Battery state parameter estimation method, device and equipment based on battery model

Abstract

This invention relates to the field of electronic technology and discloses a method, apparatus, and device for estimating battery state parameters based on a battery model. The method provided by this invention determines a target first correlation model corresponding to the battery temperature from multiple first correlation models corresponding to the battery temperature under different preset temperatures. The actual capacity of the battery is calculated using the number of cycles, calendar aging time, and the target first correlation model. The calculated battery capacity takes into account the capacity decay caused by aging and temperature during operation, making the calculated battery capacity more accurate. Consequently, the battery SOC calculated based on the battery capacity is also more accurate, solving the problem of inaccurate estimation results caused by estimating battery SOC based on a fixed battery capacity in related technologies.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, and more specifically to a method, apparatus, and device for estimating battery state parameters based on a battery model. Background Technology

[0002] Battery management is the core and key to battery energy storage technology. Among them, accurate and efficient estimation of battery operating state parameters is of great significance for realizing battery energy control management and safety protection.

[0003] Commonly estimated battery operating state parameters include State of Charge (SOC). SOC estimation methods based on battery models are widely used due to their good online application capabilities and high accuracy. In related technologies, the battery's SOC is typically calculated using the initial SOC, current, operating time, and preset battery model parameters, including the battery's actual capacity. However, these battery model parameters change continuously with battery aging, affecting the estimated SOC. Simultaneously, changes in the operating environment temperature also have a coupled effect on the battery's internal parameters, further reducing the accuracy of SOC estimation. Summary of the Invention

[0004] In view of this, the present invention provides a battery state parameter estimation method, apparatus and device based on a battery model to solve the problem that calculating the battery SOC by using preset battery parameters does not take into account the influence of temperature and aging on the battery parameters, resulting in low accuracy of battery SOC estimation.

[0005] In a first aspect, the present invention provides a battery state parameter estimation method based on a battery model, wherein the battery state parameters include battery SOC. The method includes: acquiring the battery temperature, cycle count, calendar aging time, current, initial SOC, and operating time of the battery to be estimated; determining a target first correlation model corresponding to the battery temperature in the first correlation models corresponding to different preset temperatures, wherein the target first correlation model is used to characterize the correlation between the battery capacity and cycle count and calendar aging time at the battery temperature; inputting the cycle count and calendar aging time of the battery to be estimated into the target first correlation model for calculation to obtain the battery capacity of the battery to be estimated; inputting the battery capacity, current, initial SOC, and operating time of the battery to be estimated into a pre-constructed battery SOC estimation model for calculation to obtain the estimated battery SOC value of the battery to be estimated, wherein the battery SOC estimation model is used to characterize the correlation between the battery SOC and the battery capacity, current, initial SOC, and operating time.

[0006] This invention provides a battery state parameter estimation method based on a battery model. The method determines a target first correlation model corresponding to the battery temperature from multiple first correlation models corresponding to different preset temperatures. This target first correlation model characterizes the relationship between battery capacity and cycle count and calendar aging time at the battery temperature. The cycle count and calendar aging time of the battery to be estimated are input into the target first correlation model for calculation to obtain the battery capacity. The current, initial SOC, and running time are input into a pre-constructed battery SOC estimation model for calculation to obtain the estimated battery SOC value. This invention provides a method that determines the target first correlation model corresponding to the battery temperature from multiple first correlation models corresponding to different preset temperatures. It uses the cycle count, calendar aging time, and target first correlation model to calculate the actual capacity of the battery to be estimated. The calculated battery capacity takes into account the capacity decay caused by aging and temperature during operation, making the calculated battery capacity more accurate. This, in turn, makes the battery SOC calculated based on the battery capacity more accurate, solving the problem of inaccurate estimation results caused by estimating battery SOC based on a fixed battery capacity in related technologies.

[0007] In an optional implementation, the battery state parameters further include the battery terminal voltage. The method further includes: determining a target second correlation model corresponding to the battery temperature from second correlation models corresponding to different preset temperatures, wherein the target second correlation model is used to characterize the correlation between the ohmic internal resistance of the battery at the battery temperature and the number of cycles and calendar aging time; inputting the number of cycles and calendar aging time of the battery to be estimated into the target second correlation model for calculation to obtain the ohmic internal resistance of the battery to be estimated; inputting the estimated battery SOC value into the third, fourth, and fifth correlation models corresponding to the battery temperature for calculation to obtain the open-circuit voltage of the battery to be estimated. The battery terminal voltage estimation model is used to calculate the relationship between the battery's state of charge (SOC), polarization resistance, polarization capacitance, current, and ohmic internal resistance. The model uses three correlation models to characterize the relationship between SOC and open-circuit voltage, polarization resistance, current, and ohmic internal resistance. The open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance of the battery to be estimated are input into a pre-built battery terminal voltage estimation model for calculation. The battery terminal voltage estimation model characterizes the relationship between the battery terminal voltage and the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance.

[0008] The method provided in this optional embodiment determines the target second correlation model corresponding to the battery temperature, inputs the number of cycles and calendar aging time into the target second correlation model, and solves for the ohmic internal resistance of the battery to be estimated. The solution of the ohmic internal resistance takes into account the influence of temperature, number of cycles and calendar aging time, making the solved ohmic internal resistance more accurate. Based on the battery SOC estimate and the third, fourth and fifth correlation models, the open circuit voltage, polarization capacitance and polarization resistance values ​​of the battery to be estimated are calculated. The obtained open circuit voltage, polarization capacitance and polarization resistance values ​​of the battery to be estimated take into account the influence of temperature, making the calculation results of the open circuit voltage, polarization capacitance and polarization resistance values ​​more accurate, and thus making the terminal voltage calculated by the open circuit voltage, polarization capacitance, polarization resistance and ohmic internal resistance more accurate.

[0009] In one optional implementation, the first and second correlation models corresponding to different preset temperatures are constructed through the following steps: obtaining the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature, and the battery capacity and ohmic internal resistance corresponding to different cycle numbers of the battery under test; performing temperature correction processing on the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at each preset temperature; performing temperature correction processing on the battery capacity corresponding to different cycle numbers of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to different cycle numbers of the battery under test at each preset temperature; constructing the first correlation model corresponding to different temperatures based on the battery capacity corresponding to different cycle numbers and different calendar aging times of the battery under test at each preset temperature; and constructing the second correlation model corresponding to different temperatures based on the ohmic internal resistance corresponding to different cycle numbers and different calendar aging times of the battery under test at each preset temperature.

[0010] In one optional implementation, the third, fourth, and fifth correlation models are constructed through the following steps: acquiring full discharge process test data of the battery under test at different temperatures, including the open-circuit voltage, current, and terminal voltage of the battery under test at different SOCs during discharge; inputting the open-circuit voltage, current, and terminal voltage of the battery under test at different temperatures during discharge into a pre-constructed target model for calculation to obtain the polarization resistance and polarization capacitance of the battery under test at different SOCs during discharge at the corresponding temperatures; constructing the third correlation model corresponding to each temperature based on the open-circuit voltage of the battery under test at different temperatures during discharge; constructing the fourth correlation model corresponding to each temperature based on the polarization resistance of the battery under test at different temperatures during discharge; and constructing the fifth correlation model corresponding to each temperature based on the polarization capacitance of the battery under test at different temperatures during discharge.

[0011] In an optional implementation, the method further includes: acquiring the target battery capacity, initial SOC, preset current, and a first curve of the battery under test at a target temperature, wherein the first curve is used to characterize the change curve of the battery SOC over time during actual operation of the battery under test, and the target battery capacity is calculated by inputting the target number of cycles and the target calendar aging time into a first correlation model at the target temperature; inputting the target battery capacity, preset current, and initial SOC of the battery under test into a pre-constructed battery SOC estimation model to obtain a third curve, wherein the third curve is used to characterize the change curve of the fitted battery SOC over time of the battery under test; and evaluating the fitting effect of the first correlation model based on the similarity between the first curve and the second curve.

[0012] In an optional implementation, the method further includes: acquiring the target ohmic internal resistance of the battery under test at a target temperature, the change of the target battery's SOC over time, and a third curve. The third curve is used to characterize the change of the terminal voltage of the battery under test over time during actual operation. The target ohmic internal resistance is calculated by inputting the target number of cycles and the target calendar aging time into a second correlation model at the target temperature; inputting the change of the target battery's SOC over time into a third correlation model, a fourth correlation model, and a fifth correlation model respectively to calculate the change of open-circuit voltage over time, the change of polarization resistance over time, and the change of polarization capacitance over time; inputting the target ohmic internal resistance, the change of open-circuit voltage over time, the change of polarization resistance over time, the change of polarization capacitance over time, and a preset current into a battery terminal voltage estimation model to fit a fourth curve. The fourth curve is used to characterize the change of the battery terminal voltage over time obtained by fitting the battery under test; and evaluating the fitting effect of the second correlation model, the third correlation model, the fourth correlation model, and the fifth correlation model based on the similarity between the third curve and the fourth curve.

[0013] Secondly, the present invention provides a battery state parameter estimation device based on a battery model. The device includes: an acquisition module for acquiring the battery temperature, cycle count, calendar aging time, current, initial SOC, and operating time of the battery to be estimated; a first determination module for determining a target first association model corresponding to the battery temperature from first association models corresponding to different preset temperatures, wherein the target first association model is used to characterize the relationship between the battery capacity and cycle count and calendar aging time at the battery temperature; a first calculation module for inputting the cycle count and calendar aging time of the battery to be estimated into the target first association model for calculation to obtain the battery capacity of the battery to be estimated; and a second calculation module for inputting the battery capacity, current, initial SOC, and operating time of the battery to be estimated into a pre-constructed battery SOC estimation model for calculation to obtain an estimated battery SOC value of the battery to be estimated, wherein the battery SOC estimation model is used to characterize the relationship between the battery SOC and the battery capacity, current, initial SOC, and operating time.

[0014] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the battery state parameter estimation method based on the battery model described in the first aspect or any corresponding embodiment thereof.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the battery state parameter estimation method based on a battery model according to the first aspect or any corresponding embodiment described above.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the battery state parameter estimation method based on a battery model as described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a battery state parameter estimation method based on a battery model according to an embodiment of the present invention.

[0019] Figure 2 This is a flowchart illustrating another battery state parameter estimation method based on a battery model according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the battery terminal voltage estimation model in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the HPPC testing steps in an embodiment of the present invention;

[0022] Figure 5 This is a structural block diagram of a battery state parameter estimation device based on a battery model according to an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In related technologies, the battery's SOC is generally calculated using its initial SOC, operating time, and preset model parameters, including the battery's actual capacity. However, these model parameters change continuously as the battery ages, affecting the estimated SOC. Simultaneously, changes in the operating environment temperature also have a coupled effect on the battery's internal parameters, further reducing the accuracy of the SOC estimation.

[0026] In view of this, the battery state parameter estimation method based on a battery model provided in this application embodiment can be applied to a server to estimate battery state parameters. The method provided by this invention determines a target first association model corresponding to the battery temperature from multiple first association models corresponding to the battery temperature of the battery to be estimated at different preset temperatures. It then calculates the actual capacity of the battery to be estimated using the number of cycles, calendar aging time, and the target first association model. The calculated battery capacity takes into account the capacity decay caused by aging and temperature during operation, making the calculated battery capacity more accurate. This, in turn, makes the battery SOC calculated based on the battery capacity more accurate, solving the problem of inaccurate estimation results caused by estimating battery SOC based on a fixed battery capacity in related technologies.

[0027] According to an embodiment of the present invention, a method for estimating battery state parameters based on a battery model is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a battery state parameter estimation method based on a battery model, which can be used in the aforementioned server. The battery state parameters include battery SOC. Figure 1 This is a flowchart of a battery state parameter estimation method based on a battery model according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps:

[0029] Step S101: Obtain the battery temperature, cycle count, calendar aging time, current, initial SOC, and running time of the battery to be estimated.

[0030] For example, the battery to be estimated can be a battery for which battery state parameters need to be estimated. In this embodiment, the battery to be estimated can include, but is not limited to, lithium iron phosphate cells, with the upper and lower voltage limits of the cell during charging and discharging ranging from 2.5 to 3.65V. Battery temperature refers to the temperature at which the battery operates, and cycle number refers to the number of complete charge and discharge cycles of the battery. Calendar aging refers to the phenomenon that the capacity of a battery gradually decreases with increasing storage time when it has not undergone charge and discharge cycles; calendar aging time refers to the storage time of the battery. Running time refers to the duration of continuous charging or discharging of the battery.

[0031] Step S102: Determine the target first correlation model corresponding to the battery temperature in the first correlation models corresponding to different preset temperatures. The target first correlation model is used to characterize the correlation between the battery capacity and the number of cycles and calendar aging time of the battery at the battery temperature.

[0032] For example, in the embodiments of this application, different preset temperatures correspond to different first correlation models. The first correlation model is used to characterize the correlation between battery capacity and cycle number and calendar aging time at a fixed preset temperature.

[0033] Step S103: Input the number of cycles and calendar aging time of the battery to be estimated into the target first association model for calculation to obtain the battery capacity of the battery to be estimated.

[0034] For example, the corresponding target first association model can be determined by the battery temperature. The cycle number and calendar aging time of the battery to be estimated are input into the target first association model, and the model is solved to obtain the battery capacity of the battery to be estimated. In this embodiment of the application, the target first association model can be represented by the following formula (1):

[0035]

[0036] Where Q(T) is the battery capacity at temperature T, and C rate E represents the battery charge / discharge rate. a R is the activation energy of the reaction, T is the molar gas constant, N is the cell temperature, and t is the number of cycles. p denoted as calendar aging time, and a0, a1, and a2 as fitting parameters.

[0037] Step S104: Input the battery capacity, current, initial SOC and running time of the battery to be estimated into the pre-built battery SOC estimation model for calculation to obtain the battery SOC estimate value of the battery to be estimated. The battery SOC estimation model is used to characterize the relationship between battery SOC and battery capacity, current, initial SOC and running time.

[0038] For example, in this embodiment of the application, the pre-built battery SOC estimation model can be represented by the following equation (2):

[0039]

[0040] Where SOC0 is the initial SOC value of the battery; η is the coulombic efficiency of the battery; Q(T) is the battery capacity at temperature T, t represents the operating time, i(t) represents the current, and SOC represents the estimated SOC value of the battery to be estimated.

[0041] The battery state parameter estimation method based on a battery model provided in this embodiment determines the target first association model corresponding to the battery temperature based on the battery temperature of the battery to be estimated in multiple first association models corresponding to different preset temperatures. The actual capacity of the battery to be estimated is calculated using the number of cycles, calendar aging time, and target first association model of the battery to be estimated. The calculated battery capacity takes into account the capacity decay caused by aging and temperature during operation, making the battery capacity calculation result more accurate. In turn, the battery SOC calculated based on the battery capacity is also more accurate, solving the problem of inaccurate estimation results caused by estimating battery SOC based on a fixed battery capacity in related technologies.

[0042] This embodiment provides a battery state parameter estimation method based on a battery model, which can be used in the aforementioned server. The battery state parameters also include battery terminal voltage. Figure 2 This is a flowchart of a battery state parameter estimation method based on a battery model according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0043] Step S201: Obtain the battery temperature, cycle count, calendar aging time, current, initial SOC, and runtime of the battery to be estimated. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0044] Step S202: Determine the target first correlation model corresponding to the battery temperature from the first correlation models corresponding to different preset temperatures. The target first correlation model is used to characterize the correlation between battery capacity and cycle count and calendar aging time at different battery temperatures. For details, please refer to... Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0045] Step S203: Input the cycle count and calendar aging time of the battery to be estimated into the target first correlation model for calculation to obtain the battery capacity to be estimated. For details, please refer to... Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0046] Step S204: Input the battery capacity, current, initial SOC, and operating time of the battery to be estimated into the pre-built battery SOC estimation model for calculation to obtain the estimated battery SOC value. The battery SOC estimation model is used to characterize the correlation between battery SOC and battery capacity, current, initial SOC, and operating time. For details, please refer to... Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0047] Step S205: Determine the target second correlation model corresponding to the battery temperature in the second correlation models corresponding to different preset temperatures. The target second correlation model is used to characterize the correlation between the ohmic internal resistance of the battery under test and the number of cycles and calendar aging time at the corresponding battery temperature.

[0048] For example, in this embodiment of the application, the second association model can be determined by the following formula (3):

[0049] R0(T) = b0 + b1·N + b2·t p 12 (3)

[0050] Where b0 is the correction coefficient, reflecting the influence of randomness; b1·N represents the increase in internal resistance caused by cyclic aging; and b2·t p 1 / 2 The value represents the increase in internal resistance due to calendar aging; b0, b1, and b2 are fitting coefficients based on experimental data; R0(T) represents the ohmic internal resistance of the battery at temperature T. The fitting coefficient values ​​of the second correlation model are different at different temperatures.

[0051] Step S206: Input the number of cycles and calendar aging time of the battery to be estimated into the target second correlation model for calculation to obtain the ohmic internal resistance of the battery to be estimated.

[0052] For example, the ohmic internal resistance of the battery to be estimated is calculated based on the target second correlation model corresponding to the battery temperature.

[0053] Step S207: Input the estimated SOC value of the battery into the third, fourth and fifth correlation models corresponding to the battery temperature for calculation to obtain the open circuit voltage, polarization resistance and polarization capacitance of the battery to be estimated. The third correlation model is used to characterize the correlation between battery SOC and open circuit voltage, the fourth correlation model is used to characterize the correlation between battery SOC and polarization resistance, and the fifth correlation model is used to characterize the correlation between battery SOC and polarization capacitance.

[0054] For example, in the embodiments of this application, the third correlation model, the fourth correlation model and the fifth correlation model corresponding to different temperatures are different. The third correlation model can be represented by the following formula (4), the fourth correlation model can be represented by the following formula (5), and the fifth correlation model can be represented by the following formula (6).

[0055]

[0056]

[0057]

[0058] Where OCV(T) is the open-circuit voltage of the battery at temperature T, and R p (T) represents the polarization resistance of the battery at temperature T, C p (T) represents the polarization capacitance of the battery at temperature T, SOC represents the estimated SOC value of the battery, and t0~t8, m0~m8, and n0~n8 are polynomial coefficients obtained through data fitting.

[0059] Step S208: Input the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance of the battery to be estimated into the pre-built battery terminal voltage estimation model for calculation to obtain the estimated terminal voltage of the battery. The battery terminal voltage estimation model is used to characterize the relationship between the battery terminal voltage and the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance.

[0060] For example, in this embodiment of the application, a temperature compensation module is added to the first-order RC equivalent circuit model to obtain a battery terminal voltage estimation model, thereby improving the temperature sensitivity of the model. The structure of the battery terminal voltage estimation model is as follows: Figure 3 As shown, the battery terminal voltage estimation model can be expressed by the following equations (7), (8) and (9):

[0061] U d =OCV+U Δ -R0·iU p (7)

[0062]

[0063] U Δ =n(T)·R 0,Δ ·i(9)

[0064] Among them, U d U is the battery terminal voltage; Δ This is the temperature compensation voltage, used to correct voltage errors caused by temperature changes; OCV is the battery open-circuit voltage; R0, R... p C pThese are the ohmic internal resistance, polarization resistance, and polarization capacitance, respectively; U p R is the voltage across polarized capacitor C1; i is the current; SOC0 is the initial SOC value of the battery; n(T) is a temperature-dependent function representing the temperature difference; R 0,Δ To compensate for the internal resistance of the Ohm.

[0065] In some optional implementations, the first correlation model and the second correlation model corresponding to different preset temperatures are constructed through the following steps:

[0066] Step a1: Obtain the battery capacity and internal resistance of the battery under test at different calendar aging times under the target temperature, and the battery capacity and internal resistance of the battery under test at different cycle numbers.

[0067] For example, the target temperature may include, but is not limited to, 25°C. In this embodiment, as the battery is charged and discharged, its capacity decreases, therefore the battery capacity Q is related to battery aging. As the battery is charged and discharged, its internal resistance increases. However, during a specific charge and discharge process, the R0 value at different SOCs is not significantly different. Therefore, R0 is considered to be only related to battery aging and not to the battery's SOC during a specific charge and discharge process. In summary, in this invention, the values ​​of Q and R0 change with battery aging, but the change of R0 with SOC during a specific charge and discharge process is ignored.

[0068] Specifically, the battery under test in its initial use period is subjected to a standard capacity calibration test three times at preset temperatures. This test procedure includes: resting – constant current / constant voltage charging – resting – constant current discharging. The average capacity Q of the three discharge tests is taken as the initial battery capacity. The battery under test in its initial use period is charged to 100% SOC at 25°C and subjected to cyclic aging at a 1C rate. After a certain cycle interval ΔN, the battery capacity calibration test is repeated to obtain the actual capacity Q and ohmic internal resistance R0 at different cycle numbers, until the battery capacity decays to 80% of its maximum capacity. In this embodiment, ΔN = 20. When determining the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature, the battery in its initial use is left to stand at 25°C for 7 days. During this time, the battery capacity will decrease (due to the self-discharge effect, which is not discussed in this invention). The battery is charged to 100% SOC with a current of 0.1C, and R0 is measured at this time. The battery is then fully discharged at a rate of 1C to obtain the battery capacity Q on the 7th day. This process is repeated every 7 days until the capacity decays to 80% of the maximum capacity. At this point, the battery is considered no longer suitable for continued use. This yields the calculated battery capacity and ohmic internal resistance under different calendar aging times.

[0069] Step a2: Perform temperature correction processing on the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature, and obtain the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at each preset temperature.

[0070] Step a3: Perform temperature correction processing on the battery capacity corresponding to different cycle numbers of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to different cycle numbers at each preset temperature.

[0071] For example, in this application embodiment, the battery capacity corresponding to different calendar aging times and the battery capacity corresponding to different cycle numbers are corrected for temperature using the following formula (10), and the ohmic internal resistance corresponding to different calendar aging times and the ohmic internal resistance corresponding to different cycle numbers are corrected for temperature using the following formula (11).

[0072]

[0073]

[0074] Where Q(T) and R0(T) are the temperature-corrected battery capacity and ohmic internal resistance, respectively; Q ref R 0,ref These represent the original battery capacity and ohmic internal resistance, respectively, T. ref In this embodiment, T is the aging test temperature. ref =25℃; T n To correct for temperature, E x The activation energy of the battery can be obtained from the battery manual; R is the molar gas constant, R = 8.314 J / (mol·K).

[0075] In this embodiment of the application, the temperature used for testing is 25℃ (i.e., T). ref =25℃), the measured values ​​of Q and R0 are at 25℃. Based on equations (10) and (11), substituting T=45℃, we can obtain the values ​​of Q and R0 at 45℃; substituting T=10℃, we can obtain the values ​​of Q and R0 at 10℃, and so on. The purpose of this correction formula is: by measuring only Q and R0 at 25℃, we can obtain the values ​​of Q and R0 at any temperature using the corrections of equations (10) and (11). Since the temperature changes in real time during actual application, Q and R0 also need to change in real time during simulation. If only Q and R0 at a single temperature are used, the fitting accuracy will decrease.

[0076] Step a4: Based on the battery capacity corresponding to different cycle numbers of the battery under test at each preset temperature and the battery capacity corresponding to different calendar aging times of the battery under test, construct the first correlation model corresponding to each temperature.

[0077] For example, the first correlation model is used to characterize the relationship between the battery capacity and the number of cycles and calendar aging time. The first correlation model can be specifically represented by the above formula (1), which will not be elaborated here.

[0078] Step a5: Construct a second correlation model corresponding to different temperatures based on the ohmic internal resistance of the battery under test at different cycle numbers and the ohmic internal resistance of the battery under test at different calendar aging times.

[0079] For example, the second correlation model is used to characterize the relationship between the battery's ohmic internal resistance and the number of cycles and calendar aging time. In the embodiments of this application, the second correlation model can be specifically represented by the above equation (3), which will not be repeated here.

[0080] In some optional implementations, the third, fourth, and fifth association models are constructed through the following steps:

[0081] Step b1: Obtain the full discharge process test data of the battery under test at different temperatures. The full discharge process test data includes the open circuit voltage, current and terminal voltage of the battery under test at different SOCs during the discharge process.

[0082] For example, in this embodiment of the application, the test data of the full discharge process of the battery under test at different temperatures are obtained by performing a Hybrid Pulse Power Characteristic (HPPC) test on the battery under test. Specifically, firstly, the battery in its initial use is fully charged to 100% SOC at preset temperatures, and the battery is subjected to a Hybrid Pulse Power Characteristic (HPPC) test. The test steps include: 1C current pulse discharge - rest. The battery SOC is adjusted to 90%, and the HPPC test is repeated until the discharge ends (i.e., 0% SOC is reached). Finally, the test curve of the full discharge process [0% SOC, 100% SOC] is obtained, with the SOC point interval being 10%. The HPPC test steps are as follows: Figure 4 As shown.

[0083] Step b2 involves inputting the open-circuit voltage, current, and terminal voltage corresponding to different SOCs of the battery under test during discharge at various temperatures into a pre-built target model for calculation, thereby obtaining the polarization resistance and polarization capacitance corresponding to different SOCs of the battery under test during discharge at the corresponding temperatures.

[0084] For example, in this embodiment of the application, the target model can be represented by the following equation (12):

[0085]

[0086] Among them, U d Let R be the battery terminal voltage at time t, OCV be the open-circuit voltage of the battery at that SOC, and R be the open-circuit voltage. p For polarization resistance, C p For polarization capacitor, Δt is the voltage (current) sampling period.

[0087] In this embodiment of the application, the open-circuit voltage can also be expressed by the following formula (13):

[0088] OCV=f(SOC,T) (13)

[0089] Where f represents the functional relationship between OCV, SOC, and T.

[0090] Step b3: Construct a third correlation model for each temperature based on the open-circuit voltage corresponding to different SOCs of the battery under test during discharge at each temperature.

[0091] For example, in the embodiments of this application, the third association model is referred to in (4) above, and will not be repeated here.

[0092] Step b4: Based on the polarization resistance corresponding to different SOCs of the battery under test during discharge at different temperatures, construct the fourth correlation model corresponding to each temperature.

[0093] For example, in the embodiments of this application, the third association model is referred to in (5) above, and will not be repeated here.

[0094] Step b5: Construct the fifth correlation model corresponding to each temperature based on the polarization capacitance corresponding to different SOCs of the battery under test during discharge at different temperatures.

[0095] For example, in the embodiments of this application, the third correlation model is as described above (6), and will not be repeated here. It is worth noting that the battery open-circuit voltage OCV represents the difference between the positive electrode potential and the negative electrode potential of the battery when the circuit is open, and this value is not related to battery cycle aging; R p C p It is mainly affected by the ion concentration difference within the battery, and the concentration decrease at various points inside the battery occurs proportionally, therefore R p C p The values ​​are also less related to battery cycle aging. In summary, in this invention, OCV and R... p C p Changes due to battery aging are reasonably ignored.

[0096] In some alternative implementations, the method further includes:

[0097] Step c1: Obtain the target battery capacity, initial SOC, preset current, and first curve of the battery under test at the target temperature.

[0098] For example, the preset current can be a pre-set current value. This application does not limit the magnitude of the preset current, and those skilled in the art can determine it according to their needs. The target temperature may include, but is not limited to, 25°C. In the embodiments of this application, a first curve is obtained by ampere-hour calculation. The first curve is used to characterize the change curve of the battery's SOC over time during the actual operation of the battery under test. The target battery capacity is calculated by inputting the target cycle number and the target calendar aging time into a first correlation model at the target temperature.

[0099] Step c2 involves inputting the target battery capacity, preset current, and initial SOC of the battery under test into a pre-built battery SOC estimation model, and fitting the model to obtain the third curve.

[0100] For example, the third curve is used to characterize the change of battery SOC over time obtained by fitting the battery under test.

[0101] Step c3: Evaluate the fitting effect of the first association model based on the similarity between the first curve and the second curve.

[0102] For example, in this embodiment of the application, the SOC-time curve (second curve) is compared with the actual SOC-time curve of the battery (first curve) to measure the goodness of model fitting. The better the two match, the better the model fitting effect.

[0103] In some alternative implementations, the method further includes:

[0104] Step d1: Obtain the target ohmic internal resistance of the battery under test at the target temperature, the change of the target battery SOC over time, and the third curve.

[0105] For example, the third curve is used to characterize the change of terminal voltage over time during the actual operation of the battery under test. The target ohmic internal resistance is calculated by inputting the target number of cycles and the target calendar aging time into the second correlation model at the target temperature.

[0106] Step d2 involves inputting the target battery's SOC change information over time into the third, fourth, and fifth correlation models for calculation, thereby obtaining the open-circuit voltage change information over time, the polarization resistance change information over time, and the polarization capacitance change information over time.

[0107] For example, the target battery's SOC change over time is input into the third correlation model for calculation to obtain the open-circuit voltage change over time; the target battery's SOC change over time is input into the fourth correlation model for calculation to obtain the polarization resistance change over time; and the target battery's SOC change over time is input into the fifth correlation model for calculation to obtain the polarization capacitance change over time.

[0108] Step d3 involves fitting the target ohmic internal resistance, open-circuit voltage variation over time, polarization resistance variation over time, polarization capacitance variation over time, and the preset current input battery terminal voltage estimation model to obtain the fourth curve.

[0109] For example, the fourth curve is used to characterize the change of the battery terminal voltage over time obtained by fitting the battery under test.

[0110] Step d4: Evaluate the fitting performance of the second, third, fourth, and fifth correlation models based on the similarity between the third and fourth curves.

[0111] For example, via U d -Time curve (fourth curve) and actual battery U d - Compare the time curves (third curve) to measure the goodness of model fit; the closer the two curves match, the better the model fit.

[0112] In some optional implementations, step S204 above further includes:

[0113] The battery SOC estimation model is corrected using a preset filtering algorithm to obtain the corrected model;

[0114] The estimated SOC of the battery to be estimated is calculated based on the battery capacity, current, initial SOC, running time, and the corrected model.

[0115] For example, the preset filtering algorithm may include, but is not limited to, the Adaptive Extended Kalman Filter (AEKF) algorithm, the Unscented Kalman Filter algorithm, the Particle Filter algorithm, and machine learning algorithms. In this embodiment, the AEKF algorithm is used as an example, utilizing the R0 and R2 values ​​obtained from parameter identification. p C p The expressions for each parameter and SOC are fitted. Estimation is performed through the following steps:

[0116] (1) The dynamic equation of the nonlinear system is expressed as:

[0117]

[0118] Among them, X k and Y k Let u represent the system state variable and measurement variable at time k, respectively; k w represents the input variables of the system at time k; k and v k Representing process noise and measurement noise respectively, both are uncorrelated zero-mean Gaussian white noise; A k It is the state transition matrix, B k It is the input matrix, C k It is the output matrix, D k It is a feedforward matrix.

[0119] (2) Solving for the state coefficients:

[0120]

[0121] Among them, R p C p R0, OCV (SOC) k ) are the polarization resistance, polarization capacitance, ohmic internal resistance, and the open-circuit voltage corresponding to SOC at time k, respectively.

[0122] (3) Solve for the Jacobian matrix:

[0123]

[0124] (4) State prediction and covariance prediction:

[0125]

[0126] Among them, P k Let be the state covariance matrix at time k.

[0127] (5) Calculate the Kalman gain matrix:

[0128] K k =X k-1 ·P k-1 (18)

[0129] Among them, K k This is the Kalman gain matrix.

[0130] 6) Calculate state update and covariance update:

[0131]

[0132] in, P k+1k+1 These are updates to the system state variables and the covariance matrix, respectively. f(X) k+1 )and These are the true value and the estimated value of the system's state function, respectively.

[0133] 7) Update the observed values ​​of the state variables using extended Kalman filtering and adaptive extended Kalman filtering respectively:

[0134]

[0135] The AEKF algorithm weights the error covariance during state estimation. Through the above calculations, the AEKF algorithm adjusts the state estimation based on the changes in measurement data at each step, by weighting A... k B k C k D k Four matrix iterations and updates, along with estimation and correction of process and measurement noise, reduce the error between the estimated and true state function values, thereby improving the accuracy of SOC estimation.

[0136] The following is a detailed description of a battery state parameter estimation method based on a battery model provided in this application, through a specific implementation method.

[0137] Example:

[0138] S1: This embodiment uses a first-order temperature compensation model for illustration. This invention is also applicable to higher-order temperature compensation models. In this case, the SOC calculation formulas (7) and (8) need to be adjusted. This invention only provides an embodiment for lithium iron phosphate batteries. In fact, this invention is universal for all types of batteries. In this embodiment, "preset different temperatures" refers to 0℃, 25℃ and 45℃. The temperature range can be expanded or narrowed and the sampling temperature point can be selected according to the actual application conditions of the battery.

[0139] The S2.1 battery capacity calibration test is in accordance with national standards and cannot be modified.

[0140] The pulse discharge current, pulse discharge time, and resting time in the S2.2 HPPC test can be determined according to the battery type and test temperature, and are not limited to the parameter values ​​provided in the example. In this example, the battery parameters are identified using the recursive least squares method. In practical applications, various algorithms such as recursive least squares, neural network algorithms, and particle swarm optimization algorithms can be used to achieve the same result.

[0141] S2.3 In this embodiment, taking into account both computational complexity and accuracy, an eighth-order polynomial fitting is used to obtain OCV(T)-SOC and R. p (T)-SOC, C p (T)-SOC can be adjusted according to the situation in practical applications.

[0142] The test current, sampling period, and decay threshold of the S3.1 cyclic aging test can be modified according to the actual situation.

[0143] The test current, sampling period, and decay threshold of the S3.2 calendar aging test can be modified according to the actual situation.

[0144] S4 This embodiment uses the Adaptive Extended Kalman Filter (AEKF) algorithm for SOC estimation. In practical applications, various algorithms such as the Unscented Kalman Filter algorithm, Particle Filter algorithm, and Machine Learning algorithm can be used to implement it.

[0145] The S7 battery life termination condition can be set according to actual needs. Under normal circumstances, the termination condition is set to: the battery capacity decays to 80% of the maximum capacity.

[0146] This embodiment also provides a battery state parameter estimation device based on a battery model, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0147] This embodiment provides a battery state parameter estimation device based on a battery model, such as... Figure 5 As shown, it includes:

[0148] The acquisition module 501 is used to acquire the battery temperature, cycle number, calendar aging time, current, initial SOC and running time of the battery to be estimated;

[0149] The first determining module 502 is used to determine the target first association model corresponding to the battery temperature in the first association models corresponding to different preset temperatures. The target first association model is used to characterize the relationship between the battery capacity and the number of cycles and calendar aging time of the battery at the battery temperature.

[0150] The first calculation module 503 is used to input the number of cycles and calendar aging time of the battery to be estimated into the target first association model for calculation to obtain the battery capacity of the battery to be estimated.

[0151] The second calculation module 504 is used to input the battery capacity, current, initial SOC and running time of the battery to be estimated into a pre-built battery SOC estimation model for calculation, so as to obtain the battery SOC estimate of the battery to be estimated. The battery SOC estimation model is used to characterize the relationship between battery SOC and battery capacity, current, initial SOC and running time.

[0152] In some optional embodiments, the battery state parameter also includes the battery terminal voltage, and the device further includes:

[0153] The second determination module is used to determine the target second correlation model corresponding to the battery temperature in the second correlation models corresponding to different preset temperatures. The target second correlation model is used to characterize the correlation between the ohmic internal resistance of the battery under test and the number of cycles and calendar aging time at the corresponding battery temperature.

[0154] The third calculation module is used to input the cycle number and calendar aging time of the battery to be estimated into the target second correlation model for calculation to obtain the ohmic internal resistance of the battery to be estimated.

[0155] The fourth calculation module is used to input the estimated battery SOC value into the third, fourth and fifth correlation models corresponding to the battery temperature for calculation, so as to obtain the open circuit voltage, polarization resistance and polarization capacitance of the battery to be estimated. The third correlation model is used to characterize the correlation between battery SOC and open circuit voltage, the fourth correlation model is used to characterize the correlation between battery SOC and polarization resistance, and the fifth correlation model is used to characterize the correlation between battery SOC and polarization capacitance.

[0156] The fifth calculation module is used to input the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance of the battery to be estimated into the pre-built battery terminal voltage estimation model for calculation, so as to obtain the estimated value of the battery terminal voltage. The battery terminal voltage estimation model is used to characterize the relationship between the battery terminal voltage and the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance.

[0157] In some optional implementations, the first correlation model and the second correlation model corresponding to different preset temperatures are constructed through the following steps:

[0158] Obtain the battery capacity and internal resistance of the battery under test at different calendar aging times under the target temperature, as well as the battery capacity and internal resistance of the battery under test at different cycle numbers.

[0159] Temperature correction processing is performed on the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to different calendar aging times at each preset temperature.

[0160] Temperature correction processing is performed on the battery capacity corresponding to different cycle numbers of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to different cycle numbers at each preset temperature.

[0161] Based on the battery capacity corresponding to different cycle numbers and different calendar aging times of the battery under test at each preset temperature, a first correlation model is constructed for each temperature.

[0162] Based on the ohmic internal resistance corresponding to different cycles of the battery under test at each preset temperature and the ohmic internal resistance corresponding to different calendar aging times of the battery under test, a second correlation model is constructed for each temperature.

[0163] In some optional implementations, the third, fourth, and fifth association models are constructed through the following steps:

[0164] Acquire the full discharge process test data of the battery under test at different temperatures. The full discharge process test data includes the open circuit voltage, current and terminal voltage of the battery under test at different SOCs during the discharge process.

[0165] The open-circuit voltage, current and terminal voltage of the battery under test at different temperatures during discharge are input into the pre-built target model for calculation, so as to obtain the polarization resistance and polarization capacitance of the battery under test at different temperatures during discharge.

[0166] Based on the open-circuit voltage corresponding to different SOCs of the battery under test during discharge at different temperatures, a third correlation model is constructed for each temperature.

[0167] Based on the polarization resistance corresponding to different SOCs of the battery under test during discharge at different temperatures, a fourth correlation model is constructed for each temperature.

[0168] Based on the polarization capacitance corresponding to different SOCs of the battery under test during discharge at different temperatures, a fifth correlation model is constructed for each temperature.

[0169] In some alternative embodiments, the above-described apparatus further includes:

[0170] The second acquisition module is used to acquire the target battery capacity, initial SOC, preset current and first curve of the battery under test at the target temperature. The first curve is used to characterize the change curve of battery SOC with time during the actual operation of the battery under test. The target battery capacity is calculated by inputting the target cycle number and target calendar aging time into the first correlation model at the target temperature.

[0171] The first fitting module is used to input the target battery capacity, preset current and initial SOC of the battery under test into the pre-built battery SOC estimation model and fit it to obtain the third curve. The third curve is used to characterize the change curve of battery SOC over time obtained by fitting the battery under test.

[0172] The first evaluation module is used to evaluate the fitting effect of the first association model based on the similarity between the first curve and the second curve.

[0173] In some alternative embodiments, the above-described apparatus further includes:

[0174] The third acquisition module is used to acquire the target ohmic internal resistance of the battery under test at the target temperature, the change of the target battery SOC over time, and the third curve. The third curve is used to characterize the change of the terminal voltage over time during the actual operation of the battery under test. The target ohmic internal resistance is calculated by inputting the target cycle number and the target calendar aging time into the second correlation model at the target temperature.

[0175] The sixth calculation module is used to input the target battery SOC change information over time into the third correlation model, the fourth correlation model, and the fifth correlation model for calculation, and obtain the open circuit voltage change information over time, the polarization resistance change information over time, and the polarization capacitance change information over time, respectively.

[0176] The second fitting module is used to fit the target ohmic internal resistance, open circuit voltage change over time, polarization resistance change over time, polarization capacitance change over time, and preset current into the battery terminal voltage estimation model to obtain the fourth curve. The fourth curve is used to characterize the battery terminal voltage change over time obtained by fitting the battery under test.

[0177] The second evaluation module is used to evaluate the fitting effect of the second, third, fourth and fifth association models based on the similarity between the third and fourth curves.

[0178] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0179] In this embodiment, the battery state parameter estimation device based on the battery model is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0180] This invention also provides a computer device having the above-described features. Figure 5 The device shown is a battery state parameter estimation device based on a battery model.

[0181] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0182] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0183] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0184] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0185] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0186] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0187] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0188] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0189] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for estimating battery state parameters based on a battery model, characterized in that, The battery state parameters include battery SOC, and the method includes: Obtain the battery temperature, cycle count, calendar aging time, current, initial SOC, and runtime of the battery to be estimated; A target first correlation model corresponding to the battery temperature is determined in the first correlation model corresponding to different preset temperatures. The target first correlation model is used to characterize the correlation between the battery capacity and the number of cycles and calendar aging time of the battery at the battery temperature. The cycle number and calendar aging time of the battery to be estimated are input into the target first association model for calculation to obtain the battery capacity of the battery to be estimated. The battery capacity, current, initial SOC, and operating time of the battery to be estimated are input into a pre-built battery SOC estimation model for calculation to obtain the estimated battery SOC value of the battery to be estimated. The battery SOC estimation model is used to characterize the correlation between battery SOC and battery capacity, current, initial SOC, and operating time.

2. The method according to claim 1, characterized in that, The battery state parameters also include the battery terminal voltage, and the method further includes: In the second correlation models corresponding to different preset temperatures, a target second correlation model corresponding to the battery temperature is determined. The target second correlation model is used to characterize the correlation between the battery's ohmic internal resistance and the number of cycles and calendar aging time at the battery temperature. The cycle number and calendar aging time of the battery to be estimated are input into the target second correlation model for calculation to obtain the ohmic internal resistance of the battery to be estimated. The estimated SOC of the battery is input into the third, fourth, and fifth correlation models corresponding to the battery temperature for calculation to obtain the open-circuit voltage, polarization resistance, and polarization capacitance of the battery to be estimated. The third correlation model is used to characterize the correlation between battery SOC and open-circuit voltage, the fourth correlation model is used to characterize the correlation between battery SOC and polarization resistance, and the fifth correlation model is used to characterize the correlation between battery SOC and polarization capacitance. The open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance of the battery to be estimated are input into a pre-built battery terminal voltage estimation model for calculation to obtain the estimated terminal voltage of the battery. The battery terminal voltage estimation model is used to characterize the correlation between the battery terminal voltage and the open-circuit voltage, polarization capacitance, polarization resistance, current, and ohmic internal resistance.

3. The method according to claim 2, characterized in that, The first and second correlation models corresponding to different preset temperatures are constructed through the following steps: Obtain the battery capacity and ohmic internal resistance corresponding to different calendar aging times of the battery under test at the target temperature, as well as the battery capacity and ohmic internal resistance corresponding to different cycle numbers of the battery under test; Temperature correction processing is performed on the battery capacity and ohmic internal resistance of the battery under test at different calendar aging times at the target temperature to obtain the battery capacity and ohmic internal resistance of the battery under test at different calendar aging times at each preset temperature. Temperature correction processing is performed on the battery capacity corresponding to different cycle numbers of the battery under test at the target temperature to obtain the battery capacity and ohmic internal resistance corresponding to the battery at different cycle numbers at each preset temperature. Based on the battery capacity corresponding to different cycle numbers of the battery under test at each preset temperature and the battery capacity corresponding to different calendar aging times of the battery under test, a first correlation model is constructed for each temperature. Based on the ohmic internal resistance corresponding to different cycles of the battery under test at each preset temperature and the ohmic internal resistance corresponding to different calendar aging times of the battery under test, a second correlation model is constructed for each temperature.

4. The method according to claim 3, characterized in that, The third, fourth, and fifth association models are constructed through the following steps: Acquire the full discharge process test data of the battery under test at different temperatures. The full discharge process test data includes the open circuit voltage, current and terminal voltage of the battery under test at different SOCs during the discharge process. The open-circuit voltage, current and terminal voltage of the battery under test at different temperatures during discharge are input into the pre-built target model for calculation, so as to obtain the polarization resistance and polarization capacitance of the battery under test at different temperatures during discharge. Based on the open-circuit voltage corresponding to different SOCs of the battery under test during discharge at different temperatures, a third correlation model is constructed for each temperature. Based on the polarization resistance corresponding to different SOCs of the battery under test during discharge at different temperatures, a fourth correlation model is constructed for each temperature. Based on the polarization capacitance corresponding to different SOCs of the battery under test during discharge at different temperatures, a fifth correlation model is constructed for each temperature.

5. The method according to claim 3, characterized in that, The method further includes: The target battery capacity, initial SOC, preset current, and first curve of the battery under test are obtained at the target temperature. The first curve is used to characterize the change curve of battery SOC over time during the actual operation of the battery under test. The target battery capacity is calculated by inputting the target number of cycles and the target calendar aging time into the first correlation model at the target temperature. The target battery capacity, preset current, and initial SOC of the battery under test are input into a pre-built battery SOC estimation model, and a third curve is obtained by fitting. The third curve is used to characterize the change curve of the battery SOC of the battery under test over time. The fitting effect of the first association model is evaluated based on the similarity between the first curve and the second curve.

6. The method according to claim 5, characterized in that, The method further includes: The target ohmic internal resistance of the battery under test at the target temperature, the change of the target battery SOC over time, and the third curve are obtained. The third curve is used to characterize the change of the terminal voltage over time during the actual operation of the battery under test. The target ohmic internal resistance is calculated by inputting the target number of cycles and the target calendar aging time into the second correlation model at the target temperature. The SOC change information of the target battery over time is input into the third correlation model, the fourth correlation model, and the fifth correlation model for calculation, respectively, to obtain the change information of open circuit voltage over time, the change information of polarization resistance over time, and the change information of polarization capacitance over time; The target ohmic internal resistance, the change information of the open circuit voltage over time, the change information of the polarization resistance over time, the change information of the polarization capacitance over time, and the preset current are input into the battery terminal voltage estimation model to obtain a fourth curve. The fourth curve is used to characterize the change curve of the battery terminal voltage over time obtained by fitting the battery under test. The fitting performance of the second, third, fourth, and fifth association models is evaluated based on the similarity between the third and fourth curves.

7. A battery state parameter estimation device based on a battery model, characterized in that, The device includes: The acquisition module is used to acquire the battery temperature, cycle count, calendar aging time, current, initial SOC, and running time of the battery to be estimated. The first determining module is used to determine the target first association model corresponding to the battery temperature from the first association models corresponding to different preset temperatures. The target first association model is used to characterize the relationship between the battery capacity and the number of cycles and calendar aging time of the battery at the battery temperature. The first calculation module is used to input the cycle number and calendar aging time of the battery to be estimated into the target first association model for calculation to obtain the battery capacity of the battery to be estimated. The second calculation module is used to input the battery capacity, current, initial SOC and running time of the battery to be estimated into a pre-built battery SOC estimation model for calculation, so as to obtain the battery SOC estimate of the battery to be estimated. The battery SOC estimation model is used to characterize the correlation between battery SOC and battery capacity, current, initial SOC and running time.

8. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the battery state parameter estimation method based on the battery model as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the battery state parameter estimation method based on the battery model according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the battery state parameter estimation method based on a battery model as described in any one of claims 1 to 6.

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