013. Obtain a first internal resistance value of the battery according to a plurality of second internal resistance values of the battery.

[0108] Based on the iterative algorithm of the internal resistance in each time window from S1011 to S1012, the mean value of the parameters to be identified in (t, t+Δτ) is obtained as the primary output of the algorithm. The matrix θ of the parameters to be identified corresponding to the time t, t+1, ..., t+Δτ in the time window (t, t+Δτ) is obtained. (2) (t), θ (2) (t+1),...,θ (2) The average of (t+Δτ) is taken to obtain the first internal resistance of the battery in the time window.

[0109] This process is repeated in the following manner, and the dynamically changing internal resistance of the battery during the entire discharge process is finally calculated.

[0110] like Figure 3As shown in FIG, a schematic diagram of a virtual shutdown and internal resistance identification process provided by an embodiment of the present application is provided. Assume that the entire constant current discharge process includes n internal resistance identifications. For the n-1 internal resistance identification process, M set current values are obtained, and M equivalent voltage values V are obtained. eff ; Starting from the starting time t, collect multiple actual current values of the battery at multiple times within the time window (t, t+Δτ), and multiple actual voltage values corresponding to each of the multiple actual current values; obtain the multiple spliced current values and the multiple first voltage values corresponding to each of the multiple current values to obtain the system matrix of the battery; obtain the second voltage value of the battery at the current time t based on the multiple spliced current values and the multiple first voltage values corresponding to each of the multiple current values Get the second voltage value at the current time t With the first voltage value V t (t); convert the difference into a second internal resistance value difference based on the correction matrix of the parameter matrix to be identified; and output the second internal resistance value at the current time t. Obtain the first internal resistance value of the battery identified for the nth time. Internal resistance identification in other time windows can be iteratively performed according to the chronological order.

[0111] like Figure 4 As shown, for Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S102. For example, step S102 includes the following steps:

[0112] S1021. Obtain an initial battery state of charge at each of the multiple moments according to the current value at the moment immediately preceding each of the multiple moments and the battery state of charge at the moment immediately preceding each of the multiple moments.

[0113] Specifically, the calculation equation and observation equation of the battery state of charge are determined to achieve automatic iteration of the battery state of charge over time, and to establish the relationship between the battery state of charge and observable variables:

[0114]

[0115] in

[0116]

[0117]

[0118]

[0119]

[0120] Where SOC(t) represents the SOC of the battery at time t; U p(t) represents the polarization voltage of the battery at time t; τ1 is the system time constant, τ1 = R 1p C 1p , and R 1p 、C 1p It can be obtained through the identification algorithm of step S101; C p represents the capacity of the battery; Δt is the time interval selected based on experimental data and calculation requirements, and is 1s in this embodiment; ω t represents the process noise, that is, in x t Error value in the process of automatic update over time; t It represents the test noise, that is, through x t The error generated in the process of calculating the second voltage value V.

[0121] It can be understood that the calculation method of the battery terminal voltage V in the process of obtaining the SOC is the same as the second voltage value in the process of obtaining the internal resistance of the battery. The calculation method can be different.

[0122] Among them, the relationship between the open circuit voltage of the battery and the battery state of charge is as follows: Figure 5 shown.

[0123] According to the above SOC calculation equation, we can get This includes the initial SOC.

[0124] S1022. Correct the initial battery state of charge at each of the multiple moments based on the first voltage value corresponding to the current value at each of the multiple moments and the second voltage value corresponding to the current value at each of the multiple moments to obtain a corrected battery state of charge at each of the multiple moments.

[0125] Specifically, the Kalman gain is calculated and the average battery state of charge of the battery at the current time t is corrected:

[0126]

[0127]

[0128]

[0129]

[0130] The initial values of the system error and the estimated error can be set as:

[0131]

[0132] R υ =0.05 2

[0133] Among them, P t It represents the covariance matrix in the SOC estimation process and has no actual physical meaning. Specifically, is the first updated P at time t t The value of P represents the second updated t The value of P at time t t Output value. K t In general battery state of charge estimation algorithms, it is called Kalman gain. w is the process noise ω in step S1021 t The variance, R υ It is the test noise υ in S1021 t The variance of e t is the difference between the test terminal voltage and the battery terminal voltage V calculated in S1021. Including the corrected SOC at time t.

[0134] Furthermore, the adaptive error iteration of the covariance matrix and Kalman gain matrix of the battery SOC estimation algorithm can be completed:

[0135]

[0136]

[0137]

[0138] The covariance matrix and Kalman gain matrix after completing the adaptive error iteration at the current time t can be used to correct the SOC at the next time t+1.

[0139] like Figure 6 FIG. 1 is a flow chart of establishing a battery thermal network model according to an embodiment of the present application. For example, the method may include the following steps:

[0140] S601. Establish a simplified thermal network model of a battery with a tab structure.

[0141] For example, the thermal network model can be represented in the form of a matrix:

[0142]

[0143] In this embodiment, the temperature of the battery includes the temperature at the battery tab and the temperature of the battery body. First, the initial tab temperature T can be measured by a thermocouple after standing for 1 hour. tab，1 Then, the temperature of the tab and the temperature of the battery body at other moments are obtained iteratively.

[0144] Among them, in the above thermal network model, T tab，t+1 represents the temperature of the battery at the next moment t+1 at the terminal ear; T t+1 Indicates the temperature of the battery body at the next time t+1, including the heat generated by the battery's internal resistance and the reversible heat corresponding to the battery's state of charge.

[0145] Among them, in the battery tab thermal model:

[0146]

[0147]

[0148]

[0149]

[0150] Among them A tab The heat transfer matrix at the tab position describes the heat transfer behavior at the tab; B tab The heat transfer matrix at the tab position describes the heat exchange between the tab and the battery body; C tab The heat dissipation matrix representing the tab position describes the heat convection between the battery tab and the environment; Q tab Indicates the heat generated at the tab; T t represents the temperature of the battery at time t; T a Indicates the ambient temperature.

[0151] Among them, in the battery body thermal model:

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] C=[HHHHN 1×12 HHHH] T

[0159]

[0160] in

[0161]

[0162] Among them, A is the internal heat transfer matrix of the battery body thermal network model, which describes the heat transfer behavior inside the battery body; B represents the heat exchange matrix between the battery body and the tab when the battery body is the research object; C represents the heat dissipation matrix of the battery body, which describes the heat convection between the battery body and the environment; C p，tab is the specific heat capacity of the battery tab; C p，b is the specific heat capacity of the battery; K x , K y , K z k represents the heat transfer related constants in the x, y and z directions of the thermal network model; x , k y , k z Represents the battery thermal conductivity in the x, y and z directions respectively; Δx, Δy and Δz represent the equivalent spacing between thermal network nodes; I tab Indicates the current passing through the terminal ear; I n (n=1,2,...20) represent the currents passing through different nodes respectively; V tab Indicates the volume of the battery tab, V b Indicates the volume of the battery body; T n (n=1, 2, ... 20) represent the temperatures of different thermal network nodes; R n (n=1, 2, ...20) represents the internal resistance of different thermal network nodes; H represents the constant related to convective heat transfer; h is the convective heat transfer coefficient of the battery surface; N represents the number of battery thermal network nodes; dU / dT represents the entropy thermal coefficient of the battery, which is used to calculate the reversible heat generation of the battery.

[0163] The above Q includes the internal resistance heat generation and reversible heat generation of each thermal network node of the battery body. For example, represents the internal resistance heat generation of node 1, Represents the reversible heat generation at node 1.

[0164] like Figure 7 As shown, a schematic diagram of a multi-level thermal network model of a battery with a tab structure provided in an embodiment of the present application can be provided. The battery body can be divided into multiple areas of set sizes, and the temperature of each area of set size is represented by the temperature of a thermal network node. Among them, the thermal network nodes include surface temperature points, internal temperature points and temperature points at the tabs. Among them, the temperature of the battery body can be represented by multiple surface temperature points and internal temperature points in the x, y and z directions. In addition, in the z direction, multiple temperature points at the tabs are also included.

[0165] like Figure 8The figure below shows a schematic diagram of a multi-level thermal network model for a battery with a tab structure, according to a specific example provided in an embodiment of the present application. The battery body includes 20 thermal network nodes. The left figure is a schematic diagram of the three-dimensional structure of the multi-level thermal network model, where points 1-4 and 17-20 are the surface temperature points of the battery body, and points 5-16 are the internal temperature points of the battery body. The right figure is a right view of the multi-level thermal network model.

[0166] S602. Determine all geometric parameters in the thermal network model by testing the length, height, and thickness of the battery body and the tabs. Among them, Δx, Δy, and Δz are proportional to the thickness, length, and height of the battery, respectively. For example, assuming the battery is 100mm long, 148mm high, and 20mm thick, the calculation results are Δx = 20 / 4 = 5mm, Δy = 100 / 2 = 50mm, and Δy = 148 / 2 = 74mm. The physical properties in S501 need to be obtained through experimental testing to complete the establishment of the thermal network model.

[0167] S603. Obtain the equivalent contact internal resistance at the battery tab.

[0168] It can be assumed that the battery tab contact internal resistance is constant, and an adiabatic constant current discharge experiment can be designed to obtain the equivalent contact internal resistance at the battery tab. The specific steps are as follows:

[0169] (1) Place thermocouples near the tab and at a distance xm (e.g., 3mm) below the tab, and wrap the battery with insulation material for thermal insulation treatment.

[0170] (2) The battery is discharged with a current of 1C rate, and the temperature values measured by the two thermocouples are recorded during the discharge process.

[0171] (3) The temperature change near the equivalent contact internal resistance of the battery tab can be expressed as:

[0172]

[0173] After transformation, the general solution of the homogeneous equation is:

[0174]

[0175] Among them, T below，tab Refers to the temperature of the lower part of the battery tab near the tab, where K and M are constants.

[0176] (4) Based on the data of the temperature difference between the two thermocouples, the coefficient between the experimental curve and the theoretical curve can be compared to obtain the equivalent internal resistance R at the battery tab. tab For example, the R calculated in this embodiment is tab =0.01mΩ.

[0177] S604. Obtain physical parameters of the battery.

[0178] The physical parameters of the battery include one or more of the following parameters: thermal conductivity and convection heat transfer coefficient of the battery in all directions.

[0179] For example, the anisotropic thermal conductivity and convective heat transfer coefficient of the battery can be determined as follows: x-direction thermal conductivity k x =5.5W / (m·K), the thermal conductivity in the y and z directions is k y =k z =40W / (m·K); the convection heat transfer coefficient is h=15W / (m 2 ·K).

[0180] Through the above process, the battery thermal network model is established.

[0181] like Figure 9 As shown, for Figure 1 FIG. 1 is a flow chart showing a further detailed description of step S103. For example, step S103 includes:

[0182] S1031. Obtain a first internal resistance value of each of one or more nodes of the battery according to the first internal resistance value of the battery.

[0183] Based on the first internal resistance of the battery identified in step S101, the first internal resistance of each node is calculated. Considering that the battery regions represented by each node are electrically connected in parallel, the first internal resistance of each node is as follows:

[0184] R i =a i R(a i >1)

[0185] Among them, a i = represents the number of battery thermal network nodes that actually generate heat; R is the total internal resistance of the battery, which can be obtained through the internal resistance identification algorithm in step S101. Here, the heat generation of surface nodes is not calculated, and only the heat generation of internal nodes is considered.

[0186] For example, in Figure 8 In the multi-level thermal network model shown, the first internal resistance value of each node is as follows:

[0187] R5=R6=…R 16 =12R

[0188] S1032. According to the multiple current values, obtain multiple current values of each of the one or more nodes flowing through the battery.

[0189] The current flowing through each node is as follows:

[0190]

[0191] Among them, a i represents the number of battery thermal network nodes that actually generate heat. Where I is the total battery current, which can be obtained through testing or data collection. Here, heat generation at surface nodes is not calculated; only heat generation at internal nodes is considered.

[0192] For example, in Figure 8 In the multi-level thermal network model shown, the current flowing through each node is as follows:

[0193]

[0194] S1033. Obtain the entropy thermal coefficient dU / dT of the battery according to the battery state of charge at each of the multiple moments.

[0195] The entropy thermal coefficient of the battery is determined by hybrid pulse power characteristic (HPPC) experiment as follows:

[0196]

[0197] S1034. Obtain the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each of the one or more nodes of the battery, the current value flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0198] Substitute the first internal resistance value of each of the one or more nodes of the battery, the current value flowing through each of the one or more nodes of the battery at each time in multiple moments, and the entropy thermal coefficient of the battery into Figure 6 The thermal network model established in the illustrated embodiment can be used to obtain the temperature of the battery, including the temperature at the battery tab and the temperature of the battery body.

[0199] Assume that at 25℃, the battery is discharged at a constant current of 6C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 25℃, the battery is charged at a constant current of 0.5C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 35℃, the battery is discharged at a constant current of 6C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 25℃, the battery is charged at a constant current of 0.5C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 45℃, the battery is discharged at a constant current of 6C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 25℃, the battery is charged at a constant current of 0.5C until the voltage drops to the discharge cut-off voltage; let it stand for 1 hour; at 55℃, the battery is discharged at a constant current of 6C until the voltage drops to the discharge cut-off voltage, and the following equation can be obtained: Figure 10The temperature output comparison result diagram of the embodiment of the present application is shown in the figure. The figure shows three temperature curves: the temperature calculated value of the core node, the temperature calculated value of the surface node and the temperature experimental value of the surface node. Figure 8 The battery internal nodes in the figure. The core node temperature calculation values and the surface node temperature calculation values can be calculated using the battery temperature acquisition scheme described above. The surface node temperature experimental values can be obtained using an experimental test method. As can be seen from the figure, the surface node temperature calculation values obtained using the battery temperature acquisition scheme described above are substantially consistent with the surface node temperature experimental values obtained using the experimental test method. Therefore, the battery temperature can be accurately obtained using the battery temperature acquisition scheme described above.

[0200] According to a battery temperature acquisition method provided in an embodiment of the present application, there is no need to set a thermocouple in the battery. By including M set current values in multiple current values and including M equivalent voltages in multiple voltage values, the first internal resistance value of the battery and the SOC at multiple moments can be determined in real time and accurately, so that the battery temperature can be acquired in real time and accurately, thereby improving the safety of battery use.

[0201] Based on the same concept of the above battery temperature acquisition method, the present application also provides a battery temperature estimation device. Part or all of the above method can be implemented by software or firmware. Figure 11 FIG. 1 is a schematic diagram of a battery temperature acquisition device 1100 provided in an embodiment of the present application, which is used to execute the above-mentioned battery temperature acquisition method. Specifically, the device 1100 includes:

[0202] The first determining unit 1101 is configured to determine a first internal resistance of the battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values include a plurality of actual current values of the battery collected at a plurality of moments, the plurality of voltage values include a plurality of actual voltage values corresponding to each of the plurality of actual current values, the plurality of moments are within a set time window, and the starting time of the set time window is t; the plurality of current values also include M set current values, and the plurality of first voltage values also include M equivalent voltages V eff , the M equivalent voltages V eff One-to-one correspondence with the M set current values, the set current value is 0; a second determination unit 1102, used to determine the battery state of charge of the battery at the multiple moments; and a first acquisition unit 1103, used to obtain the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge of the battery, wherein the battery thermal network model is used to characterize the heat generation at the battery tab and / or the heat generation of the battery body.

[0203] In one possible implementation, the device further includes: a third determination unit 1104 (represented by a dotted line in the figure), which is used to determine the system matrix and the parameter matrix to be identified of the battery, wherein the system matrix is a matrix for the battery, including direct test quantities and noise error parameters, and the parameter matrix to be identified is a parameter set including the inherent physical properties of the battery itself and the weights of the noise error parameters; the first determination unit 1101 includes: a fourth determination unit 1101a, which is used to determine the multiple second voltage values corresponding to the current value at each of the multiple moments based on the preliminary identification results of the system matrix and the parameter matrix to be identified at each of the multiple moments, and the parameter matrix to be identified at each of the multiple moments. The preliminary identification result is obtained by transforming the final identification result of the parameter matrix to be identified at the previous moment based on any one of the moments; a second acquisition unit 1101b is used to obtain the final identification result of the parameter matrix to be identified at each of the multiple moments according to the correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values and the preliminary identification result of the parameter matrix to be identified at each of the multiple moments, and the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; and a third acquisition unit 1101c is used to obtain the first internal resistance value of the battery according to the multiple second internal resistance values of the battery.

[0204] In another possible implementation, the first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

[0205] In another possible implementation, the second determination unit 1102 includes: a fourth acquisition unit 1102a, used to obtain the initial battery state of charge of each of the multiple moments based on the current value of the previous moment of each of the multiple moments and the battery state of charge of the previous moment of each of the multiple moments; and a correction unit 1102b, used to correct the initial battery state of charge of each of the multiple moments based on the first voltage value corresponding to the current value of each of the multiple moments and the second voltage value corresponding to the current value of each of the multiple moments, so as to obtain the corrected battery state of charge of each of the multiple moments.

[0206] In another possible implementation, the first acquisition unit 1103 includes: a fifth acquisition unit 1103a, used to obtain the first internal resistance value of each of the one or more nodes of the battery based on the first internal resistance value of the battery; a sixth acquisition unit 1103b, used to obtain multiple current values flowing through each of the one or more nodes of the battery based on the multiple current values; a seventh acquisition unit 1103c, used to obtain the entropy thermal coefficient of the battery based on the battery state of charge at each of the multiple moments of the battery; and an eighth acquisition unit 1103d, used to obtain the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each node of the one or more nodes of the battery, the multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0207] According to a battery temperature acquisition device provided in an embodiment of the present application, by including M set current values in multiple current values and including M equivalent voltages in multiple voltage values, the first internal resistance value of the battery and the battery state of charge at multiple moments can be determined in real time and accurately, thereby obtaining the battery temperature in real time and accurately, thereby improving the safety of battery use.

[0208] Optionally, when part or all of the battery temperature acquisition method in the above embodiment is implemented by software or firmware, Figure 12 Another battery temperature acquisition device 1200 is provided to achieve this. Figure 12 As shown, the battery temperature acquisition device 1200 may include:

[0209] Memory 1203 and processor 1204 (the processor 1204 in the system may be one or more, Figure 12 In this embodiment, the input device 1201, the output device 1202, the memory 1203 and the processor 1204 can be connected via a bus or other means, wherein: Figure 12 The bus connection is taken as an example.

[0210] The processor 1204 is used to execute Figure 12 The method steps performed in .

[0211] Specifically, the processor 1204 is configured to call the program instructions to perform the following operations:

[0212] Determine a first internal resistance of the battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values include a plurality of actual current values of the battery collected at a plurality of moments, the plurality of voltage values include a plurality of actual voltage values corresponding to each of the plurality of actual current values, the plurality of moments are within a set time window, and the starting time of the set time window is t; the plurality of current values also include M set current values, and the plurality of first voltage values also include M equivalent voltages V eff , the M equivalent voltages V eff There is a one-to-one correspondence between the M set current values, where the set current value is 0; determining the battery state of charge of the battery at the multiple moments; and obtaining the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge, wherein the battery thermal network model is used to characterize the heat generation at the battery tab and / or the heat generation of the battery body.

[0213] In a possible implementation, the processor 1204 further executes the following instructions: determining a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix for the battery including direct test quantities and noise error parameters, and the parameter matrix to be identified is a parameter set including inherent physical properties of the battery itself and weights of the noise error parameters; the processor 1204 executes the instruction to determine the first internal resistance of the battery based on multiple current values and multiple first voltage values corresponding to each current value in the multiple current values, including: determining multiple second voltage values corresponding to the current value at each of the multiple moments based on the system matrix and the preliminary identification results of the parameter matrix to be identified at each of the multiple moments voltage value, a preliminary identification result of the parameter matrix to be identified at each of the multiple moments is obtained by transforming the final identification result of the parameter matrix to be identified at the previous moment of any moment; obtaining a final identification result of the parameter matrix to be identified at each of the multiple moments according to the correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values, and the preliminary identification result of the parameter matrix to be identified at each of the multiple moments, the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; and obtaining the first internal resistance value of the battery according to the multiple second internal resistance values of the battery.

[0214] In another possible implementation, the first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

[0215] In another possible implementation, the processor 1204 executes the instruction for determining the battery state of charge of the battery at the multiple moments, including: obtaining the initial battery state of charge of each of the multiple moments based on the current value of the previous moment of each of the multiple moments and the battery state of charge of the previous moment of each of the multiple moments; and correcting the initial battery state of charge of each of the multiple moments based on the first voltage value corresponding to the current value of each of the multiple moments and the second voltage value corresponding to the current value of each of the multiple moments, to obtain the corrected battery state of charge of each of the multiple moments.

[0216] In another possible implementation, the processor 1204 executes the instruction for obtaining the temperature of the battery based on the battery thermal network model according to the first internal resistance value and the battery state of charge of the battery, including: obtaining the first internal resistance value of each of the one or more nodes of the battery according to the first internal resistance value of the battery; obtaining multiple current values flowing through each of the one or more nodes of the battery according to the multiple current values; obtaining the entropy thermal coefficient of the battery according to the battery state of charge at each of the multiple moments of the battery; and obtaining the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each of the one or more nodes of the battery, the multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

[0217] Optionally, the program of the battery temperature acquisition method can be stored in the memory 1203. The memory 1203 can be a physically independent unit or integrated with the processor 1204. The memory 1203 can also be used to store data.

[0218] Optionally, when part or all of the battery temperature acquisition method in the above embodiment is implemented via software, the battery temperature estimation system may include only a processor. A memory for storing programs is located outside the battery temperature estimation system, and the processor is connected to the memory via circuits or wires to read and execute the programs stored in the memory.

[0219] The processor may be a central processing unit (CPU), a network processor (NP), or a WLAN device.

[0220] The processor may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0221] The memory may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory may also include a combination of the above types of memory.

[0222] The input device 1201 / output device 1202 may include a display screen and a keyboard, and optionally, may also include a standard wired interface and a wireless interface.

[0223] like Figure 13 As shown, it is a structural schematic diagram of another battery temperature acquisition device provided in an embodiment of the present application. The figure illustrates a battery module, which includes multiple batteries. The battery temperature acquisition device is used to obtain the temperature of each battery in the battery module. The battery module can be uniformly managed by a battery management system (BMS). The BMS is located on the BMS circuit board. The BMS includes a BMS processor 1301, a set current value and equivalent voltage acquirer 1302 and a temperature-electrical signal converter 1304. In addition, a thermocouple 1303 is provided on the surface of each battery (near the tab). The thermocouple 1303 is used to measure the initial temperature T at the tab. tab，1 The thermocouple 1303 will measure the initial temperature T at the tab. tab，1 Output to the temperature-electrical signal converter 1304. The temperature-electrical signal converter 1304 is used to convert the temperature T tab，1The BMS processor 1301 can be used to perform Figure 11 The functions of the first determining unit 1101, the second determining unit 1102, the first acquiring unit 1103, and the third determining unit 1104 are shown in FIG. The set current value and equivalent voltage acquirer 1302 is used to acquire M set current values and M equivalent voltages. When the BMS processor 1301 performs the functions of the actual first determining unit 1101, it can collect the current signal and voltage signal of each battery.

[0224] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a battery temperature acquisition device, the above-mentioned battery temperature acquisition method is implemented.

[0225] An embodiment of the present application also provides a computer program product, which, when executed on a computing device, enables the above-mentioned battery temperature acquisition method to be executed.

[0226] An embodiment of the present application also provides an energy storage system, comprising a plurality of batteries and the above-mentioned battery temperature acquisition device.

[0227] It should be noted that the term "plurality" in the embodiments of this application refers to two or more. Therefore, in this application, "plurality" can also be understood as "at least two." "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / ," unless otherwise specified, generally indicates that the associated objects are in an "or" relationship.

[0228] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0229] In the several embodiments provided in this application, it should be understood that the disclosed systems, systems, and methods can be implemented in other ways. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling, direct coupling, or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the system or unit can be electrical, mechanical, or other forms.

[0230] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0231] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions may be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic medium such as a floppy disk, a hard disk, a tape, a magnetic disk, or an optical medium such as a digital versatile disc (DVD), or a semiconductor medium such as a solid state disk (SSD).

Claims

1. A battery temperature acquisition method, characterized in that: The method comprises: Determine a first internal resistance of the battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values include a plurality of actual current values of the battery at a plurality of moments collected in a first time window, the plurality of voltage values include a plurality of actual voltage values corresponding to each of the plurality of actual current values collected in the first time window, the plurality of moments are within a set time window, and the starting time of the set time window is t; the plurality of current values also include M set current values in a second time window, and the plurality of first voltage values also include M equivalent voltages V in the second time window. eff , the M equivalent voltages V eff One-to-one correspondence with the M set current values, the set current value being 0; determining a battery state of charge of the battery at the plurality of moments; The temperature of the battery is obtained according to the first internal resistance value and the battery state of charge of the battery based on a battery thermal network model, wherein the battery thermal network model is used to characterize heat generation at the battery tab and / or heat generation of the battery body.

2. The method according to claim 1, characterized in that The method further comprises: Determining a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix including directly measured quantities and noise error parameters for the battery, and the parameter matrix to be identified is a parameter set including inherent physical properties of the battery itself and weights of the noise error parameters; The determining of a first internal resistance of the battery according to a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values includes: Determining a plurality of second voltage values corresponding to the current value at each of the plurality of moments based on the system matrix and a preliminary identification result of the parameter matrix to be identified at each of the plurality of moments, wherein the preliminary identification result of the parameter matrix to be identified at each of the plurality of moments is obtained by transforming a final identification result of the parameter matrix to be identified at a moment immediately preceding any moment; Obtaining a final identification result of the parameter matrix to be identified at each of the multiple moments according to a correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values, and a preliminary identification result of the parameter matrix to be identified at each of the multiple moments, wherein the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; A first internal resistance value of the battery is obtained according to a plurality of second internal resistance values of the battery.

3. The method according to claim 2, characterized in that The first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

4. The method according to any one of claims 1 to 3, characterized in that Determining the battery state of charge of the battery at the plurality of moments includes: Obtaining an initial battery state of charge at each of the multiple moments according to a current value at a moment preceding each of the multiple moments and a battery state of charge at a moment preceding each of the multiple moments; According to the first voltage value corresponding to the current value at each of the multiple moments and the second voltage value corresponding to the current value at each of the multiple moments, the initial battery state of charge at each of the multiple moments is corrected to obtain the corrected battery state of charge at each of the multiple moments.

5. The method according to any one of claims 1 to 4, characterized in that The acquiring the temperature of the battery according to the first internal resistance value of the battery and the battery state of charge based on a battery thermal network model includes: Obtaining a first internal resistance value of each of one or more nodes of the battery according to the first internal resistance value of the battery; acquiring, based on the multiple current values, multiple current values flowing through each of the one or more nodes of the battery; Obtaining an entropy thermal coefficient of the battery according to a battery state of charge at each of a plurality of moments of the battery; The temperature of the battery is obtained based on the battery thermal network model according to one or more of the first internal resistance value of each of the one or more nodes of the battery, the multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

6. A battery temperature acquisition device, characterized in that: The device comprises: a first determining unit, configured to determine a first internal resistance of a battery based on a plurality of current values and a plurality of first voltage values corresponding to each of the plurality of current values; the plurality of current values comprising a plurality of actual current values of the battery at a plurality of moments collected in a first time window, the plurality of voltage values comprising a plurality of actual voltage values corresponding to each of the plurality of actual current values collected in the first time window, the plurality of moments being within a set time window, and a start time of the set time window being t; the plurality of current values further comprising M set current values in a second time window, and the plurality of first voltage values further comprising M equivalent voltages V in the second time window eff , the M equivalent voltages V eff One-to-one correspondence with the M set current values, the set current value being 0; a second determining unit, configured to determine the battery state of charge of the battery at the plurality of moments; A first acquisition unit is configured to acquire the temperature of the battery based on a battery thermal network model according to the first internal resistance value and the battery state of charge of the battery, wherein the battery thermal network model is used to characterize the heat generated at the battery tab and / or the heat generated by the battery body.

7. The device according to claim 6, characterized in that The device further comprises: a third determining unit, configured to determine a system matrix and a parameter matrix to be identified of the battery, wherein the system matrix is a matrix including directly measured quantities and noise error parameters for the battery, and the parameter matrix to be identified is a parameter set including inherent physical properties of the battery itself and weights of the noise error parameters; The first determining unit includes: a fourth determining unit, configured to determine a plurality of second voltage values corresponding to the current value at each of the plurality of moments based on the system matrix and a preliminary identification result of the parameter matrix to be identified at each of the plurality of moments, wherein the preliminary identification result of the parameter matrix to be identified at each of the plurality of moments is obtained by transforming a final identification result of the parameter matrix to be identified at a moment immediately preceding any moment; a second acquiring unit, configured to acquire a final identification result of the parameter matrix to be identified at each of the multiple moments based on a correction matrix of the parameter matrix to be identified at each of the multiple moments, the multiple first voltage values, the multiple second voltage values, and a preliminary identification result of the parameter matrix to be identified at each of the multiple moments, wherein the final identification result of the parameter matrix to be identified at each of the multiple moments includes multiple second internal resistance values of the battery; The third acquiring unit is configured to acquire the first internal resistance value of the battery according to the multiple second internal resistance values of the battery.

8. The device according to claim 7, characterized in that The first internal resistance value of the battery is an average value of the sum of multiple second internal resistance values of the battery.

9. The device according to any one of claims 6 to 8, characterized in that The second determining unit includes: a fourth acquiring unit, configured to acquire an initial battery state of charge at each of the multiple moments according to the current value at a moment preceding each of the multiple moments and the battery state of charge at a moment preceding each of the multiple moments; a correction unit, configured to correct an initial battery state of charge at each of the multiple moments according to a first voltage value corresponding to the current value at each of the multiple moments and a second voltage value corresponding to the current value at each of the multiple moments, to obtain a corrected battery state of charge at each of the multiple moments.

10. The device according to any one of claims 6 to 9, characterized in that The first acquiring unit includes: a fifth acquiring unit, configured to acquire a first internal resistance value of each of the one or more nodes of the battery according to the first internal resistance value of the battery; a sixth acquiring unit, configured to acquire, based on the multiple current values, multiple current values flowing through each of the one or more nodes of the battery; a seventh acquiring unit, configured to acquire an entropy thermal coefficient of the battery according to a battery state of charge at each of a plurality of moments of the battery; an eighth acquisition unit, for acquiring the temperature of the battery based on the battery thermal network model according to one or more of the first internal resistance value of each of the one or more nodes of the battery, multiple current values flowing through each of the one or more nodes of the battery, and the entropy thermal coefficient of the battery.

11. A battery temperature acquisition device, characterized in that: include: A processor, a memory, an input device, and an output device, wherein the memory stores instructions, and when the instructions are executed by the processor, the battery temperature acquisition device executes the method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that The storage medium stores a computer program or instruction. When the computer program or instruction is executed by the battery temperature acquisition device, the method according to any one of claims 1 to 5 is implemented.

13. An energy storage system comprising a plurality of batteries and the battery temperature acquisition device according to any one of claims 6 to 10, wherein the battery temperature acquisition device is used to acquire the temperature of the batteries.

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