A battery capacity attenuation rate estimation method and device, electronic equipment and medium

Abstract

The application provides a battery capacity attenuation rate estimation method and device, electronic equipment and medium. The method obtains battery data at a previous time and a current current, obtains a current heat generation and a current battery temperature based on a battery electrical model and a battery temperature model, obtains a current battery capacity attenuation rate based on a previous battery capacity attenuation rate, a previous battery temperature, a previous current and a preset battery capacity attenuation model, and obtains a battery capacity attenuation rate curve based on battery capacity attenuation rates at multiple times and time steps between the multiple times, so as to estimate the battery capacity attenuation rate. The battery life can be accurately reflected based on the battery capacity attenuation rate, so that the battery life is predicted.

Description

Technical Field

[0001] This application relates to the field of power battery technology, specifically to a method, device, electronic device, and medium for predicting battery capacity decay rate. Background Technology

[0002] With the electric vehicle market growing rapidly and the number of electric vehicles on the road exploding, battery lifespan has become one of the key factors that consumers and OEMs consider. Therefore, predicting the battery lifespan of battery packs is a crucial aspect of new energy vehicle research.

[0003] In patent CN108919129A, a method for predicting the lifespan of a power battery under time-varying operating conditions is provided. This method calculates the lifespan of the power battery based on the percentage of temperature range and depth of discharge range. However, this method does not consider the different decay rates for different capacity degradation amounts, leading to inaccurate predictions. In another related patent CN110658460A, a method and apparatus for predicting the battery lifespan of a battery pack are provided. This method predicts battery lifespan based on cycle capacity loss and calendar capacity loss. However, this method does not consider the impact of capacity degradation rate on the decay rate, thus its prediction of battery lifespan also contains errors. In summary, existing methods cannot accurately predict battery lifespan. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, this application provides a method, apparatus, electronic device and medium for predicting battery capacity degradation rate, so as to solve the above-mentioned technical problem of inaccurate prediction of battery life.

[0005] This application provides a method for estimating battery capacity degradation rate, including acquiring the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity degradation rate at the previous moment; obtaining the heat generated at the current moment based on the previous moment's current and a preset battery electrical model; inputting the current moment's heat generated into a preset battery temperature model to obtain the current moment's battery temperature, wherein the current moment's battery temperature includes the current moment's highest battery temperature and the current moment's lowest battery temperature; and obtaining the current moment's battery capacity degradation rate, the previous moment's battery temperature, the previous moment's current moment's current moment's battery temperature based on the previous moment's battery capacity degradation rate, the previous moment's battery temperature, the previous moment's current and the preset battery capacity degradation model. Capacity decay rate; using the current current, heat generation, battery temperature, and battery capacity decay rate at the current moment as the new previous current, heat generation, battery temperature, and battery capacity decay rate, the current at the next moment is obtained and used as the new current at the current moment. This process is repeated multiple times to obtain battery capacity decay rates at multiple moments. A battery capacity decay rate curve is determined based on the multiple battery capacity decay rates and the time step between the multiple moments. The time step is calculated based on a preset step size threshold to estimate the battery capacity decay rate.

[0006] In one embodiment of this application, obtaining the heat generation at the current moment based on the previous moment's current and a preset battery electrical model includes: constructing the preset battery electrical model based on the equivalent resistance and voltage source, such that the current magnitude of the preset battery electrical model is the previous moment's current; and calculating the heat generation at the current moment based on the equivalent resistance and the previous moment's current.

[0007] In one embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes: if the highest battery temperature at the previous moment is greater than or equal to a preset high temperature threshold, then battery thermal management is activated, and the thermal management water temperature is a preset cooling temperature; if the lowest battery temperature at the previous moment is less than or equal to a preset low temperature threshold, then battery thermal management is activated, and the thermal management water temperature is a preset heating temperature; if the highest battery temperature at the previous moment is less than the preset high temperature threshold and the lowest battery temperature at the previous moment is greater than the preset low temperature threshold, then battery thermal management is deactivated.

[0008] In one embodiment of this application, the heat generated at the current moment is input into a preset battery temperature model to obtain the battery temperature at the current moment, including: the highest battery temperature at the current moment is calculated using the following formula:

[0009]

[0010] Among them, T max The current highest battery temperature, T′ max Q represents the highest battery temperature at the previous moment.cell This represents the current battery heat generation, C*m is the cell's thermal capacity, k0 is the on / off state of the battery thermal management system (0 for off, 1 for on), and T... water It is the temperature of the thermal management water; T air The ambient temperature is Δt, the time step is k. water1 k air1 k water2 k air2 Preset calibration coefficients;

[0011] In one embodiment of this application, the process of inputting the current heat generation into a preset battery temperature model to obtain the current battery temperature further includes: calculating the current minimum battery temperature using the following formula:

[0012]

[0013] Among them, T min This refers to the lowest battery temperature at the current moment; T′ min Q is the lowest battery temperature at the previous moment. cell This represents the current battery heat generation, C*m is the cell's thermal capacity, k0 is the battery thermal management on / off status (0 for off, 1 for on), and T... water It refers to the thermal management water temperature; T air The ambient temperature is Δt, the time step is k. water1 k air1 k water2 k air2 These are the preset calibration coefficients.

[0014] In one embodiment of this application, obtaining the battery capacity decay rate at the current moment based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and a preset battery capacity decay model includes: differentiating time based on the calendar capacity decay rate formula and then discretizing it to obtain the calendar capacity decay rate time differential formula; differentiating time based on the cyclic capacity decay rate and then discretizing it to obtain the cyclic capacity decay rate time differential formula; and obtaining the preset battery capacity decay model based on the calendar capacity decay rate time differential formula and the decay rate time differential formula.

[0015] In one embodiment of this application, before obtaining the previous current, current, heat generation, battery temperature, and battery capacity decay rate, the method further includes: receiving a preset step size calculation threshold, which includes a maximum capacity change threshold, a maximum open-circuit voltage change threshold, a maximum battery temperature change threshold, a maximum battery temperature change threshold, and an operating condition threshold; and obtaining a time step based on the preset step size calculation threshold.

[0016] In one embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes: collecting the highest and lowest ambient temperatures of the target area for each day of the year; obtaining an ambient temperature function of the target area for the year based on the highest and lowest ambient temperatures of each day; obtaining a target ambient temperature based on the ambient temperature function; and using the target ambient temperature as the ambient temperature coefficient of the preset battery temperature model.

[0017] In one embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes: collecting the highest and lowest ambient temperatures of the target area for each day of the year; randomly sampling between the highest and lowest ambient temperatures, obtaining multiple sampled temperature values, and obtaining the target ambient temperature based on the average of the sampled temperature values; and using the target ambient temperature as the ambient temperature coefficient of the preset battery temperature model.

[0018] This application also provides a battery capacity degradation rate prediction device, which includes a data acquisition module for acquiring the previous current, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity degradation rate at the previous moment; a battery electrical model module for obtaining the heat generated at the current moment based on the previous current and a preset battery electrical model; a battery temperature model module for inputting the current heat generated into a preset battery temperature model to obtain the current battery temperature, wherein the current battery temperature includes the current maximum battery temperature and the current minimum battery temperature; and a battery capacity degradation rate calculation module for calculating the battery capacity degradation rate based on the previous battery capacity degradation rate, the previous battery temperature, and the previous current. The current and a preset battery capacity decay model are used to obtain the battery capacity decay rate at the current moment. An iterative module uses the current current, the current heat generation, the current battery temperature, and the current battery capacity decay rate as the new previous current, new previous heat generation, new previous battery temperature, and new previous battery capacity decay rate to obtain the current at the next moment and use it as the new current at the current moment. This process is repeated multiple times to obtain battery capacity decay rates at multiple moments. A battery capacity decay rate prediction module is used to determine a battery capacity decay rate curve based on the multiple battery capacity decay rates and the time step between multiple moments. The time step is calculated based on a preset step size threshold to predict the battery capacity decay rate.

[0019] Embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the battery capacity degradation rate prediction method as described in any of the above embodiments.

[0020] Embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform a battery capacity degradation rate estimation method as described in any of the above embodiments.

[0021] The beneficial effects of the present invention are as follows: The battery capacity decay rate prediction method, apparatus, electronic device, and medium in the embodiments of this application acquire battery data from the previous moment and current current. Based on a battery electrical model and a battery temperature model, the method obtains the current heat generation and current battery temperature. Based on the battery capacity decay rate from the previous moment, the battery temperature from the previous moment, the current from the previous moment, and a preset battery capacity decay model, the method obtains the current battery capacity decay rate. Based on the battery capacity decay rate at multiple moments and the time step between multiple moments, a battery capacity decay rate curve is obtained to predict the battery capacity decay rate. The battery capacity decay rate can accurately reflect the battery's lifespan, thereby achieving the function of predicting battery lifespan.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0024] Figure 1 This is a schematic diagram of a battery capacity degradation rate prediction system shown in an exemplary embodiment of this application;

[0025] Figure 2 This is a schematic diagram illustrating a battery capacity degradation rate prediction method in an exemplary embodiment of this application;

[0026] Figure 3 This is a schematic diagram of discharge power shown in an exemplary embodiment of this application;

[0027] Figure 4 This is a schematic diagram illustrating the annual temperature of the target area as shown in an exemplary embodiment of this application;

[0028] Figure 5 This is an exemplary embodiment of the present application illustrating the relationship between open-circuit voltage and remaining charge.

[0029] Figure 6This is a schematic diagram illustrating a battery thermal management control strategy in an exemplary embodiment of this application;

[0030] Figure 7 This is a schematic diagram of the battery internal resistance shown in an exemplary embodiment of this application;

[0031] Figure 8 This is a schematic diagram of a battery cell charging window shown in an exemplary embodiment of this application;

[0032] Figure 9 This is a schematic diagram illustrating the battery capacity degradation rate calculation process in an exemplary embodiment of this application;

[0033] Figure 10 This is a schematic diagram illustrating the calculation results of battery capacity decay rate in an exemplary embodiment of this application;

[0034] Figure 11 This is a block diagram illustrating a battery capacity degradation rate estimation device in an exemplary embodiment of this application;

[0035] Figure 12 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0036] The embodiments of this application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0037] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0038] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0039] Please see Figure 1 , Figure 1 This is a schematic diagram of a battery capacity degradation rate prediction system illustrated in an exemplary embodiment of this application. The system includes a data acquisition unit 101 and a computer device 102. The computer device 102 can be at least one of a microcomputer, an embedded computer, or a neural network computer. The computer device 102 receives and acquires the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity degradation rate at the previous moment. Based on the current at the previous moment and a preset battery electrical model, the computer device 102 obtains the heat generated at the current moment. The heat generated at the current moment is input into a preset battery temperature model to obtain the battery temperature at the current moment. The battery temperature at the current moment includes the highest battery temperature and the lowest battery temperature at the current moment. Based on the battery capacity degradation rate at the previous moment and the current current at the current moment, the system calculates the current heat generated. The current battery capacity decay rate is obtained by using the battery temperature, the previous current, and a preset battery capacity decay rate model. The current current, the current heat generation, the current battery temperature, and the current battery capacity decay rate are used as the new previous current, the new previous heat generation, the new previous battery temperature, and the new previous battery capacity decay rate. The current at the next moment is obtained and used as the new current at the current moment. The above steps are repeated multiple times to obtain the battery capacity decay rate at multiple moments. The battery capacity decay rate curve is determined based on the multiple battery capacity decay rates and the time step between multiple moments. The time step is calculated based on a preset step size threshold to predict the battery capacity decay rate. The data acquisition device 101 can be various current sensors or temperature sensors used to measure current and temperature and upload them to the computer device 102.

[0040] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a battery capacity degradation rate prediction method in an exemplary embodiment of this application. In an exemplary embodiment, the battery capacity degradation rate prediction method includes at least steps S210 to S260, which are described in detail below:

[0041] Step S210: Obtain the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity decay rate at the previous moment.

[0042] In one embodiment of this application, before step S210, the method further includes: receiving a preset step size calculation threshold, which includes a maximum capacity change threshold, a maximum open-circuit voltage change threshold, a maximum battery temperature change threshold, a maximum battery temperature change threshold, and an operating condition threshold; and obtaining a time step based on the preset step size calculation threshold.

[0043] In one embodiment of this application, the time step Δt is not constant but variable, achieved by introducing ΔC. max(Maximum capacity change threshold), ΔOCV max (OCV maximum change threshold) (Brain maximum temperature change threshold) (Behicle minimum temperature maximum change threshold) (Operating condition threshold), calculate the time step Δt. The time step is smaller under the cyclic capacity decay condition and larger under the calendar capacity decay condition, which can shorten the calculation time. The expression for Δt is as follows:

[0044]

[0045] In equation (1), Δt is the time step, and ΔC is the time step. max ΔOCV is the threshold for the maximum change in capacity. max The threshold for the maximum change in open-circuit voltage. This is the threshold for the maximum change in the battery's highest temperature. This is the threshold for the maximum change in the lowest battery temperature. This represents the operating condition threshold.

[0046] Step S220: Calculate the heat generation at the current moment based on the current at the previous moment and the preset battery electrical model.

[0047] In one embodiment of this application, a preset battery electrical model is constructed based on the equivalent resistance and voltage source, such that the current magnitude of the preset battery electrical model is the current at the previous moment; the heat generation at the current moment is calculated based on the equivalent resistance and the current at the previous moment.

[0048] In one embodiment of the present invention, the battery electrical model is an equivalent circuit model, represented by a resistor R0 and a voltage source OCV, where R0(SOC, T), the battery internal resistance, is a function of the battery's SOC (remaining charge) and temperature. Figure 7 As shown, Figure 7 This is a schematic diagram of the battery internal resistance shown in an exemplary embodiment of this application. The resistance is calculated using linear interpolation. The open-circuit voltage (OCV) of the battery is a function of the battery's SOC (remaining charge). Figure 5 As shown, Figure 5 This is an exemplary embodiment of the present application illustrating the relationship between open-circuit voltage and remaining charge, which is obtained through linear interpolation during the calculation process, as detailed below:

[0049] U L =OCV-I*R0 Formula (2)

[0050]

[0051] OCV = f OCV (SOC) Equation (4)

[0052] Qcell =I 2 *R0 Equation (5)

[0053]

[0054] Among them, U in equations (2), (3), (4), (5), and (6) L R0 is the terminal voltage, OCV is the voltage source, I is the current (positive for discharging, negative for charging), R0 is the equivalent resistance, T is the battery temperature, SOC is the remaining charge, and Q is the voltage source. cell This represents the battery's heat generation, Δt is the time step, and C is the capacitance.

[0055] Step S230: Input the heat generated at the current moment into the preset battery temperature model to obtain the battery temperature at the current moment. The battery temperature at the current moment includes the highest battery temperature and the lowest battery temperature at the current moment.

[0056] In one embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes collecting the highest and lowest ambient temperatures of the target area for each day of the year to obtain a schematic diagram of the annual temperature of the target area, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of the annual temperature of the target area, which includes the highest and lowest ambient temperatures of the target area every day of the year. Based on the highest and lowest ambient temperatures of the day, the ambient temperature function of the target area for the year is obtained. Based on the ambient temperature function, the target ambient temperature is obtained and used as the ambient temperature coefficient of the preset battery temperature model.

[0057] In another embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes: collecting the highest and lowest ambient temperatures of the target area for each day of the year; randomly sampling between the highest and lowest ambient temperatures, obtaining multiple sampled temperature values, and obtaining the target ambient temperature based on the average of the sampled temperature values; and using the target ambient temperature as the ambient temperature coefficient of the preset battery temperature model.

[0058] In one embodiment of this application, before inputting the heat generated at the previous moment into the preset battery temperature model, the method further includes: if the highest battery temperature at the previous moment is greater than or equal to a preset high temperature threshold, then battery thermal management is activated, and the thermal management water temperature is a preset cooling temperature; if the lowest battery temperature at the previous moment is less than or equal to a preset low temperature threshold, then battery thermal management is activated, and the thermal management water temperature is a preset heating temperature; if the highest battery temperature at the previous moment is less than the preset high temperature threshold and the lowest battery temperature at the previous moment is greater than the preset low temperature threshold, then battery thermal management is deactivated.

[0059] In one embodiment of this application, the battery thermal management strategy is as follows: Figure 6As shown, Figure 6 This is a schematic diagram of a battery thermal management control strategy shown in an exemplary embodiment of this application. When the battery's highest temperature is greater than or equal to 35 degrees Celsius, the fast charging cooling of the battery thermal management is activated, and when the battery's highest temperature is less than or equal to 30 degrees Celsius, the fast charging cooling is deactivated. The thermal management water temperature for fast charging cooling is 20 degrees Celsius. When the battery's lowest temperature is less than or equal to 15 degrees Celsius, the fast charging heating of the battery thermal management is activated, and when the battery's lowest temperature is greater than or equal to 20 degrees Celsius, the fast charging heating is deactivated. The thermal management water temperature for fast charging heating is 30 degrees Celsius.

[0060] In one embodiment of this application, the heat generated at the current moment is input into a preset battery temperature model to obtain the battery temperature at the current moment, including: the highest battery temperature at the current moment is calculated using the following formula:

[0061]

[0062] In equation (7), T max It is the current battery temperature, T′. max Q represents the highest battery temperature at the previous moment. cell This represents the current battery heat generation, C*m is the cell's thermal capacity, k0 is the battery thermal management on / off state (0 for off, 1 for on), and T... water It refers to the thermal management water temperature; T air The ambient temperature is Δt, the time step is k. water1 k air1 k water2 k air2 Preset calibration coefficients;

[0063] In one embodiment of this application, the heat generated at the current moment is input into a preset battery temperature model to obtain the battery temperature at the current moment, including: the minimum battery temperature at the current moment is calculated using the following formula:

[0064]

[0065] In equation (8), T min It is the lowest battery temperature at the current moment, T′ min Q is the lowest battery temperature at the previous moment. cell This represents the current battery heat generation, C*m is the cell's thermal capacity, k0 is the battery thermal management on / off state (0 for off, 1 for on), and T... water It refers to the thermal management water temperature; T air The ambient temperature is Δt, the time step is k. water1 k air1 k water2 k air2 Preset calibration coefficients;

[0066] Step S240: Obtain the current battery capacity decay rate based on the battery capacity decay rate, battery temperature, current, and preset battery capacity decay model from the previous moment.

[0067] In one embodiment of this application, obtaining the battery capacity decay rate at the current moment based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and a preset battery capacity decay model includes: differentiating time based on the calendar capacity decay rate formula and then discretizing it to obtain the calendar capacity decay rate time differential formula; differentiating time based on the cyclic capacity decay rate and then discretizing it to obtain the cyclic capacity decay rate time differential formula; and obtaining the preset battery capacity decay model based on the calendar capacity decay rate time differential formula and the decay rate time differential formula.

[0068] In one embodiment of this application, the calendar capacity decay rate formula is specifically as follows:

[0069]

[0070] In equation (9), K cal Ea cal Z cal K cyc Ea cyc and Z cyc Here, T is a preset coefficient, T is the battery temperature, and t is time.

[0071] In one embodiment of this application, the formula for the cycle capacity decay rate is specifically as follows:

[0072]

[0073] In equation (10), K cal Ea cal Z cal K cyc Ea cyc and Z cyc Here, T is a preset coefficient, N is the battery temperature, and N is the number of charge / discharge cycles.

[0074] By performing equivalent transformations on equations (9) and (10), changing the unit of t in the calendar capacity decay rate formula from days to seconds, and changing the unit of N in the cycle capacity decay rate formula from cycles to amperes-seconds, we obtain the following equation:

[0075]

[0076]

[0077] In equations (11) and (12), K cal Ea cal Z cal Kcyc Ea cyc and Z cyc The preset coefficients are: t is time (in seconds), I is current (in amperes), C is battery capacity (in watt-hours), and T is battery temperature.

[0078] The time derivative based on the calendar capacity decay rate formula includes:

[0079]

[0080] In equation (13), Q represents the calendar capacity decay rate. loss K is the battery capacity degradation rate at the current moment. cal Ea cal Z cal K cyc Ea cyc and Z cyc Here, T is the highest or lowest temperature of the battery, and t is the current time.

[0081] Then discretize the time:

[0082]

[0083] In equation (14), Let Q′ be the calendar capacity decay rate. loss K represents the battery capacity degradation rate at the previous moment. cal Ea cal Z cal K cyc Ea cyc and Z cyc Δt is a preset coefficient, T is the highest or lowest temperature of the battery, and Δt is the time step.

[0084] The time derivative based on the cyclic capacity decay rate formula includes:

[0085]

[0086] In equation (15), Q is the cycle capacity decay rate. loss K is the battery capacity degradation rate at the current moment. cal Ea cal Z cal K cyc Ea cyc and Z cyc Here, T is the battery's highest or lowest temperature, I is the current, and C is the capacitance.

[0087] Then discretize the time:

[0088]

[0089] In equation (16), Let Q′ be the cycle capacity decay rate. loss K represents the battery capacity degradation rate at the previous moment. cal Ea cal Z cal K cyc Ea cyc and Z cyc Here, T is the highest or lowest temperature of the battery, Δt is the time step, I is the current, and C is the capacitance.

[0090] In one embodiment of this application, the preset battery capacity decay model is as follows:

[0091]

[0092] In equation (17), Q loss Let K be the battery capacity degradation rate, t be the current time, t′ be the previous time, and K be the battery capacity degradation rate. cal Ea cal Z cal K cyc Ea cyc and Z cyc All are preset coefficients, where T is the highest or lowest temperature of the battery, I is the current, and C is the battery capacity.

[0093] Step S250: The current current, heat generation, battery temperature, and battery capacity decay rate at the current moment are used as the current at the previous moment, the new heat generation at the previous moment, the new battery temperature at the previous moment, and the new battery capacity decay rate at the previous moment. The current at the next moment is obtained and used as the new current at the current moment. The above steps are repeated multiple times to obtain the battery capacity decay rate at multiple moments.

[0094] In one embodiment of this application, the initial battery capacity decay rate is 0%. Through multiple iterative calculations, the battery capacity decay rate at multiple times is obtained based on multiple time steps, and the battery life is predicted based on the multiple battery capacity decay rates.

[0095] Step S260: Determine the battery capacity decay rate curve based on multiple battery capacity decay rates and time steps between multiple time points. The time step is obtained by calculating a threshold based on a preset step size in order to predict the battery capacity decay rate.

[0096] In one embodiment of the present invention, the battery capacity decay rate curve is shown as follows: Figure 10 As shown, in Figure 10 It includes multiple time periods and multiple battery capacity decay rates, and the battery life can be predicted based on the battery capacity decay rate curve.

[0097] Please see Figure 9 , Figure 9 This is a schematic diagram illustrating a battery capacity degradation rate calculation process in an exemplary embodiment of this application. The process includes calculating T... max (0), T min (0), SOC(0), Q loss (0) and other parameters are initialized to determine the operating condition input expression. The operating condition for calculating the battery capacity degradation rate consists of a combination of three types of operating conditions: battery discharge condition, battery charging condition, and static condition. Generally, the battery discharge condition is input with the battery discharge power (denoted as P). d (t), such as Figure 3 As shown, Figure 3 This is a schematic diagram of discharge power shown in an exemplary embodiment of this application. The battery charging condition specifies when charging begins (denoted as I). c (t) Its value is related to SOC and temperature, such as Figure 8 As shown, Figure 8 This is a schematic diagram of the battery cell charging window shown in an exemplary embodiment of this application. The resting condition is defined as the resting condition, excluding the discharging and charging conditions (denoted as I). st =0). To unify these three operating conditions into a single expression, this patent introduces two step functions f. st (t) and f c (t):

[0098]

[0099]

[0100] Unified expression for operating condition input:

[0101]

[0102] In equation (18), U L (t) represents the battery terminal voltage, determined by a preset battery electrical model; I c (t) represents the current, determined by the preset battery electrical model and the preset battery temperature model, P d Input battery discharge power

[0103] Then, the data collector collects operating condition data or output results as inputs to the time step model, battery electrical model, battery temperature model, and battery capacity decay rate model, respectively. The time step model obtains the time step, the battery electrical model obtains the current heat generation, current terminal voltage, and current remaining power, the battery temperature model obtains the current maximum and minimum battery temperatures and current current, and the battery capacity decay rate model obtains the battery capacity decay rate. The result of one iteration is used as input to the entire data loop to obtain multiple battery capacity decay rates for multiple iterations until the current time reaches the maximum time threshold, thus completing the prediction of the battery capacity decay rate.

[0104] Figure 11 This is a block diagram illustrating a battery capacity degradation rate prediction device, as shown in an exemplary embodiment of this application. Figure 11 As shown, the exemplary battery capacity degradation rate prediction device includes a data acquisition module 1101, a battery electrical model module 1102, a battery temperature model module 1103, a battery capacity degradation rate calculation module 1104, an iteration module 1105, and a battery capacity degradation rate prediction module 1106.

[0105] The data acquisition module 1101 is used to acquire the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity decay rate at the previous moment.

[0106] Battery electrical model module 1102 is used to obtain the heat generation at the current moment based on the current at the previous moment and a preset battery electrical model;

[0107] The battery temperature model module 1103 is used to input the heat generated at the current moment into the preset battery temperature model to obtain the battery temperature at the current moment. The battery temperature at the current moment includes the highest battery temperature and the lowest battery temperature at the current moment.

[0108] The battery capacity decay rate calculation module 1104 is used to obtain the current battery capacity decay rate based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and the preset battery capacity decay model.

[0109] The iteration module 1105 takes the current, heat generation, battery temperature, and battery capacity decay rate at the current moment as the new previous moment's current, heat generation, battery temperature, and battery capacity decay rate, obtains the current at the next moment and uses it as the new current at the current moment, and repeats the above steps multiple times to obtain the battery capacity decay rate at multiple moments.

[0110] The battery capacity degradation rate prediction module 1106 is used to determine the battery capacity degradation rate curve based on multiple battery capacity degradation rates and time steps between multiple times. The time step is obtained by calculating a threshold based on a preset step size in order to predict the battery capacity degradation rate.

[0111] It should be noted that the battery capacity degradation rate prediction device and the battery capacity degradation rate prediction method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the battery capacity degradation rate prediction device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0112] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the battery capacity degradation rate prediction method provided in the above embodiments.

[0113] Figure 12 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 12 The computer system 1200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0114] like Figure 12 As shown, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1202 or programs loaded from storage portion 1208 into Random Access Memory (RAM) 1203, such as performing the methods described in the above embodiments. Various programs and data required for system operation are also stored in RAM 1203. The CPU 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. An Input / Output (I / O) interface 1205 is also connected to bus 1204.

[0115] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0116] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by central processing unit (CPU) 1201, it performs various functions defined in the system of this application.

[0117] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0120] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a computer's processor, causes the computer to perform the battery capacity degradation rate estimation method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0121] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the battery capacity degradation rate estimation method provided in the various embodiments described above.

[0122] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for predicting battery capacity decay rate, characterized in that, The method for predicting battery capacity degradation rate includes: Get the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity decay rate at the previous moment; The heat generation at the current moment is obtained based on the current at the previous moment and the preset battery electrical model; the preset battery model is constructed based on the equivalent resistance and voltage source, and the heat generation at the current moment is calculated based on the equivalent resistance and the current at the previous moment. Input the heat generated at the current moment into the preset battery temperature model to obtain the battery temperature at the current moment. The battery temperature at the current moment includes the highest battery temperature and the lowest battery temperature at the current moment. The current battery capacity decay rate is obtained based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and the preset battery capacity decay model. The current current, the heat generated at the current time, the battery temperature at the current time, and the battery capacity decay rate at the current time are used as the new previous current, the new previous heat generated, the new previous battery temperature, and the new previous battery capacity decay rate. The current at the next time is obtained and used as the new current at the current time. The above steps are repeated multiple times to obtain the battery capacity decay rate at multiple times. A battery capacity decay rate curve is determined based on multiple battery capacity decay rates and time steps between multiple time points. The time steps are calculated based on a preset time step threshold to estimate the battery capacity decay rate. The battery capacity decay rate at the current moment is obtained based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and the preset battery capacity decay model, including: Based on the time derivative of the calendar capacity decay rate formula, and then discretized, the time derivative formula of the calendar capacity decay rate is obtained; based on the time derivative of the cycle capacity decay rate, and then discretized, the time derivative formula of the cycle capacity decay rate is obtained; based on the time derivative formulas of the calendar capacity decay rate and the decay rate, the preset battery capacity decay model is obtained. It also includes unifying the discharge, charging, and resting conditions into a single current expression based on a step function: wherein, , is the battery road end voltage determined by a pre-set battery electrical model; is the current determined by a pre-set battery electrical model and a pre-set battery temperature model, is the battery discharge power input; is the current at rest.

2. The battery capacity degradation rate estimation method according to claim 1, characterized by, Based on the current at the previous moment and the preset battery electrical model, the heat generation at the current moment includes: The preset battery electrical model is constructed based on the equivalent resistance and voltage source, such that the current magnitude of the preset battery electrical model is the current at the previous moment. The heat generated at the current moment is calculated based on the equivalent resistance and the current at the previous moment.

3. The battery capacity degradation rate estimation method according to claim 1, characterized by, Before inputting the current heat generation into the preset battery temperature model, the following steps are also included: If the highest battery temperature at the previous moment is greater than or equal to a preset high temperature threshold, then battery thermal management is activated, and the thermal management water temperature is the preset cooling temperature. If the lowest battery temperature at the previous moment is less than or equal to a preset low temperature threshold, then battery thermal management is activated, and the thermal management water temperature is the preset heating temperature. If the highest battery temperature at the previous moment is less than a preset high temperature threshold, and the lowest battery temperature at the previous moment is greater than a preset low temperature threshold, then battery thermal management is turned off.

4. The method for predicting battery capacity degradation rate according to claim 3, characterized in that, Input the current heat generation into the preset battery temperature model to obtain the current battery temperature, including: The current maximum battery temperature is calculated using the following formula: in, This refers to the highest battery temperature at the current moment. That was the highest battery temperature at the previous moment. This refers to the current battery heat generation. It is the heat capacity of the battery cell. This indicates the on / off state of the battery thermal management system; 0 indicates off, and 1 indicates on. It is the temperature of the heat management water; It is the ambient temperature. It is the time step mentioned. , , , These are the preset calibration coefficients.

5. The method for predicting battery capacity degradation rate according to claim 3, characterized in that, Input the current heat generation into the preset battery temperature model to obtain the current battery temperature, including: The current minimum battery temperature is calculated using the following formula: in, This refers to the lowest battery temperature at the current moment. That was the lowest battery temperature at the previous moment. This refers to the current battery heat generation. It is the heat capacity of the battery cell. This indicates the on / off state of the battery thermal management system; 0 indicates off, and 1 indicates on. It is the temperature of the heat management water; It is the ambient temperature. It is the time step mentioned. , , , These are the preset calibration coefficients.

6. The method for predicting battery capacity degradation rate according to claim 1, characterized in that, Before obtaining the previous current, current, heat generation, battery temperature, and battery capacity decay rate, the following steps are also included: The preset step size calculation threshold is received, which includes the maximum capacity change threshold, the maximum open circuit voltage change threshold, the maximum battery temperature change threshold, the maximum battery temperature change threshold, and the operating condition threshold. The time step is obtained by calculating the threshold based on the preset step size.

7. The method for predicting battery capacity degradation rate according to claim 1, characterized in that, Before inputting the current heat generation into the preset battery temperature model, the following steps are also included: Collect the highest and lowest ambient temperatures for each day of the target area over a year; The ambient temperature function of the target region over one year is obtained based on the highest and lowest ambient temperatures each day. The target ambient temperature is obtained based on the ambient temperature function, and the target ambient temperature is used as the ambient temperature coefficient of the preset battery temperature model.

8. The method for predicting battery capacity degradation rate according to claim 1, characterized in that, Before inputting the heat generated in the previous moment into the preset battery temperature model, the following steps are also included: Collect the highest and lowest ambient temperatures for each day of the target area over a year; Randomly sample between the highest and lowest ambient temperatures to obtain multiple sampled temperature values, and obtain the target ambient temperature based on the average of the sampled temperature values. The target ambient temperature is used as the ambient temperature coefficient of the preset battery temperature model.

9. A battery capacity degradation rate prediction device based on the battery capacity degradation rate prediction method according to any one of claims 1 to 8, characterized in that, The battery capacity degradation rate prediction device includes: The data acquisition module is used to acquire the current at the previous moment, the current at the current moment, the heat generated at the previous moment, the battery temperature at the previous moment, and the battery capacity decay rate at the previous moment. The battery electrical model module is used to obtain the heat generation at the current moment based on the current at the previous moment and the preset battery electrical model; the preset battery model is constructed based on the equivalent resistance and voltage source, and the heat generation at the current moment is calculated based on the equivalent resistance and the current at the previous moment. The battery temperature model module is used to input the heat generated at the current moment into a preset battery temperature model to obtain the battery temperature at the current moment. The battery temperature at the current moment includes the highest battery temperature and the lowest battery temperature at the current moment. The battery capacity decay rate calculation module is used to obtain the battery capacity decay rate at the current moment based on the battery capacity decay rate at the previous moment, the battery temperature at the previous moment, the current at the previous moment, and a preset battery capacity decay model. The iterative module takes the current at the current moment, the heat generated at the current moment, the battery temperature at the current moment, and the battery capacity decay rate at the current moment as the new previous moment current, the new previous moment heat generated, the new previous moment battery temperature, and the new previous moment battery capacity decay rate, obtains the current at the next moment and takes it as the new current at the current moment, and repeats the above steps multiple times to obtain the battery capacity decay rate at multiple moments. The battery capacity degradation rate prediction module is used to determine a battery capacity degradation rate curve based on multiple battery capacity degradation rates and time steps between multiple times. The time steps are obtained by calculating a threshold based on a preset step size in order to predict the battery capacity degradation rate.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the battery capacity degradation rate prediction method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by the computer's processor, causes the computer to perform the battery capacity degradation rate prediction method according to any one of claims 1 to 8.

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