Battery direct current internal resistance prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the application discloses a battery direct current resistance prediction method and device, electronic equipment and storage medium, the method comprises the following steps: obtaining the first curve of the open circuit voltage of the first battery changing with SOC under the current SOH state, the second curve of the working condition voltage of the first battery changing with SOC under the current SOH state and the association information between the battery resistance and the direct current resistance of the first battery under the preset SOH state; the battery resistance of the first battery under the current SOH state is generated according to the first curve and the second curve; the direct current resistance of the first battery under the current SOH state is predicted according to the association information and the battery resistance of the first battery under the current SOH state, thereby greatly reducing the test of the direct current resistance of the battery, reducing the influence of excessive test on the performance of the battery, saving the cost and accelerating the product development process.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, electronic device, and storage medium for predicting the DC internal resistance of a battery. Background Technology

[0002] The DC internal resistance (DCR) of a battery is an important indicator for evaluating its health and power performance. Testing the DC internal resistance of a battery can assess the consistency of its internal resistance, the impedance values ​​of module soldering or connection terminals, and its ability to evaluate discharge power or energy.

[0003] Currently, the DC internal resistance of batteries is typically tested using direct testing methods or simulations employing three-dimensional electrochemical-thermal coupling models. However, when studying the DC internal resistance throughout the entire battery lifespan, regardless of whether direct testing methods or simulations are used, multiple DC internal resistance tests are required throughout the battery's lifespan. Excessive testing can negatively impact battery performance. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method, apparatus, electronic device, and storage medium for predicting the DC internal resistance of a battery, aiming to solve the technical problem that requires multiple DC internal resistance tests throughout the entire lifespan of a battery in the prior art.

[0005] To address the aforementioned problems, in a first aspect, embodiments of this application provide a method for predicting the DC internal resistance of a battery, comprising:

[0006] Acquire the first curve of the open circuit voltage of the first battery changing with SOC under the current SOH state, the second curve of the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under the preset SOH state;

[0007] The battery resistance value of the first battery in the current SOH state is generated based on the first curve and the second curve.

[0008] Based on the associated information and the battery resistance of the first battery in the current SOH state, the DC internal resistance of the first battery in the current SOH state is predicted.

[0009] Secondly, embodiments of this application also provide a device for predicting the DC internal resistance of a battery, comprising:

[0010] The first acquisition unit is used to acquire a first curve of the open circuit voltage of the first battery changing with SOC in the current SOH state, a second curve of the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery in a preset SOH state.

[0011] The first generation unit is used to generate the battery resistance value of the first battery in the current SOH state based on the first curve and the second curve.

[0012] The prediction unit is used to predict the DC internal resistance of the first battery in the current SOH state based on the associated information and the battery resistance value of the first battery in the current SOH state.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the DC internal resistance of a battery as described in the first aspect above.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the battery DC internal resistance prediction method described in the first aspect.

[0015] This application provides a method, apparatus, electronic device, and storage medium for predicting the DC internal resistance of a battery. The method acquires first and second curves showing the open-circuit voltage and operating voltage of a first battery changing with state of charge (SOC) under the current state of equilibrium (SOH), as well as the correlation information between the battery resistance and DC internal resistance under a preset SOH state. It then generates the battery resistance value under the current SOH state using the first and second curves, and finally predicts the DC internal resistance of the first battery under the current SOH state using the correlation information and the generated battery resistance value. This method requires only one DC internal resistance test to predict the battery's DC internal resistance under any SOH state, significantly reducing the need for extensive testing, minimizing the impact of excessive testing on battery performance, saving costs, and accelerating product development. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart illustrating the method for predicting the DC internal resistance of a battery provided in an embodiment of this application;

[0018] Figure 2 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0019] Figure 3 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0020] Figure 4 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0021] Figure 5 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0022] Figure 6 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0023] Figure 7 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0024] Figure 8 Another flowchart illustrating the method for predicting the DC internal resistance of a battery provided in this application embodiment;

[0025] Figure 9 SOC-OCV curves at different temperatures provided for embodiments of this application;

[0026] Figure 10 The SOC-OCV curve and U of the battery under a certain SOH state provided in the embodiments of this application are work -SOC curve;

[0027] Figure 11 The graphs showing the battery resistance and DC internal resistance of the battery under different SOH states provided in the embodiments of this application;

[0028] Figure 12 A graph showing the predicted DC internal resistance of the battery under different SOH states provided in the embodiments of this application;

[0029] Figure 13 A schematic diagram of a battery DC internal resistance prediction device provided in an embodiment of this application;

[0030] Figure 14 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] Please see Figure 1 , Figure 1 This is a flowchart illustrating the method for predicting the DC internal resistance of a battery provided in an embodiment of this application. The method for predicting the DC internal resistance of a battery described in this application is applied to a terminal electronic device, and is executed by application software installed in the terminal electronic device. The terminal electronic device can be a desktop computer, laptop computer, tablet computer, mobile phone, electric vehicle terminal, etc.

[0036] It should be noted that the application scenarios described in the following embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0037] It should also be noted that the first, second, third, and fourth batteries mentioned in the following embodiments are the same model of battery. The second, third, fourth, and fifth batteries can be substituted for each other in the following embodiments. Alternatively, this application may use a single battery to achieve the functions of the second, third, fourth, and fifth batteries in the following embodiments. In addition, the first, second, third, fourth, and fifth batteries mentioned in this application are preferably lithium batteries. That is to say, the batteries mentioned in this application are not limited to lithium batteries, but can also be other types of batteries, such as lead-acid batteries. The specific choice can be made according to the actual application, and this embodiment does not make specific limitations.

[0038] The method for predicting the DC internal resistance of a battery is described in detail below.

[0039] like Figure 1 As shown, the method includes the following steps S110 to S150.

[0040] S110. Obtain the first curve of the open circuit voltage of the first battery changing with SOC under the current SOH state, the second curve of the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under the preset SOH state.

[0041] S120. Generate the battery resistance value of the first battery in the current SOH state based on the first curve and the second curve;

[0042] S130. Based on the associated information, predict the DC internal resistance of the first battery in the current SOH state using the battery resistance value of the first battery in the current SOH state.

[0043] The preset SOH (State of Health) state can be any SOH state of the first battery before the current SOH state. The preset SOH state mentioned in this application is preferably the state when the health of the first battery is 100%. That is to say, the correlation information between the battery resistance and DC internal resistance of the first battery in the preset SOH state can be obtained when the first battery is subjected to BOL (Beginning of Life) test. At the same time, the current SOH state of the first battery can be either the state where the health of the first battery is 100% or a state other than 100%.

[0044] In this embodiment, the operating condition voltage is the operating condition voltage of the first battery during cyclic discharge in the current SOH state, i.e., the operating condition discharge voltage. The associated information is the relationship between the battery resistance and DC internal resistance of the first battery throughout its entire life cycle, which can be expressed by the following formula:

[0045] DCR=R+k

[0046] In the above formula, DCR is the DC internal resistance of the first battery under different SOH states, R is the battery resistance of the first battery under the corresponding SOH state, and k is a constant.

[0047] As can be seen from the above formula, the correlation information between the DC internal resistance and the battery internal resistance of the first battery under the current SOH state is k in the above formula. Therefore, after performing a BOL test on the first battery to obtain the correlation information between the battery resistance and the DC internal resistance of the first battery, that is, the constant k, it can be used as the correlation information between the battery resistance and the DC internal resistance of the first battery under different SOH states.

[0048] Specifically, a BOL test is performed on the first battery to obtain the correlation information between the battery resistance and the DC internal resistance. This includes: obtaining the first curve and the second curve of the open-circuit voltage and operating voltage of the first battery changing with SOC (State of Charge) when SOH is 100%; generating the battery resistance value of the first battery when SOH is 100% based on the first curve and the second curve; obtaining the initial DC internal resistance of the first battery during the BOL test; and obtaining the correlation information between the battery resistance value and the DC internal resistance of the first battery based on the battery resistance value and the initial DC internal resistance when SOH is 100%.

[0049] In addition, the battery resistance value of the first battery in the current SOH state can be calculated using the following formula.

[0050]

[0051] Among them, U OCV U is the open-circuit voltage. work I is the operating voltage, and I is the discharge current.

[0052] In some embodiments, such as Figure 2 As shown, before step S110, steps S210 and S220 are also included.

[0053] S210. Obtain a third curve showing the change of SOH of the second battery with the number of cycles; wherein the second battery and the first battery are the same model of battery;

[0054] S220. Determine the current SOH of the first battery based on the number of cycles of the first battery and the third curve.

[0055] In this embodiment, the second battery and the first battery are the same model of battery. After obtaining the third curve of the SOH of the second battery changing with the number of cycles, the SOH corresponding to the number of charge-discharge cycles of the first battery of this type can be obtained from the third curve. Therefore, after obtaining the third curve and the number of cycles performed by the first battery at the current SOH, the current SOH of the first battery can be determined from the third curve.

[0056] In some embodiments, such as Figure 3 As shown, step S110 includes steps S111 and S112.

[0057] S111. Obtain the average temperature of the first battery during cycling in the current SOH state;

[0058] S112. Determine the first curve of the open-circuit voltage of the first battery changing with SOC under the current SOH state based on the average temperature of the first battery during cycling under the current SOH state.

[0059] Specifically, since the temperature of the first battery varies under different SOH states during charge-discharge cycles, this embodiment, after obtaining the average temperature of the first battery during cycles in the current SOH state, can use this average temperature to correct the first curve of the open-circuit voltage versus SOC under the current SOH state and temperature, thereby reducing the error in predicting the DC internal resistance of the first battery under the current SOH state. The average temperature of the first battery during cycles in the current SOH state can be predetermined and can be obtained by testing a battery of the same model as the first battery.

[0060] In some embodiments, such as Figure 4 As shown, before step S111, steps S310 and S320 are also included.

[0061] S310. Obtain multiple fourth curves showing the temperature change of the third battery over time during different SOH state cycles; wherein the third battery is the same model as the first battery;

[0062] S320. Perform a time-weighted average on the multiple fourth curves to obtain the average temperature of the third battery under different SOH states during cycling.

[0063] In this embodiment, the third battery is the same model as the first battery. By testing multiple fourth curves of the temperature change of the third battery over time in different SOH states, and then performing a time-weighted average, the average temperature of the third battery in different SOH states can be obtained, and thus the average temperature of the first battery in the current SOH state can be determined.

[0064] In some embodiments, such as Figure 5 As shown, before step S112, steps S410 and S420 are also included.

[0065] S410. Obtain multiple first curves showing the change of open-circuit voltage with SOC when the fourth battery is cycled at different temperatures under the current SOH state; wherein the fourth battery and the first battery are the same model of battery;

[0066] S420. The multiple first curves are processed using linear interpolation to obtain the first curve of the fourth battery cycling at the average temperature of the current SOH state.

[0067] In this embodiment, the fourth battery has the same model as the first battery, and the current SOH state of the fourth battery is also the same as that of the first battery. By performing cyclic testing on the fourth battery at different temperatures under the current SOH state, and obtaining the first curve of the open-circuit voltage changing with SOC during the cyclic test, and then processing the multiple first curves obtained by linear interpolation based on the average temperature of the current SOH state, the first curve of the fourth battery cycling under the average temperature of the current SOH state can be obtained. Thus, the first curve of the open-circuit voltage changing with SOC of the first battery under the current SOH state can be corrected by the first curve of the first battery cycling under the current SOH state, thereby reducing the error in predicting the DC internal resistance of the first battery under the current SOH state.

[0068] In some embodiments, such as Figure 6 As shown, step S110 also includes steps S113 and S114.

[0069] S113. Obtain the fifth curve of the voltage of the first battery as a function of capacity under the current SOH state;

[0070] S114. Process the fifth curve to obtain a second curve showing the change of the operating voltage of the first battery under the current SOH state with SOC.

[0071] In this embodiment, the fifth curve is the curve of voltage change with capacity when the first battery is cyclically discharged in the current SOH state. After obtaining the fifth curve, it can be converted into a second curve of the operating voltage of the first battery in the previous SOH state as a function of SOC.

[0072] In some embodiments, such as Figure 7 As shown, step S110 also includes steps S115, S116 and S117.

[0073] S115. Obtain the sixth curve of the open circuit voltage of the first battery under BOL test as a function of SOC, the seventh curve of the operating voltage as a function of SOC, and the DC internal resistance.

[0074] S116. Generate the battery resistance value of the first battery under the BOL test based on the sixth curve and the seventh curve;

[0075] S117. Generate correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state based on the battery resistance and DC internal resistance of the first battery under the BOL test.

[0076] In this embodiment, the preset SOH state is the state of the first battery when it undergoes the BOL test, that is, the SOH of the first battery is 100%. By performing the BOL test on the first battery, the sixth curve showing the change of the open circuit voltage with the SOC, the seventh curve showing the change of the operating voltage with the SOC, and the DC internal resistance of the first battery can be obtained. Then, the battery resistance value of the first battery under the BOL test can be calculated by applying the formula for calculating the battery resistance value to the sixth and seventh curves. At this time, the DC internal resistance of the first battery can be directly obtained from the BOL test. Finally, the obtained battery resistance value and DC internal resistance are input into the formula DCR=R+k to obtain the correlation information between the battery resistance value and the DC internal resistance of the first battery under the preset SOH state, that is, the constant k.

[0077] In some embodiments, such as Figure 8 As shown, before step S110, steps S510, S520, S530 and S540 are also included.

[0078] S510. Obtain the first curve and the second curve of the open circuit voltage and operating voltage of the fifth battery under different SOH states as a function of SOC; wherein the fifth battery and the first battery are the same model of battery;

[0079] S520. Generate the battery resistance value of the fifth battery under different SOH states based on the first curve and the second curve of the fifth battery under different SOH states.

[0080] S530. Obtain the DC internal resistance of the fifth battery under different SOH states;

[0081] S540. Verify whether there is a correlation between the battery resistance and DC internal resistance of the fifth battery under different SOH states based on the battery resistance and DC internal resistance of the fifth battery under different SOH states.

[0082] Specifically, the main purpose of this embodiment is to verify the correlation between the battery resistance and DC internal resistance of the first battery under different SOH states, thereby ensuring the accuracy of the DC internal resistance prediction of the first battery under different SOH states in this application. The fifth battery has the same model number as the first battery.

[0083] In this embodiment, the SOC-OCV (Open Circuit Voltage) curves of the first battery under different SOH states and temperature cycles, the cycle number-SOH curve of the fifth cell during cycling, the charge / discharge voltage-capacity curves under different SOH states, and the temperature-time curves during cycling are tested. Then, the temperature-time curves during cycling are analyzed, and a time-weighted average is used to obtain the average discharge temperature of the fifth battery under different SOH states. Based on this average temperature, linear interpolation is applied to the SOC-OCV curves of the first battery under different SOH states and temperature cycles to obtain the SOC-OCV curves of the fifth battery under different SOH states and average temperature cycles. Each SOH state corresponds to an average temperature for which a SOC-OCV curve exists, ultimately yielding the following: Figure 9 The SOC-OCV curves at multiple different temperatures are shown. The voltage-capacity curves of the fifth battery under different SOH states can be obtained by cycling the fifth battery at the corresponding average temperature under the corresponding SOH state.

[0084] After obtaining the SOC-OCV curves and voltage-capacity curves of the fifth battery under different SOH states, the voltage-capacity curves can be converted into U values ​​of the fifth battery under different SOH states. work -SOC curve, by analyzing the SOC-OCV curves of the fifth cell at each SOH state and U work -Analyze the SOC curve, such as Figure 10 As shown, for Figure 10 By fitting the two curves, we can obtain the formula for calculating the battery's internal resistance mentioned in the above embodiments.

[0085] While calculating the internal resistance of the fifth battery under different SOH states, the DC internal resistance of the fifth battery under different SOH states was also measured, thus yielding the following results: Figure 11 The curves showing the battery resistance and DC internal resistance of the fifth battery as a function of SOH are presented. Through analysis of... Figure 11 By fitting the curve in the above example, we can obtain DCR = R + k. Meanwhile, for... Figure 11 The curve of battery resistance versus SOH is processed to obtain... Figure 12The curve showing the change of DC internal resistance of the fifth cell with SOH is predicted.

[0086] from Figure 12 As can be seen, the accuracy of the DC internal resistance prediction of the battery in this application is well matched with the actual DC internal resistance of the battery under different SOH states. Therefore, it can be determined that the constant k between the battery resistance and DC internal resistance under different SOH states will not change due to changes in usage. Thus, after obtaining the constant k between the battery resistance and DC internal resistance in advance, this application can directly calculate the battery resistance in advance, and thus directly predict the battery's DC internal resistance.

[0087] In the battery DC internal resistance prediction method provided in this application embodiment, a first curve showing the open-circuit voltage of the first battery changing with SOC in the current SOH state, a second curve showing the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance value and DC internal resistance of the first battery in a preset SOH state are obtained. The battery resistance value of the first battery in the current SOH state is generated based on the first curve and the second curve. The DC internal resistance of the first battery in the current SOH state is predicted based on the correlation information and the battery resistance value of the first battery in the current SOH state. This method requires only one DC internal resistance test to predict the DC internal resistance of the battery in any SOH state, greatly reducing the need for extensive testing, minimizing the impact of excessive testing on battery performance, saving costs, and accelerating product development.

[0088] This application also provides a battery DC internal resistance prediction device 100, which is used to perform any of the aforementioned battery DC internal resistance prediction methods.

[0089] Specifically, please refer to Figure 13 , Figure 13 This is a schematic diagram of a battery DC internal resistance prediction device 100 provided in an embodiment of this application.

[0090] like Figure 13 As shown, the battery DC internal resistance prediction device 100 includes: a first acquisition unit 110, a first generation unit 120, and a prediction unit 130.

[0091] The first acquisition unit 110 is used to acquire a first curve of the open circuit voltage of the first battery changing with SOC in the current SOH state, a second curve of the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery in a preset SOH state.

[0092] The first generation unit 120 is used to generate the battery resistance value of the first battery in the current SOH state based on the first curve and the second curve.

[0093] The prediction unit 130 is used to predict the DC internal resistance of the first battery in the current SOH state based on the associated information and the battery resistance value of the first battery in the current SOH state.

[0094] In other embodiments of the invention, the battery DC internal resistance prediction device 100 further includes a second acquisition unit and a first determination unit.

[0095] The second acquisition unit is used to acquire a third curve showing the change of SOH of the second battery with the number of cycles; wherein the second battery and the first battery are the same model of battery;

[0096] The first determining unit is used to determine the current SOH of the first battery based on the number of cycles of the first battery and the third curve.

[0097] In other embodiments of the invention, the first acquisition unit 110 includes: a third acquisition unit and a second determination unit.

[0098] The third acquisition unit is used to acquire the average temperature of the first battery during cycling in the current SOH state;

[0099] The second determining unit is used to determine a first curve of the open-circuit voltage of the first battery changing with SOC in the current SOH state based on the average temperature of the first battery cycling in the current SOH state.

[0100] In other embodiments of the invention, the battery DC internal resistance prediction device 100 further includes: a fourth acquisition unit and a time-weighted averaging unit.

[0101] The fourth acquisition unit is used to acquire multiple fourth curves showing the temperature change of the third battery over time during different SOH state cycles; wherein the third battery is the same model as the first battery;

[0102] The time-weighted averaging unit is used to perform time-weighted averaging on the multiple fourth curves to obtain the average temperature of the third battery under different SOH states during cycling.

[0103] In other embodiments of the invention, the battery DC internal resistance prediction device 100 further includes a fifth acquisition unit and a first processing unit.

[0104] The fifth acquisition unit is used to acquire multiple first curves showing the change of open-circuit voltage as a function of SOC when the fourth battery is cycled at different temperatures under the current SOH state; wherein the fourth battery and the first battery are the same model of battery;

[0105] The first processing unit is used to process the plurality of first curves using a linear interpolation method to obtain the first curve of the fourth battery cycling at the average temperature of the current SOH state.

[0106] In other embodiments of the invention, the first acquisition unit 110 further includes: a sixth acquisition unit and a second processing unit.

[0107] The sixth acquisition unit is used to acquire the fifth curve of the voltage change of the first battery with capacity under the current SOH state;

[0108] The second processing unit is used to process the fifth curve to obtain a second curve showing the change of the operating voltage of the first battery under the current SOH state with SOC.

[0109] In other embodiments of the invention, the first acquisition unit 110 further includes a seventh acquisition unit, a second generation unit, and a third generation unit.

[0110] The seventh acquisition unit is used to acquire the sixth curve of the open circuit voltage of the first battery under BOL test as a function of SOC, the seventh curve of the operating voltage as a function of SOC, and the DC internal resistance.

[0111] The second generation unit is used to generate the battery resistance value of the first battery under the BOL test based on the sixth curve and the seventh curve.

[0112] The third generation unit is used to generate correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state based on the battery resistance and DC internal resistance of the first battery under the BOL test.

[0113] In other embodiments of the invention, the battery DC internal resistance prediction device 100 further includes: an eighth acquisition unit, a fourth generation unit, a ninth acquisition unit, and a verification unit.

[0114] The eighth acquisition unit is used to acquire the first curve and the second curve of the open circuit voltage and operating voltage of the fifth battery under different SOH states as a function of SOC; wherein the fifth battery and the first battery are the same model of battery;

[0115] The fourth generation unit is used to generate the battery resistance value of the fifth battery under different SOH states based on the first curve and the second curve of the fifth battery under different SOH states.

[0116] The ninth acquisition unit is used to acquire the DC internal resistance of the fifth battery under different SOH states;

[0117] The verification unit is used to verify whether there is a correlation between the battery resistance and DC internal resistance of the fifth battery under different SOH states based on the battery resistance and DC internal resistance of the fifth battery under different SOH states.

[0118] The battery DC internal resistance prediction device 100 provided in this application embodiment is used to perform the above-mentioned acquisition of a first curve showing the change of the open-circuit voltage of the first battery with the state of charge (SOC) under the current state of zero electrical discharge (SOH), a second curve showing the change of the operating voltage of the first battery with the state of charge (SOC) under the current state of zero electrical discharge (SOH), and the correlation information between the battery resistance value and DC internal resistance of the first battery under a preset state of zero electrical discharge (SOH); to generate the battery resistance value of the first battery under the current state of zero electrical discharge (SOH) based on the first curve and the second curve; and to predict the DC internal resistance of the first battery under the current state of zero electrical discharge (SOH) based on the correlation information and the battery resistance value of the first battery under the current state of zero electrical discharge (SOH).

[0119] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned battery DC internal resistance prediction device 100 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0120] The aforementioned device for predicting the DC internal resistance of a battery can be implemented as a computer program, which can, for example... Figure 14 It runs on the electronic device shown.

[0121] Please see Figure 14 , Figure 14 This is a schematic block diagram of the electronic device provided in the embodiments of this application.

[0122] See Figure 14 The device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501, wherein the memory may include a storage medium 503 and internal memory 504.

[0123] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it causes the processor 502 to execute a method for predicting the DC internal resistance of the battery.

[0124] The processor 502 provides computing and control capabilities to support the operation of the entire device 500.

[0125] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for predicting the DC internal resistance of the battery.

[0126] This network interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 14 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the device 500 to which the present application is applied. The specific device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0127] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following functions: acquiring a first curve showing the open-circuit voltage of the first battery changing with SOC in the current SOH state, a second curve showing the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery in a preset SOH state; generating the battery resistance of the first battery in the current SOH state based on the first curve and the second curve; and predicting the DC internal resistance of the first battery in the current SOH state based on the correlation information and the battery resistance of the first battery in the current SOH state.

[0128] In one embodiment, before acquiring the first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, the second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, the processor 502 further implements the following steps: acquiring the third curve showing the SOH of the second battery changing with the number of cycles; wherein the second battery and the first battery are the same model of battery; and determining the current SOH of the first battery based on the number of cycles of the first battery and the third curve.

[0129] In one embodiment, when the processor 502 acquires the first curve of the open-circuit voltage of the first battery changing with SOC in the current SOH state, it further implements the following steps: acquiring the average temperature of the first battery cycling in the current SOH state; and determining the first curve of the open-circuit voltage of the first battery changing with SOC in the current SOH state based on the average temperature of the first battery cycling in the current SOH state.

[0130] In one embodiment, before obtaining the average temperature of the first battery cycling under the current SOH state, the processor 502 further performs the following steps: obtaining multiple fourth curves showing the temperature change of the third battery over time during cycling under different SOH states; wherein the third battery and the first battery are of the same model; and performing a time-weighted average on the multiple fourth curves to obtain the average temperature of the third battery cycling under different SOH states.

[0131] In one embodiment, before determining the first curve of the open-circuit voltage of the first battery changing with SOC under the current SOH state based on the average temperature of the first battery cycling under the current SOH state, the processor 502 further implements the following steps: obtaining multiple first curves of the open-circuit voltage of the fourth battery changing with SOC when cycling at different temperatures under the current SOH state; wherein the fourth battery and the first battery are the same model of battery; and processing the multiple first curves using linear interpolation to obtain the first curve of the fourth battery cycling at the average temperature of the current SOH state.

[0132] In one embodiment, when the processor 502 acquires the second curve of the operating voltage of the first battery in the current SOH state as a function of SOC, it further implements the following steps: acquiring the fifth curve of the voltage of the first battery in the current SOH state as a function of capacity; processing the fifth curve to obtain the second curve of the operating voltage of the first battery in the current SOH state as a function of SOC.

[0133] In one embodiment, when the processor 502 acquires the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, it further implements the following steps: acquiring the sixth curve of the open-circuit voltage of the first battery under BOL test as a function of SOC, the seventh curve of the operating voltage as a function of SOC, and the DC internal resistance; generating the battery resistance of the first battery under BOL test based on the sixth curve and the seventh curve; and generating the correlation information between the battery resistance and DC internal resistance of the first battery under the preset SOH state based on the battery resistance and DC internal resistance of the first battery under BOL test.

[0134] In one embodiment, before acquiring the first curve showing the open-circuit voltage of the first battery changing with SOC in the current SOH state, the second curve showing the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery in a preset SOH state, the processor 502 further implements the following steps: acquiring the first curve and the second curve showing the open-circuit voltage and operating voltage of the fifth battery changing with SOC in different SOH states; wherein, the fifth battery and the first battery are the same model of battery; generating the battery resistance of the fifth battery in different SOH states based on the first curve and the second curve of the fifth battery in different SOH states; acquiring the DC internal resistance of the fifth battery in different SOH states; verifying whether there is a correlation between the battery resistance and the DC internal resistance of the fifth battery in different SOH states based on the battery resistance and DC internal resistance of the fifth battery in different SOH states.

[0135] Those skilled in the art will understand that Figure 14 The embodiments of device 500 shown do not constitute a limitation on the specific configuration of device 500. In other embodiments, device 500 may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, in some embodiments, device 500 may include only a memory and processor 502. In such embodiments, the structure and function of the memory and processor 502 are similar to those shown. Figure 14 The embodiments shown are consistent and will not be described again here.

[0136] It should be understood that, in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors 502, digital signal processors 502 (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 502 may be a microprocessor 502, or it may be any conventional processor 502, etc.

[0137] In another embodiment of this application, a computer storage medium is provided. This storage medium can be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 5032, which, when executed by a processor 502, performs the following steps: acquiring a first curve showing the open-circuit voltage of the first battery changing with state of charge (SOC) in the current state of equilibrium (SOH), a second curve showing the operating voltage of the first battery changing with SOC in the current state of SOH, and correlation information between the battery resistance and DC internal resistance of the first battery in a preset state of SOH; generating the battery resistance value of the first battery in the current state of SOH based on the first curve and the second curve; and predicting the DC internal resistance of the first battery in the current state of SOH based on the correlation information and the battery resistance value of the first battery in the current state of SOH.

[0138] In one embodiment, before the processor executes the program instructions to obtain the first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, the second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, the processor further implements the following steps: obtaining the third curve showing the SOH of the second battery changing with the number of cycles; wherein the second battery and the first battery are the same model of battery; and determining the current SOH of the first battery based on the number of cycles of the first battery and the third curve.

[0139] In one embodiment, when the processor executes the program instructions to obtain a first curve showing the change of the open-circuit voltage of the first battery with SOC in the current SOH state, it further implements the following steps: obtaining the average temperature of the first battery cycling in the current SOH state; and determining the first curve showing the change of the open-circuit voltage of the first battery with SOC in the current SOH state based on the average temperature of the first battery cycling in the current SOH state.

[0140] In one embodiment, before the processor executes the program instructions to obtain the average temperature of the first battery cycling under the current SOH state, it further performs the following steps: obtaining multiple fourth curves showing the temperature change of the third battery over time in different SOH state cycles; wherein the third battery and the first battery are the same model of battery; and performing a time-weighted average on the multiple fourth curves to obtain the average temperature of the third battery cycling under different SOH states.

[0141] In one embodiment, before the processor executes the program instructions to determine the first curve of the open-circuit voltage of the first battery changing with SOC under the current SOH state based on the average temperature of the first battery cycling under the current SOH state, the processor further implements the following steps: obtaining multiple first curves of the open-circuit voltage of the fourth battery changing with SOC when cycling at different temperatures under the current SOH state; wherein the fourth battery and the first battery are the same model of battery; and processing the multiple first curves using a linear interpolation method to obtain the first curve of the fourth battery cycling at the average temperature of the current SOH state.

[0142] In one embodiment, when the processor executes the program instructions to obtain the second curve of the operating voltage of the first battery in the current SOH state as a function of SOC, it further implements the following steps: obtaining the fifth curve of the voltage of the first battery in the current SOH state as a function of capacity; processing the fifth curve to obtain the second curve of the operating voltage of the first battery in the current SOH state as a function of SOC.

[0143] In one embodiment, when the processor executes the program instructions to obtain the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, it further implements the following steps: obtaining a sixth curve showing the open-circuit voltage of the first battery changing with SOC under BOL testing, a seventh curve showing the operating voltage changing with SOC, and DC internal resistance; generating the battery resistance of the first battery under BOL testing based on the sixth curve and the seventh curve; and generating the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state based on the battery resistance and DC internal resistance of the first battery under BOL testing.

[0144] In one embodiment, before the processor executes the program instructions to obtain the first curve of the open-circuit voltage of the first battery changing with SOC in the current SOH state, the second curve of the operating voltage of the first battery changing with SOC in the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery in a preset SOH state, the processor further implements the following steps: obtaining the first curve and the second curve of the open-circuit voltage and operating voltage of the fifth battery changing with SOC in different SOH states; wherein, the fifth battery and the first battery are the same model of battery; generating the battery resistance of the fifth battery in different SOH states based on the first curve and the second curve of the fifth battery in different SOH states; obtaining the DC internal resistance of the fifth battery in different SOH states; verifying whether there is a correlation between the battery resistance and the DC internal resistance of the fifth battery in different SOH states based on the battery resistance and DC internal resistance of the fifth battery in different SOH states.

[0145] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Units with the same function may be grouped into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0147] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device 500 (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks.

[0150] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the DC internal resistance of a battery, characterized in that, include: The system acquires a first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, a second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state. The correlation information is the relationship between the battery resistance and the DC internal resistance of the first battery throughout its entire life cycle, satisfying: DCR = R + k; where DCR is the DC internal resistance of the first battery under different SOH states, R is the battery resistance of the first battery under the corresponding SOH state, and k is a constant. The battery resistance value of the first battery in the current SOH state is generated based on the first curve and the second curve. Based on the associated information and the battery resistance of the first battery in the current SOH state, predict the DC internal resistance of the first battery in the current SOH state; When generating the battery resistance value of the first battery in the current SOH state, the following conditions must be met: in, Open circuit voltage, I is the operating voltage, and I is the discharge current.

2. The method for predicting the DC internal resistance of a battery according to claim 1, characterized in that, Before acquiring the first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, the second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, the method further includes: Obtain a third curve showing the change of SOH of the second battery with the number of cycles; wherein the second battery and the first battery are the same model of battery; The current state of harm (SOH) of the first battery is determined based on the number of cycles of the first battery and the third curve.

3. The method for predicting the DC internal resistance of a battery according to claim 1, characterized in that, Obtain the first curve of the open-circuit voltage of the first battery as a function of SOC under the current SOH state, including: Obtain the average temperature of the first battery during cycling in the current SOH state; The first curve of the open-circuit voltage of the first battery under the current SOH state as a function of SOC is determined based on the average temperature of the first battery during cycling under the current SOH state.

4. The method for predicting the DC internal resistance of a battery according to claim 3, characterized in that, Before obtaining the average temperature of the first battery cycling under the current SOH state, the method further includes: Multiple fourth curves showing the temperature change of the third battery over time during different SOH state cycles are obtained; wherein the third battery is the same model as the first battery; The average temperature of the third battery under different SOH states is obtained by performing a time-weighted average on the multiple fourth curves.

5. The method for predicting the DC internal resistance of a battery according to claim 3, characterized in that, Before determining the first curve of the open-circuit voltage versus SOC of the first battery under the current SOH state based on the average temperature of the first battery cycling under the current SOH state, the method further includes: Multiple first curves showing the change of open-circuit voltage as a function of SOC when the fourth battery is cycled at different temperatures under the current SOH state are obtained; wherein the fourth battery is the same model as the first battery; The multiple first curves are processed using linear interpolation to obtain the first curve of the fourth battery cycling at the average temperature of the current SOH state.

6. The method for predicting the DC internal resistance of a battery according to any one of claims 1-5, characterized in that, Obtain a second curve showing the operating voltage of the first battery under the current SOH state as a function of SOC, including: Obtain the fifth curve showing the voltage of the first battery as a function of capacity under the current SOH state; The fifth curve is processed to obtain a second curve showing the change of the operating voltage of the first battery under the current SOH state with SOC.

7. The method for predicting the DC internal resistance of a battery according to any one of claims 1-5, characterized in that, Obtaining the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state includes: Obtain the sixth curve of the open-circuit voltage of the first battery under BOL test as a function of SOC, the seventh curve of the operating voltage as a function of SOC, and the DC internal resistance. The battery resistance value of the first battery under the BOL test is generated based on the sixth curve and the seventh curve. Based on the battery resistance and DC internal resistance of the first battery under the BOL test, the correlation information between the battery resistance and DC internal resistance of the first battery under the preset SOH state is generated.

8. The method for predicting the DC internal resistance of a battery according to any one of claims 1-5, characterized in that, Before acquiring the first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, the second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and the correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state, the method further includes: Obtain the first curve and the second curve of the open circuit voltage and operating voltage of the fifth battery as a function of SOC under different SOH states; wherein the fifth battery and the first battery are the same model of battery; The battery resistance values ​​of the fifth battery under different SOH states are generated based on the first curve and the second curve of the fifth battery under different SOH states. Obtain the DC internal resistance of the fifth battery under different SOH states; Verify whether there is a correlation between the battery resistance and DC internal resistance of the fifth battery under different SOH states.

9. A device for predicting the DC internal resistance of a battery, characterized in that, include: The first acquisition unit is used to acquire a first curve showing the open-circuit voltage of the first battery changing with SOC under the current SOH state, a second curve showing the operating voltage of the first battery changing with SOC under the current SOH state, and correlation information between the battery resistance and DC internal resistance of the first battery under a preset SOH state; the correlation information is the relationship between the battery resistance and the DC internal resistance of the first battery throughout its entire life cycle, satisfying: DCR=R+k; DCR is the DC internal resistance of the first battery under different SOH states, R is the battery resistance of the first battery under the corresponding SOH state, and k is a constant; The first generation unit is used to generate the battery resistance value of the first battery in the current SOH state based on the first curve and the second curve. The prediction unit is used to predict the DC internal resistance of the first battery in the current SOH state based on the associated information and the battery resistance value of the first battery in the current SOH state. When generating the battery resistance value of the first battery in the current SOH state, the following conditions must be met: in, Open circuit voltage, I is the operating voltage, and I is the discharge current.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for predicting the DC internal resistance of a battery as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the method for predicting the DC internal resistance of a battery as described in any one of claims 1 to 8.

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