Online Prediction Method, Device, Electronic Device and Storage Medium for Battery SOC

Abstract

The present invention provides an online prediction method, device, electronic device and storage medium for the state of charge (SOC) of a battery. The method includes: obtaining an estimated SOC and an estimated internal resistance predicted based on the real-time state of the battery to be measured; determining the internal resistance corresponding to the estimated SOC based on the estimated SOC and a pre-stored corresponding relationship, where the corresponding relationship is obtained by performing charge and discharge experiments on the battery to be measured in an offline state; determining the real-time internal resistance corresponding to the real-time state of the battery to be measured based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values, where the weight value is used to characterize the size of the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery to be measured; and recalculating the SOC of the battery to be measured based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery to be measured. The present invention can achieve online prediction of the battery SOC and reduce the deviation during online prediction of the battery SOC.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle battery management, and particularly to an online prediction method, device, electronic device and storage medium for battery SOC. Background Art

[0002] Lithium-ion batteries have high energy, long cycle life and no pollution, and are a good choice for the power system of electric vehicles. A key to improving the cost performance of power battery packs and ensuring the safety performance of electric vehicles is to improve the accuracy of power lithium battery state estimation and the reliability of battery management. Among them, the state of charge (SOC) of the battery is an important state quantity in the battery management system, which provides a basis for energy distribution management.

[0003] SOC cannot be directly measured. The main estimation methods for SOC of power batteries at home and abroad include ampere-hour integration method, open-circuit voltage method, discharge test method, etc. In the laboratory, these methods can accurately obtain the remaining power of the battery. However, in the actual application of electric vehicles, since the initial value of SOC cannot be guaranteed to be accurate and the measured current often contains noise, accurate SOC values are often not obtained only by the ampere-hour integration method. During the driving of the vehicle, the open-circuit voltage is difficult to measure, and it is also not feasible to discharge the battery.

[0004] Currently, due to the complex actual operating environment, there is a problem that the online prediction deviation of battery SOC is large and it is difficult to achieve. Summary of the Invention

[0005] The present invention provides an online prediction method, device, electronic device and storage medium for battery SOC, which can realize the online prediction of battery SOC and reduce the deviation during the online prediction of battery SOC.

[0006] In a first aspect, the present invention provides an online prediction method for battery SOC, including: obtaining an estimated SOC and an estimated internal resistance predicted based on the real-time state of a battery to be measured; determining an internal resistance corresponding to the estimated SOC based on the estimated SOC and a pre-stored corresponding relationship; the corresponding relationship is obtained by performing charge and discharge experiments on the battery to be measured in an offline state; determining a real-time internal resistance corresponding to the real-time state of the battery to be measured based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values; the weight value is used to characterize the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery to be measured; recalculating the SOC of the battery to be measured based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery to be measured.

[0007] The present invention provides an online prediction method for the SOC of a battery. First, the real-time state of the battery to be measured is preliminarily predicted and estimated to obtain an estimated SOC and an estimated internal resistance. Then, based on the corresponding relationship obtained from the charge and discharge experiments on the battery to be measured in the offline state, the internal resistance corresponding to the estimated SOC is determined. Finally, based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values, the real-time internal resistance corresponding to the real-time state of the battery to be measured is comprehensively determined, and the SOC is recalculated based on the real-time internal resistance. Since the weight value characterizes the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery to be measured, the real-time internal resistance is closer to the actual internal resistance corresponding to the real-time state, improving the accuracy of the real-time internal resistance, thereby improving the accuracy of the real-time SOC recalculated further, realizing the online prediction of the battery SOC, and reducing the deviation during the online prediction of the battery SOC.

[0008] In a possible implementation manner, obtaining the estimated SOC and the estimated internal resistance predicted based on the real-time state of the battery to be measured includes: obtaining the real-time state during the Nth use of the battery to be measured, where the real-time state includes the real-time current and the real-time voltage; inputting the real-time current and the real-time voltage into a pre-established SOC prediction model and an internal resistance prediction model respectively to obtain the estimated SOC and the estimated internal resistance; the SOC prediction model and the internal resistance prediction model are obtained after parameter update based on the state at the end of the (N - 1)th use of the battery to be measured and the state at the start of the Nth use.

[0009] In a possible implementation manner, before obtaining the estimated SOC and the estimated internal resistance predicted based on the real-time state of the battery to be measured, it further includes: obtaining the open-circuit voltage of the battery to be measured at the start of the Nth use; obtaining the SOC value and the internal resistance of the battery to be measured at the end of the (N - 1)th use; determining the SOC of the battery to be measured at the start of the Nth use based on the open-circuit voltage of the battery to be measured at the start of the Nth use and the corresponding relationship between the open-circuit voltage and the SOC; determining the target circuit model corresponding to the SOC from a plurality of pre-established circuit models based on the SOC of the battery to be measured at the start of the Nth use and the charge and discharge state of the battery to be measured during the Nth use; updating the parameters of the SOC state equation and the internal resistance state equation corresponding to the target circuit model based on the SOC and the open-circuit voltage of the battery to be measured at the start of the Nth use, and the SOC value and the internal resistance of the battery to be measured at the end of the (N - 1)th use to obtain the SOC prediction model and the internal resistance prediction model.

[0010] In a possible implementation manner, before determining a target circuit model corresponding to the SOC from multiple pre-established circuit models based on the SOC at the start of the Nth use of the battery under test and the charge and discharge states of the battery under test during the Nth use, the following steps are also included: Under an offline state and standard working conditions, charging experiments and discharging experiments are respectively conducted on the battery under test to obtain standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process; the standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process are fitted to obtain circuit models at each SOC during the charging process and the discharging process of the battery under test, and parameter identification is performed on the circuit models at each SOC; the circuit models at each SOC are fused to obtain a standard circuit model applicable to both the charging process and the discharging process; under an offline state and non-standard working conditions, charging experiments and discharging experiments are conducted on the battery under test to obtain current data and terminal voltage data under non-standard working conditions; based on the current data and terminal voltage data under non-standard working conditions, the standard circuit model is corrected to obtain a circuit model applicable to each SOC in a general environment.

[0011] In a possible implementation manner, before determining a real-time internal resistance corresponding to the real-time state of the battery under test based on an estimated internal resistance and an internal resistance corresponding to an estimated SOC, and their respective weight values, the following steps are also included: obtaining the open-circuit voltage of the battery under test at the start of the Nth use; determining the SOC corresponding to the open-circuit voltage based on the open-circuit voltage; determining the credibility of the SOC prediction model based on the estimated SOC and the SOC corresponding to the open-circuit voltage; the credibility is used to characterize the gap size between the estimated SOC and the SOC corresponding to the open-circuit voltage; determining the weight value of the estimated internal resistance and the weight value of the internal resistance corresponding to the estimated SOC based on the credibility of the SOC prediction model.

[0012] In a possible implementation manner, determining a real-time internal resistance corresponding to the real-time state of the battery under test based on an estimated internal resistance and an internal resistance corresponding to an estimated SOC, and their respective weight values, includes: determining the real-time internal resistance corresponding to the real-time state of the battery under test based on the following formula.

[0013] R0 = αR 0_M +(1 - α)R 0_K ; where R0 is the real-time internal resistance, α is the credibility of the SOC prediction model, R 0_M is the internal resistance corresponding to the estimated SOC, and R 0_K is the estimated internal resistance.

[0014] In a possible implementation manner, the SOC prediction model is expressed as the following formula:

[0015]

[0016] Among them, SOC is the real-time SOC corresponding to the real-time state of the battery to be measured, SOC0 is the SOC at the start of the Nth use, Q N is the nominal capacity of the battery to be measured, η is the discharge proportionality coefficient, I is the real-time current of the battery to be measured, C p is the polarization capacitance of the battery to be measured, R p is the polarization internal resistance of the battery to be measured, U p is C p and R p is the real-time voltage of the parallel RC link, C d is the diffusion capacitance of the battery to be measured, R d is the diffusion internal resistance of the battery to be measured, U d is C d and R d is the real-time voltage of the parallel RC link, U is the real-time terminal voltage of the battery to be measured, U ocv is the open-circuit voltage of the battery to be measured, R0 is the real-time internal resistance of the battery to be measured.

[0017] The internal resistance prediction model is expressed by the following formula:

[0018]

[0019] Among them, R 0,k+1 is the estimated value of the real-time internal resistance R0 at the (k + 1)th moment, R 0,k is the estimated value of the real-time internal resistance R0 at the kth moment, λ k+1 is the system noise at the (k + 1)th moment, U k+1 is the terminal voltage of the battery to be measured at the (k + 1)th moment, U ocv,k+1 is the open-circuit voltage of the battery to be measured at the (k + 1)th moment, I k+1 is the real-time current at the (k + 1)th moment, U p,k+1 is the real-time voltage across R p at the (k + 1)th moment, U d,k+1 is the voltage across R d at the (k + 1)th moment, v k is the measurement noise at the kth moment, where the kth moment and the (k + 1)th moment are two adjacent moments during the Nth use.

[0020] Second aspect, an embodiment of the present invention provides an online prediction device for the battery SOC, including: a communication module, configured to obtain an estimated SOC and an estimated internal resistance predicted based on the real-time state of the battery to be measured; a processing module, configured to determine an internal resistance corresponding to the estimated SOC based on the estimated SOC and a pre-stored corresponding relationship; the corresponding relationship is obtained by performing charge and discharge experiments on the battery to be measured in an offline state; the processing module is further configured to determine a real-time internal resistance corresponding to the real-time state of the battery to be measured based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values; the weight value is used to characterize the gap size between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery to be measured; the processing module is further configured to recalculate the SOC of the battery to be measured based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery to be measured.

[0021] Third aspect, an embodiment of the present invention provides an electronic device, characterized in that the electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the steps of the method according to the first aspect and any possible implementation manner in the first aspect as described above.

[0022] Fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, characterized in that when the computer program is executed by a processor, it implements the steps of the method according to the first aspect and any possible implementation manner in the first aspect as described above.

[0023] The technical effects brought by any implementation manner in the second aspect to the fourth aspect as described above can refer to the technical effects brought by the corresponding implementation manner in the first aspect, and will not be elaborated here. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a flowchart of an online prediction method for the battery SOC provided by an embodiment of the present invention;

[0026] Figure 2 is a flowchart of another online prediction method for the battery SOC provided by an embodiment of the present invention;

[0027] Figure 3It is a schematic flowchart of another online prediction method for battery SOC provided by an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of a charge and discharge circuit model provided by an embodiment of the present invention;

[0029] Figure 5 It is a schematic flowchart of another online prediction method for battery SOC provided by an embodiment of the present invention;

[0030] Figure 6 It is a schematic structural diagram of an online prediction device for battery SOC provided by an embodiment of the present invention;

[0031] Figure 7 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0032] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0033] In the description of the present invention, unless otherwise specified, " / " means "or". For example, A / B can represent A or B. The "and / or" herein is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, "at least one" and "a plurality of" refer to two or more. The words such as "first" and "second" do not limit the quantity and execution order, and the words such as "first" and "second" do not necessarily limit to be different.

[0034] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific way for easy understanding.

[0035] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments in conjunction with the drawings of the present invention.

[0036] As described in the background art, during the driving of an automobile, it is difficult to measure the open-circuit voltage, the actual operating environment is complex, and there are large deviations in the online prediction of the battery SOC, making it difficult to achieve the technical problem.

[0037] To solve the above technical problems, the embodiments of the present invention adopt a means of combining an equivalent model and a prediction method, and at the same time apply the effective information of the battery during use to perform online prediction of the SOC of the battery in the hybrid system. The equivalent model adopts a circuit model. Considering that the charge and discharge states of the battery in the hybrid system may alternate, when establishing the model, the influence of charge and discharge on the circuit parameters is fully considered, and a general charge and discharge model of the battery is established. The prediction method adopts the unscented Kalman filtering method. Considering that during online prediction, as the battery ages, the credibility of the previously established model will decrease. The embodiments of the present invention estimate the credibility of the battery model by online measuring the open-circuit voltage of the battery after being left unused for a long time and using the SOC value obtained by the open-circuit voltage method. The embodiments of the present invention correct the internal resistance of the battery according to the credibility of the second-order RC circuit model, and use the corrected internal resistance for the estimation of SOC, improving the online prediction accuracy and accuracy of SOC.

[0038] As Figure 1 shown, the embodiments of the present invention provide an online prediction method for battery SOC, and the execution subject is an online prediction device for battery SOC. The online prediction method includes steps S101-S104.

[0039] S101. Obtain an estimated SOC and an estimated internal resistance predicted based on the real-time state of the battery to be measured.

[0040] In some embodiments, the real-time state of the battery to be measured refers to the real-time state of the battery to be measured during use. Exemplarily, the real-time state of the battery to be measured may be the real-time state of the battery to be measured during the Nth use.

[0041] In some embodiments, the real-time state may include a real-time current and a real-time voltage.

[0042] As a possible implementation manner, the online prediction device may receive the estimated SOC and the estimated internal resistance sent by other devices.

[0043] Exemplarily, other devices are provided with a prediction model obtained by testing in an off-line state, and other devices predict the estimated SOC and the estimated internal resistance based on the prediction model. The online prediction device may communicate with other devices to obtain the estimated SOC and the estimated internal resistance.

[0044] As another possible implementation manner, the online prediction device may also obtain the real-time state of the battery to be measured and calculate the estimated SOC and the estimated internal resistance based on the real-time state.

[0045] Exemplarily, the online prediction device may determine the estimated SOC and the estimated internal resistance based on steps S1011 - S1012.

[0046] S1011. Obtain the real - time state of the battery under test during its Nth use.

[0047] Among them, the real - time state includes the real - time current and the real - time voltage.

[0048] S1012. Input the real - time current and the real - time voltage into the pre - established SOC prediction model and internal resistance prediction model respectively to obtain the estimated SOC and the estimated internal resistance.

[0049] Among them, the SOC prediction model and the internal resistance prediction model are obtained after parameter update based on the state at the end of the (N - 1)th use and the state at the start of the Nth use of the battery under test.

[0050] In some embodiments, the SOC prediction model may be represented as an SOC state equation. Exemplarily, the SOC state equation may be expressed as the following formula:

[0051]

[0052] Among them, SOC is the real - time SOC corresponding to the real - time state of the battery under test, SOC0 is the SOC at the start of the Nth use, Q N is the nominal capacity of the battery under test, η is the discharge ratio coefficient, I is the real - time current of the battery under test, C p is the polarization capacitance of the battery under test, R p is the polarization internal resistance of the battery under test, U p is for C p and R p is the real - time voltage of the parallel RC link of C d and R d is the diffusion capacitance of the battery under test, R d is for C d and R d is the real - time voltage of the parallel RC link of C ocv and U is the real - time terminal voltage of the battery under test, U

[0053] As a possible implementation, the online prediction device may obtain the estimated SOC through the unscented Kalman filter method based on the SOC state equation.

[0054] As another possible implementation, the online prediction device may obtain the estimated SOC through the particle filter method based on the SOC state equation.

[0055] In some embodiments, the internal resistance prediction model can be expressed as an internal resistance state equation. Exemplarily, the internal resistance state equation can be expressed by the following formula:

[0056]

[0057] where R 0,k+1 is the estimated value of the real-time internal resistance R0 at time k + 1, R 0,k is the estimated value of the real-time internal resistance R0 at time k, λ k+1 is the system noise at time k + 1, U k+1 is the terminal voltage of the battery under test at time k + 1, U ocv,k+1 is the open-circuit voltage of the battery under test at time k + 1, I k+1 is the real-time current at time k + 1, U p,k+1 is the real-time voltage across R p at time k + 1, U d,k+1 is the voltage across R d at time k + 1, v k is the measurement noise at time k, where time k and time k + 1 are two adjacent times during the Nth use.

[0058] As a possible implementation, the online prediction device can obtain the estimated internal resistance through the unscented Kalman filter method based on the internal resistance state equation.

[0059] As another possible implementation, the online prediction device can obtain the estimated internal resistance through the particle filter method based on the internal resistance state equation.

[0060] S102. Determine the internal resistance corresponding to the estimated SOC based on the estimated SOC and the pre-stored corresponding relationship.

[0061] The corresponding relationship is obtained from the charge and discharge experiments on the battery under test in the offline state.

[0062] S103. Determine the real-time internal resistance corresponding to the real-time state of the battery under test based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values.

[0063] The weight value is used to characterize the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery under test.

[0064] As a possible implementation, the online prediction device can determine the corrected internal resistance based on the following formula.

[0065] R0 = αR 0_M + (1 - α)R 0_K ;

[0066] Wherein, R0 is the real-time internal resistance, α is the confidence level of the SOC prediction model, and R 0_M is the internal resistance corresponding to the estimated SOC, and R 0_K is the estimated internal resistance.

[0067] It should be noted that α can be the confidence level of the SOC prediction model, which is used to characterize the reliability of the SOC prediction model. In this way, the online prediction device can comprehensively consider the factors of offline model prediction and online prediction, and comprehensively determine the real-time internal resistance based on the confidence level of the model, making the real-time internal resistance closer to the actual internal resistance corresponding to the real-time state, and improving the accuracy of the real-time internal resistance.

[0068] S104. Recalculate the SOC of the battery under test based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery under test.

[0069] As a possible implementation, the online prediction device can update the parameters of the SOC prediction model through the real-time internal resistance, and calculate the SOC of the battery based on the updated SOC prediction model, so as to obtain the real-time SOC corresponding to the real-time state of the battery under test.

[0070] The present invention provides an online prediction method for the SOC of a battery. First, a preliminary prediction estimate is made on the real-time state of the battery under test to obtain an estimated SOC and an estimated internal resistance. Then, based on the corresponding relationship obtained from the charge and discharge experiment on the battery under test in the offline state, the internal resistance corresponding to the estimated SOC is determined. Finally, based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values, the real-time internal resistance corresponding to the real-time state of the battery under test is comprehensively determined, and the SOC is recalculated based on the real-time internal resistance. Since the weight value characterizes the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery under test, the real-time internal resistance is made closer to the actual internal resistance corresponding to the real-time state, improving the accuracy of the real-time internal resistance, and thus improving the accuracy of the further recalculated real-time SOC, realizing the online prediction of the battery SOC and reducing the deviation during the online prediction of the battery SOC.

[0071] Optionally, as Figure 2 shown, for the online prediction method for the SOC of the battery provided in the embodiment of the present invention, before step S101, the online prediction method further includes steps S201-S205.

[0072] S201. Obtain the open-circuit voltage of the battery under test at the start of the Nth use.

[0073] S202. Obtain the SOC value and internal resistance of the battery under test at the end of the (N-1)th use.

[0074] S203. Determine the SOC of the battery under test at the start of its Nth use based on the open-circuit voltage of the battery under test at the start of its Nth use and the corresponding relationship between the open-circuit voltage and the SOC.

[0075] S204. Based on the SOC of the battery under test at the start of its Nth use and the charge-discharge state of the battery under test during its Nth use, determine the target circuit model corresponding to this SOC from multiple pre-established circuit models.

[0076] In some embodiments, a positive current indicates that the battery under test is in a charging state; a negative current indicates that the battery under test is in a discharging state.

[0077] S205. Update the parameters of the SOC state equation and the internal resistance state equation corresponding to the target circuit model based on the SOC and open-circuit voltage of the battery under test at the start of its Nth use, and the SOC value and internal resistance at the end of its (N - 1)th use, to obtain an SOC prediction model and an internal resistance prediction model.

[0078] In this way, the embodiments of the present invention can update the parameters of the SOC state equation and the internal resistance state equation corresponding to the target circuit model based on the state of the battery under test at the start of its Nth use and the state at the end of its (N - 1)th use, making the SOC prediction model and the internal resistance prediction model more time-sensitive and improving the accuracy of the SOC prediction model and the internal resistance prediction model during prediction.

[0079] Optionally, as Figure 3 shown, for the online prediction method of the battery SOC provided by the embodiments of the present invention, before step S204, the online prediction method further includes steps S301 - S305.

[0080] S301. Under offline conditions and standard working conditions, conduct a charge experiment and a discharge experiment on the battery under test respectively to obtain the standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process.

[0081] In some embodiments, refer to the composite pulse power characteristic test in the "FreedomCAR Battery Test Manual" to conduct charge and discharge experiments on the battery. Discharge a fully charged battery at a rate of 1C at 25°C. After 0.1 hour, let it stand for 0.5 hour, and record SOC = 0.9. Conduct experiments in sequence to obtain data of SOC = 0.8, 0.7,..., 0.2, 0.1 during discharge. Charge a fully discharged battery at a rate of 1C at 25°C for 0.1 hour, then let it stand for 0.5 hour, and record SOC = 0.1. Conduct experiments in sequence to obtain data at SOC = 0.2, 0.3,..., 0.8, 0.9 during discharge.

[0082] Considering the accuracy of the model and the computational complexity, the form of the model is determined to be a second-order RC circuit model. The component parameters in the original second-order RC circuit model are constant values. The research on the equivalent circuit model of the battery during the charge and discharge process finds that not only does the battery itself have a hysteresis voltage characteristic, but the values of the various circuit components in the equivalent circuit also vary during the charge and discharge process. Therefore, when modeling the battery in a hybrid power system, it is necessary to consider the generality of the model during the charge and discharge process and adopt an improved charge and discharge general model.

[0083] As Figure 4 shown, an embodiment of the present invention provides a charge and discharge circuit model. Among them, U ocv is the open-circuit voltage of the battery under test, R0 is the real-time internal resistance of the battery under test, R p is the polarization internal resistance of the battery under test, C p is the polarization capacitance of the battery under test, R d is the diffusion internal resistance of the battery under test, C d is the diffusion capacitance of the battery under test, R p , C p , R d , C d are all variable parameters related to the SOC and charge and discharge state of the battery under test.

[0084] S302. Fit the standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process to obtain the circuit model of the battery under test at each SOC during the charging process and the discharging process, and perform parameter identification on the circuit model at each SOC.

[0085] As a possible implementation, the online prediction device can perform parameter identification on the circuit model at each SOC based on Figure 4 the charge and discharge circuit model shown.

[0086] Exemplarily, obtain U OCV and R0 using experimental data: Judge the numerical value of the measured terminal voltage. When it changes less than a certain threshold value over time, it is considered that the static time is sufficient at this time, and the average value of the subsequent terminal voltage is taken as the open-circuit voltage U OCV . Use the voltage jump value at the end of the charge (discharge) to calculate the ohmic resistance R0 at this SOC. Assume that a jump occurs at time k, then the calculation formula for R0 is as follows:

[0087]

[0088] Wherein, R0 is the real-time internal resistance, u(k) is the open-circuit voltage of the battery to be measured at time k, u(k-1) is the open-circuit voltage of the battery to be measured at time k-1, i(k) is the real-time current of the battery to be measured at time k, and i(k-1) is the real-time current of the battery to be measured at time k-1.

[0089] In some embodiments, C p is the polarization capacitance of the battery to be measured, R p is the polarization internal resistance of the battery to be measured, C d is the diffusion capacitance of the battery to be measured, R d is the diffusion internal resistance of the battery to be measured, R p , C p , R d and C d and other parameters can be obtained by using the terminal voltage data after the battery charge (discharge) current stops acting and through a data fitting method. For example, the non-linear least squares method.

[0090] Exemplarily, the mathematical equation in the fitting process can be the following formula.

[0091]

[0092] Wherein, I is the average value of the current of the battery to be measured, T is the time when the current acts, U(t) is the terminal voltage at time t, and U ocv is the open-circuit voltage of the battery to be measured. In this way, after identifying the circuit parameters at each SOC during the discharge (charge) process, the circuit parameters at each SOC during the charge and discharge processes can be obtained.

[0093] It should be noted that the battery models during charging and discharging can be obtained respectively through the above steps. Now, they are combined to obtain a common charge-discharge model. From the identification results, it can be seen that the open-circuit voltage has a small difference during the charge and discharge processes, and according to the principle of the battery, it does not switch during the charge-discharge conversion process. The same open-circuit voltage is selected for the charge model and the discharge model, and it can be calculated by the following formula.

[0094] U ocv =(U ocv0 +U ocv1 ); wherein, U ocv is the common open-circuit voltage of the charge model and the discharge model, U ocv0 is the open-circuit voltage of the discharge model, and U ocv1 is the open-circuit voltage of the charge model.

[0095] Other parameters in the circuit model are switched according to the charging or discharging conditions. When the current is positive, it is judged as charging and flag = 1; when the current value is negative, it is judged as discharging and flag = 0. When flag = 1, the charging model is adopted. When flag = 0, the discharging model is adopted. When it is judged that flag changes between 0 and 1, the model is switched, that is, the parameters of each component in the circuit model are switched. During the switching process, the following processing is carried out in accordance with the electrical characteristics of each component: the resistance value is switched instantaneously, while the capacitance value is switched according to the exponential law.

[0096] Exemplarily, assuming that flag changes from 1 to 0 (i.e., from charging to discharging), the capacitance value can be calculated by the following formula.

[0097] C = C0 + (C1 - C0)e -t ; where C is the capacitance value, C1 is the capacitance value in the charging model, C0 is the capacitance value in the discharging model, and t is the time.

[0098] It should be noted that t starts timing from the switching instant. At the switching instant (t = 0+), the capacitance value does not jump, but remains C1. As time goes by, the value of C gradually switches to C0 according to the exponential relationship. Similarly, the capacitance value when flag changes from 0 to 1 (i.e., from discharging to charging) can be calculated by the following formula.

[0099] C = C1 + (C0 - C1)e -t ; where C is the capacitance value, C1 is the capacitance value in the charging model, C0 is the capacitance value in the discharging model, and t is the time.

[0100] S303. Integrate the circuit models under each SOC to obtain a standard circuit model that is applicable to both the charging process and the discharging process.

[0101] S304. Under the offline state and non-standard working conditions, conduct charging experiments and discharging experiments on the battery to be measured to obtain current data and terminal voltage data under non-standard working conditions.

[0102] S305. Based on the current data and terminal voltage data under non-standard working conditions, correct the standard circuit model to obtain a circuit model applicable to each SOC in the general environment.

[0103] It should be noted that the establishment of the charge and discharge circuit model for the battery has been completed above. However, the charge and discharge data are all carried out under standard conditions, that is, at a temperature of 25 degrees and a charge and discharge current of 1C. In actual operation, the environment will have a relatively large impact on the discharge capacity of the battery, such as temperature and charge and discharge rate. Therefore, when modeling the battery and then predicting the SOC, the factors of the external environment need to be considered. Since the parameter selection of each component of the circuit is based on the current SOC value, the concerned factors are integrated into the calculation of the SOC value, which will in turn affect the circuit model under different temperatures or different charge and discharge rates.

[0104] Exemplarily, the online prediction device can represent the remaining battery capacity SOC(t) at time t by the following formula.

[0105] Among them, Q N is the nominal capacity of the battery to be measured, η is the discharge proportionality coefficient, i(τ) is the discharge current of the battery to be measured at time τ, and SOC(0) is the remaining battery capacity of the battery to be measured at time 0. Since under actual working conditions, the SOC of the battery is related to factors such as the charge and discharge rate of the battery, the degree of battery aging, and the external temperature.

[0106] Considering that the charge and discharge rate and temperature have a large impact, their impact on SOC is uniformly integrated into the discharge proportionality coefficient η, as shown in the following formula.

[0107] η = η i ×η T ; where ηi is the impact of the charge and discharge rate on the battery SOC, and ηT is the impact of temperature on the battery SOC. Taking the amount of electricity discharged at 1C at 25°C as the standard, that is, η = 1. In order to obtain the expressions of ηi and ηT, the embodiments of the present invention can take the following steps.

[0108] Exemplarily, the online prediction device can obtain the current data and terminal voltage data under non-standard working conditions through the following experiment.

[0109] (1) Under the environment of 25°C, discharge the fully charged battery at different discharge rates, and record the experimental temperature and the maximum capacity discharged.

[0110] (2) At different temperatures, discharge the fully charged battery at a 1C discharge rate, and record the discharge rate and the maximum capacity discharged.

[0111] According to the data obtained from the above experiments (1) and (2), data fitting is performed to obtain the expressions of ηi and ηT.

[0112]

[0113] Among them, Ci where \(C\) is the discharge rate, \(T\) is the experimental temperature, and \(a\) i , \(b\) i , \(c\) i are fitting coefficients related to the discharge rate, and \(a\) T , \(b\) T , \(c\) T are fitting coefficients related to the temperature.

[0114] In this way, a general model considering the charge-discharge mode and environmental factors (temperature and charge-discharge current rate) can be obtained. During this process, it can be found that the influence of the aging degree of the battery during use on the SOC prediction is not considered. Because the aging degree is different from the temperature and discharge rate and is not accurately measurable. And there is no highly unified definition of the aging degree, and it is often characterized by the change in the internal resistance of the battery. The influence of the battery aging phenomenon on the SOC can be used to correct the SOC during the online prediction stage. Exemplarily, the online prediction device can correct the SOC during the online prediction stage through steps S101 - S104.

[0115] In this way, the embodiments of the present invention can, in the offline state, respectively experiment on the current and voltage of the battery under standard and non-standard working conditions, and comprehensively determine the circuit model applicable to each SOC in a general environment based on the data obtained from the experiments, improving the universality of the circuit model. And the circuit model is tested under different SOCs, improving the accuracy of the circuit model.

[0116] Optionally, as Figure 5 shown, in the online prediction method for the battery SOC provided by the embodiments of the present invention, before step S103, the online prediction method further includes steps S401 - S405.

[0117] S401. Obtain the open-circuit voltage of the battery to be tested at the start of the \(N\)th use.

[0118] S402. Based on the open-circuit voltage, determine the SOC corresponding to the open-circuit voltage.

[0119] Among them, the open-circuit voltage can be the terminal voltage of the battery to be tested at the start of the \(N\)th use.

[0120] S403. Based on the estimated SOC and the SOC corresponding to the open-circuit voltage, determine the credibility of the SOC prediction model.

[0121] In the embodiments of the present application, the credibility is used to characterize the gap size between the estimated SOC and the SOC corresponding to the open-circuit voltage.

[0122] S404. Based on the credibility of the SOC prediction model, determine the weight value of the estimated internal resistance and the weight value of the internal resistance corresponding to the estimated SOC.

[0123] As a possible implementation manner, embodiments of the present invention may determine the credibility of the SOC prediction model based on the following formula.

[0124]

[0125] Wherein, β is the minimum value of the credibility of the SOC prediction model, α is the credibility of the SOC prediction model, SOC_k is the estimated SOC, and SOC_ocv is the SOC corresponding to the open-circuit voltage.

[0126] It should be noted that since the value of the internal resistance itself is not large, and there is always noise in the process of measuring current and voltage, the internal resistance value obtained by the estimator in the initial stage often has errors, and as the program progresses, this error will decrease. Therefore, overall, a relatively high credibility is given to the SOC prediction model. Exemplarily, β can be 0.7.

[0127] In this way, embodiments of the present invention may determine the weight values of the estimated internal resistance and the internal resistance corresponding to the estimated SOC respectively based on the credibility of the SOC prediction model, comprehensively consider the factors of offline model prediction and online prediction, and comprehensively determine the real-time internal resistance based on the credibility of the model, so that the real-time internal resistance is closer to the actual internal resistance corresponding to the real-time state, and the accuracy of the real-time internal resistance is improved.

[0128] It should be noted that when performing online prediction of the SOC of the battery, the main problem to be solved is the reliability of the prediction model. As the battery is used, affected by the external environment and the number of charge and discharge cycles, the initially established battery model will deviate from the actual situation. If this deviation is not considered and only the initially established model is applied to predict the SOC based on the measured external data, the prediction accuracy often decreases. Therefore, the credibility of the SOC prediction model during the operation of the battery is defined here. To measure the reliability of the model, a reference point, that is, the true value, needs to be found. By measuring the battery voltage after the vehicle has not run for a long time and using the open-circuit voltage method to estimate the SOC value at this time as the true SOC value at this time to measure the reliability of the circuit model. Since the influence of temperature and charge and discharge rate on the battery capacity has been considered in the process of establishing the model, it can be considered that during the operation of the battery, the decrease in the reliability of the model is mainly caused by the aging degree of the battery. And the aging degree of the battery is reflected in the internal resistance, which in turn affects the prediction of the SOC. To obtain a more accurate SOC prediction value, first use the Kalman filter method to estimate the internal resistance of the battery, correct the internal resistance R0 using the reliability parameter of the model, and then use this internal resistance value to further predict the SOC.

[0129] It should be understood that the sequence numbers of the steps in the above embodiments do not indicate the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0130] The following is an embodiment of the device of the present invention. For the details not described in detail, reference may be made to the corresponding method embodiments above.

[0131] Figure 6 FIG. shows a schematic structural diagram of an on-line prediction device for the SOC of a battery provided by an embodiment of the present invention. The on-line prediction device 500 includes a communication module 501 and a processing module 502.

[0132] The communication module 501 is configured to obtain an estimated SOC and an estimated internal resistance predicted based on the real-time state of the battery to be measured.

[0133] The processing module 502 is configured to determine the internal resistance corresponding to the estimated SOC based on the estimated SOC and a pre-stored corresponding relationship; the corresponding relationship is obtained by performing charge and discharge experiments on the battery to be measured in an off-line state.

[0134] The processing module 502 is further configured to determine the real-time internal resistance corresponding to the real-time state of the battery to be measured based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values; the weight value is used to characterize the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery to be measured.

[0135] The processing module 502 is further configured to recalculate the SOC of the battery to be measured based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery to be measured.

[0136] In a possible implementation manner, the communication module 501 is specifically configured to obtain the real-time state of the battery to be measured during the Nth use, and the real-time state includes real-time current and real-time voltage; the processing module 502 is specifically configured to input the real-time current and real-time voltage into a pre-established SOC prediction model and an internal resistance prediction model to obtain an estimated SOC and an estimated internal resistance respectively; the SOC prediction model and the internal resistance prediction model are obtained after parameter update based on the state at the end of the (N-1)th use of the battery to be measured and the state at the start of the Nth use.

[0137] In a possible implementation, the communication module 501 is further configured to obtain the open-circuit voltage of the battery under test at the start of the Nth use; obtain the SOC value and internal resistance of the battery under test at the end of the (N - 1)th use; the processing module 502 is further configured to determine the SOC of the battery under test at the start of the Nth use based on the open-circuit voltage of the battery under test at the start of the Nth use and the corresponding relationship between the open-circuit voltage and the SOC; determine the target circuit model corresponding to the SOC from multiple pre-established circuit models based on the SOC of the battery under test at the start of the Nth use and the charge-discharge state of the battery under test during the Nth use; update the parameters of the SOC state equation and the internal resistance state equation corresponding to the target circuit model based on the SOC and open-circuit voltage of the battery under test at the start of the Nth use, and the SOC value and internal resistance of the battery under test at the end of the (N - 1)th use, to obtain the SOC prediction model and the internal resistance prediction model.

[0138] In a possible implementation, the processing module 502 is further configured to, in an offline state and under standard working conditions, perform a charge experiment and a discharge experiment on the battery under test respectively, to obtain the standard current data and the standard terminal voltage data of the battery under test during the charging process and the discharging process; fit the standard current data and the standard terminal voltage data of the battery under test during the charging process and the discharging process, to obtain the circuit models at each SOC during the charging process and the discharging process of the battery under test, and perform parameter identification on the circuit models at each SOC; fuse the circuit models at each SOC to obtain a standard circuit model applicable to both the charging process and the discharging process; in an offline state and under non-standard working conditions, perform a charge experiment and a discharge experiment on the battery under test, to obtain the current data and the terminal voltage data under non-standard working conditions; correct the standard circuit model based on the current data and the terminal voltage data under non-standard working conditions, to obtain the circuit models at each SOC applicable to a general environment.

[0139] In a possible implementation, the processing module 502 is further configured to obtain the open-circuit voltage of the battery under test at the start of the Nth use; determine the SOC corresponding to the open-circuit voltage based on the open-circuit voltage; determine the credibility of the SOC prediction model based on the estimated SOC and the SOC corresponding to the open-circuit voltage; the credibility is used to characterize the gap size between the estimated SOC and the SOC corresponding to the open-circuit voltage; determine the weight value of the estimated internal resistance and the weight value of the internal resistance corresponding to the estimated SOC based on the credibility of the SOC prediction model.

[0140] Figure 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 7As shown, the electronic device 600 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, it implements the steps in the above method embodiments, such as Figure 1 the steps 101 to 104 shown. Alternatively, when the processor 601 executes the computer program 603, it implements the functions of each module / unit in the above device embodiments. For example, Figure 5 the functions of the communication module 501 and the processing module 502 shown.

[0141] Exemplarily, the computer program 603 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 602 and executed by the processor 601 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 603 in the electronic device 600. For example, the computer program 603 can be divided into Figure 5 the communication module 501 and the processing module 502 shown.

[0142] The so-called processor 601 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0143] The memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. The memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk equipped on the electronic device 600, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 602 may also include both the internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store the computer program and other programs and data required by the terminal. The memory 602 may also be used to temporarily store data that has been output or will be output.

[0144] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0145] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0146] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. An online prediction method for battery SOC, characterized in that, Including: Obtaining an estimated SOC and an estimated internal resistance based on the real-time state prediction of the battery under test; the estimated SOC is obtained by predicting based on an SOC prediction model, and the estimated internal resistance is obtained by predicting based on an internal resistance prediction model; Determining the internal resistance corresponding to the estimated SOC based on the estimated SOC and a pre-stored corresponding relationship; the corresponding relationship is obtained by performing charge and discharge experiments on the battery under test in an offline state; Determining the real-time internal resistance corresponding to the real-time state of the battery under test based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values; The weight value is used to characterize the magnitude of the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery under test; Recalculating the SOC of the battery under test based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery under test; Before determining the real-time internal resistance corresponding to the real-time state of the battery under test based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values, it further includes: obtaining the open-circuit voltage at the start of the Nth use of the battery under test; determining the SOC corresponding to the open-circuit voltage based on the open-circuit voltage; determining the credibility of the SOC prediction model based on the estimated SOC and the SOC corresponding to the open-circuit voltage; the credibility is used to characterize the magnitude of the gap between the estimated SOC and the SOC corresponding to the open-circuit voltage; determining the weight value of the estimated internal resistance and the weight value of the internal resistance corresponding to the estimated SOC based on the credibility of the SOC prediction model; Determining the real-time internal resistance corresponding to the real-time state of the battery under test based on the estimated internal resistance and the internal resistance corresponding to the estimated SOC, as well as their respective weight values, includes: determining the real-time internal resistance corresponding to the real-time state of the battery under test based on the following formula; R0 = αR 0_M +(1 - α)R 0_K ; where R0 is the real-time internal resistance, α is the credibility of the SOC prediction model, R 0_M is the internal resistance corresponding to the estimated SOC, and R 0_K is the estimated internal resistance.

2. The online prediction method of the battery SOC according to claim 1, wherein, The obtaining of the estimated SOC and the estimated internal resistance based on the real-time state prediction of the battery under test includes: Obtaining the real-time state during the Nth use of the battery under test, where the real-time state includes a real-time current and a real-time voltage; Inputting the real-time current and the real-time voltage into a pre-established SOC prediction model and an internal resistance prediction model to respectively obtain the estimated SOC and the estimated internal resistance; the SOC prediction model and the internal resistance prediction model are obtained after parameter update based on the state at the end of the (N - 1)th use and the state at the start of the Nth use of the battery under test.

3. The online prediction method of the battery SOC according to claim 2, characterized in that, Before the obtaining of the estimated SOC and the estimated internal resistance based on the real-time state prediction of the battery under test, it further includes: Obtaining the open-circuit voltage at the start of the Nth use of the battery under test; Obtaining the SOC value and the internal resistance at the end of the (N - 1)th use of the battery under test; Determining the SOC at the start of the Nth use of the battery under test based on the open-circuit voltage at the start of the Nth use of the battery under test and the corresponding relationship between the open-circuit voltage and the SOC; Determining the target circuit model corresponding to the SOC from a plurality of pre-established circuit models based on the SOC at the start of the Nth use of the battery under test and the charge and discharge state during the Nth use of the battery under test; Based on the SOC and open-circuit voltage of the battery under test at the start of the Nth use, as well as the SOC value and internal resistance of the battery under test at the end of the (N - 1)th use, update the parameters of the SOC state equation and internal resistance state equation corresponding to the target circuit model to obtain the SOC prediction model and internal resistance prediction model.

4. The online prediction method of the battery SOC according to claim 3, characterized in that, Before determining the target circuit model corresponding to the SOC from multiple pre-established circuit models based on the SOC of the battery under test at the start of the Nth use and the charge-discharge state of the battery under test during the Nth use, it further includes: Under offline conditions and standard working conditions, conduct charge experiments and discharge experiments on the battery under test respectively to obtain the standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process; Fit the standard current data and standard terminal voltage data of the battery under test during the charging process and the discharging process to obtain the circuit models at each SOC during the charging process and the discharging process of the battery under test, and perform parameter identification on the circuit models at each SOC; Fuse the circuit models at each SOC to obtain a standard circuit model applicable to both the charging process and the discharging process; Under offline conditions and non-standard working conditions, conduct charge experiments and discharge experiments on the battery under test to obtain the current data and terminal voltage data under non-standard working conditions; Based on the current data and terminal voltage data under non-standard working conditions, correct the standard circuit model to obtain a circuit model applicable to each SOC in a general environment.

5. The online prediction method of the battery SOC according to claim 1, characterized in that, The SOC prediction model is expressed as the following formula: Among them, SOC is the real-time SOC corresponding to the real-time state of the battery to be measured, SOC0 is the SOC at the start of the Nth use, Q N is the nominal capacity of the battery to be measured, η is the discharge proportionality coefficient, I is the real-time current of the battery to be measured, C p is the polarization capacitance of the battery to be measured, R p is the polarization internal resistance of the battery to be measured, U p is C p and R p is the real-time voltage of the parallel RC link of C d is the diffusion capacitance of the battery to be measured, R d is the diffusion internal resistance of the battery to be measured, U d is C d and R d is the real-time voltage of the parallel RC link of C, U is the real-time terminal voltage of the battery to be measured, U ocv is the open-circuit voltage of the battery to be measured, and R0 is the real-time internal resistance of the battery to be measured; The internal resistance prediction model is expressed as the following formula: Among them, R 0,k+1 is the estimated value of the real-time internal resistance R0 at the (k + 1)th moment, R 0,k is the estimated value of the real-time internal resistance R0 at the kth moment, λ k+1 is the system noise at the (k + 1)th moment, U k+1 is the terminal voltage of the battery under test at the (k + 1)th moment, U ocv,k+1 is the open-circuit voltage of the battery under test at the (k + 1)th moment, I k+1 is the real-time current at the (k + 1)th moment, U p,k+1 is the real-time voltage across R p at the (k + 1)th moment, U d,k+1 is the voltage across R d at the (k + 1)th moment, v k is the measurement noise at the kth moment. Among them, the kth moment and the (k + 1)th moment are two adjacent moments during the Nth use process.

6. An online prediction device for battery SOC, characterized in that, It includes: A communication module for obtaining the estimated SOC and estimated internal resistance predicted based on the real-time state of the battery under test; The estimated SOC is predicted based on the SOC prediction model, and the estimated internal resistance is predicted based on the internal resistance prediction model; A processing module for determining the internal resistance corresponding to the estimated SOC based on the estimated SOC and the pre-stored corresponding relationship; the corresponding relationship is obtained by conducting charge-discharge experiments on the battery under test under offline conditions; The processing module is further used to determine the real-time internal resistance corresponding to the real-time state of the battery under test based on the estimated internal resistance, the internal resistance corresponding to the estimated SOC, and their respective weight values; the weight value is used to characterize the gap between the estimated SOC and the real-time SOC corresponding to the real-time state of the battery under test; The processing module is further used to recalculate the SOC of the battery under test based on the real-time internal resistance to obtain the real-time SOC corresponding to the real-time state of the battery under test; The communication module is further used to obtain the open-circuit voltage of the battery under test at the start of the Nth use; The processing module is further used to determine the SOC corresponding to the open-circuit voltage based on the open-circuit voltage; Based on the estimated SOC and the SOC corresponding to the open-circuit voltage, determine the credibility of the SOC prediction model; The credibility is used to characterize the gap between the estimated SOC and the SOC corresponding to the open-circuit voltage; based on the credibility of the SOC prediction model, the weight value of the estimated internal resistance and the weight value of the internal resistance corresponding to the estimated SOC are determined; The processing module is specifically configured to determine the real-time internal resistance corresponding to the real-time state of the battery under test based on the following formula: R0 = αR 0_M +(1 - α)R 0_K ; where R0 is the real-time internal resistance, α is the credibility of the SOC prediction model, and R 0_M is the internal resistance corresponding to the estimated SOC, and R 0_K is the estimated internal resistance.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 above are implemented.

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