Battery SOC and SOP joint estimation method, device, storage medium and product

Abstract

The present invention discloses a battery SOC and SOP joint estimation method, device, storage medium and product, wherein the estimation method comprises constructing a battery dynamic characteristic linear model; performing online parameter identification on the battery dynamic characteristic linear model to obtain parameters of the battery dynamic characteristic linear model; estimating the real-time SOC value of the battery according to the parameters of the battery dynamic characteristic linear model; and calculating the continuous peak charge and discharge power of the battery under multiple constraints according to the battery dynamic characteristic linear model and the real-time SOC value of the battery. The present invention improves real-time performance while ensuring estimation accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery state estimation, and in particular relates to a method, device, storage medium and product for jointly estimating SOC (State of charge) and SOP (State of power) of a lithium-ion battery based on a linear model. Background Art

[0002] Lithium-ion batteries have become the power source of electric vehicles due to their high energy density, low discharge rate, and long cycle life. In order to ensure the safe and stable operation of lithium-ion batteries, electric vehicles must be equipped with a battery management system (BMS) to monitor and adjust the working state of the battery. SOC and SOP are two core state quantities of lithium-ion batteries, providing key information about the battery's operation. However, SOC and SOP cannot be measured directly, and need to be estimated in real time through measurable quantities such as voltage and current. SOC describes the ratio of the remaining capacity of the battery to its maximum capacity, which is directly related to the expected range of electric vehicles. SOP describes the maximum power that the battery can release or absorb within a certain time interval, which can be used to evaluate the maximum charge and discharge capacity of the battery. Accurate estimation of SOP is an important prerequisite for achieving maximum battery energy utilization and power allocation, and is crucial for optimizing energy management and formulating driving strategies. The SOP of the battery is constrained by voltage, current, and SOC, so as to ensure that it does not damage the battery as much as possible while outputting maximum power. Therefore, SOC and SOP are usually estimated jointly.

[0003] In the battery management system, SOC estimation methods mainly include ampere-hour integration method, table lookup method, model-based method and data-driven method. The ampere-hour integration method is simple, but the error accumulates; the table lookup method relies on OCV (Open Circuit Voltage) and is greatly affected by temperature; the model-based method is accurate but complex; the data-driven method has strong adaptability, but relies on a large amount of data support, has high training costs, limited generalization ability, and poor model interpretability. At present, battery SOP estimation methods are mainly divided into two categories: feature map-based methods and model-based methods. The feature map-based method is easy to implement, but this method has limited adaptability to dynamic conditions and is difficult to meet actual needs; although the electrochemical battery model can provide higher estimation accuracy, its complexity greatly increases the calculation time and cost. Summary of the invention

[0004] The purpose of the present invention is to provide a battery SOC and SOP joint estimation method, device, storage medium and product to solve the problem that traditional methods are complex, have high calculation time and cost, and are difficult to meet the requirements of real-time estimation.

[0005] The present invention solves the above technical problems through the following technical solutions: a method for jointly estimating battery SOC and SOP, comprising:

[0006] Construct a linear model of battery dynamic characteristics;

[0007] Performing online parameter identification on the battery dynamic characteristic linear model to obtain parameters of the battery dynamic characteristic linear model;

[0008] Estimate the real-time SOC value of the battery according to the parameters of the battery dynamic characteristic linear model;

[0009] Calculate the continuous peak charge and discharge power of the battery under multiple constraints according to the battery dynamic characteristic linear model and the real-time SOC value of the battery;

[0010] Among them, the battery dynamic characteristic linear model is:

[0011] ;

[0012] in, Indicates The battery terminal voltage value at a moment, Indicates The battery terminal voltage value at a moment, Indicates The battery charge and discharge current value at each moment, Indicates The battery SOC value at a certain moment, , , and represents the polynomial coefficient, and m and n represent the order of regression terms.

[0013] Furthermore, before performing online parameter identification on the battery dynamic characteristic linear model, the estimation method further includes:

[0014] The order of the regression term of the battery dynamic characteristic linear model is determined based on the Akaike information criterion.

[0015] Furthermore, a recursive least square method with a forgetting factor is used to perform online parameter identification on the battery dynamic characteristic linear model.

[0016] Furthermore, an adaptive Kalman filter algorithm is used to estimate the real-time SOC value of the battery, including:

[0017] According to the parameters of the linear model of the battery dynamic characteristics, the state space equation of the battery is constructed, which is specifically:

[0018] ;

[0019] ;

[0020] in, Indicates The battery SOC value at a certain moment, Indicates The battery SOC value at a certain moment, is the Coulomb efficiency, Indicates the rated capacity of the battery. Indicates The battery charge and discharge current value at each moment, Indicates The battery charge and discharge current value at each moment, Indicates Zero-mean system noise at time instant, Indicates The battery terminal voltage value at a moment, Indicates The battery terminal voltage value at a moment, , , , , All are The polynomial coefficients of the linear model of the battery dynamic characteristics at each moment, Indicates Zero-mean measurement noise at each moment;

[0021] The linear model parameters of the battery dynamic characteristics obtained by online parameter identification and the state space equation are used to calculate the The battery terminal voltage at the moment ;

[0022] Calculate the prior estimate of the state of charge and covariance matrix. The specific calculation formula is:

[0023] ;

[0024] ;

[0025] in, Indicates The prior estimate of the battery SOC at the moment, Indicates The battery SOC posterior estimate at the moment, Indicates The prior estimate of the covariance matrix at time instants, represents the identity matrix, Indicates The posterior estimate of the covariance matrix at time instant, the superscript T indicates transposition, Indicates The system noise covariance matrix at each moment;

[0026] Calculate the The battery SOC posterior estimate at the moment and the The posterior estimate of the covariance matrix at the moment is The battery SOC posterior estimation value at the moment is used as the real-time SOC value of the battery; wherein the specific calculation formula is:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] in, Indicates The battery SOC posterior estimate at the moment, Indicates The Kalman gain matrix at the moment is: Indicates The innovation matrix at each moment is Indicates The posterior estimate of the covariance matrix at time instant, Indicates The terminal voltage measurement value at a moment, Indicates The measurement noise covariance at time instant is, Indicates The measurement noise covariance at time instant is, Indicates The system noise covariance matrix at time instant is: Indicates The new information real-time estimated covariance function is obtained by the window estimation principle at each moment, and M represents the window size.

[0035] Furthermore, the specific calculation process of the continuous peak charge and discharge power of the battery under the multiple constraints includes:

[0036] Calculate the continuous peak charge and discharge current under voltage constraints. The specific calculation formula is:

[0037] ;

[0038] ;

[0039] in, It means that under the voltage constraint, A sampling cycle starting at time Peak discharge current within , , , , They are all polynomial coefficients of the linear model of battery dynamic characteristics; Indicates the battery discharge cut-off voltage; Indicates The battery SOC value at a certain moment; represents the sampling interval, L represents the number of sampling times in a sampling period; represents the Coulomb efficiency; Indicates the rated capacity of the battery; It means that under the voltage constraint, A sampling cycle starting at time Peak charging current within Indicates the battery charging cut-off voltage;

[0040] Calculate the continuous peak charge and discharge current under SOC constraint. The specific calculation formula is:

[0041] ;

[0042] ;

[0043] in, It means that under the SOC constraint, A sampling cycle starting at time Peak discharge current within Indicates the minimum state of charge of the battery; It means that under the SOC constraint, A sampling cycle starting at time Peak charging current within Indicates the maximum state of charge of the battery;

[0044] Calculate the continuous peak charge and discharge current under voltage constraints, current constraints, and SOC constraints. The specific calculation formula is:

[0045] ;

[0046] ;

[0047] in, It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time Peak discharge current within It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time Peak charging current within Indicates the peak discharge current designed for the battery; Indicates the peak charging current designed for the battery;

[0048] The continuous peak charge and discharge power is calculated according to the continuous peak charge and discharge current under the voltage constraint, current constraint and SOC constraint. The specific calculation formula is:

[0049] ;

[0050] ;

[0051] in, Indicates the A sampling cycle starting at time The peak discharge power within Indicates the A sampling cycle starting at time Peak charging power within.

[0052] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the battery SOC and SOP joint estimation method as described above.

[0053] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the battery SOC and SOP joint estimation method as described above when the computer program / instruction is executed by a processor.

[0054] Based on the same concept, the present invention also provides a computer program product, including a computer program / instruction, which implements the battery SOC and SOP joint estimation method as described above when executed by a processor. Beneficial Effects

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] Taking into account the requirements of actual engineering applications for algorithm calculation speed and complexity, the present invention constructs a new linear model of battery dynamic characteristics. The linear model has a simple structure, and the total execution time is only about half of the traditional Thevenin model (i.e., Thevenin model), and the modeling accuracy is improved by about 15%. The present invention improves real-time performance while ensuring estimation accuracy.

[0057] The present invention realizes SOC state estimation based on the Akaike information criterion; at the same time, the SOC, terminal voltage, etc. of the battery are integrated to realize online SOP estimation under multiple constraints, and realizes battery multi-state joint estimation based on the battery dynamic characteristic linear model; based on the battery dynamic characteristic linear model, the present invention derives the state space equations that characterize the SOC and the battery dynamic characteristic linear model parameters; compared with the traditional ECM model (i.e., the equivalent circuit model), the present invention does not need to obtain the OCV-SOC curve through experiments, thus saving time and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 is a flow chart of a method for jointly estimating battery SOC and SOP in an embodiment of the present invention;

[0060] Figure 2 is the terminal voltage prediction result based on the battery dynamic characteristic linear model in the embodiment of the present invention;

[0061] Figure 3 is a terminal voltage prediction error curve in an embodiment of the present invention;

[0062] Figure 4 is the SOC prediction result in the embodiment of the present invention;

[0063] Figure 5 is an SOC prediction error curve in an embodiment of the present invention;

[0064] Figure 6 is a continuous peak discharge power curve in an embodiment of the present invention;

[0065] Figure 7 It is a continuous peak charging power curve in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following is a clear and complete description of the technical solution in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0068] Embodiment 1

[0069] Figure 1 The flowchart of the method for jointly estimating the battery SOC and SOP provided by the present invention is shown. This embodiment takes a lithium-ion battery as an example. Figure 1 As shown, the estimation method comprises the following steps:

[0070] Step 1: Construct a linear model of battery dynamic characteristics.

[0071] Without losing generality, a linear model describing the dynamic characteristics of lithium-ion batteries is constructed, and its specific expression is:

[0072] (1)

[0073] in, Indicates The battery terminal voltage value at a moment; Indicates The battery terminal voltage value at a moment; Indicates The battery charge and discharge current value at a moment, when When is the charging current, is positive; when When the discharge current is is negative; Indicates The battery SOC value at a certain moment, , , and represents the polynomial coefficient, and m and n represent the order of regression terms.

[0074] Step 2: Perform online parameter identification on the battery dynamic characteristic linear model to obtain the parameters of the battery dynamic characteristic linear model.

[0075] In order to simplify the online parameter identification of the battery dynamic characteristics linear model, before parameter identification, the order of the regression term of the battery dynamic characteristics linear model is determined based on the Akaike information criterion, that is, the values ​​of m and n are determined. The Akaike information criterion is used to weigh the accuracy and complexity of the battery dynamic characteristics linear model. When , the AIC (Akaikeinformation criterion) value is the smallest, that is, The linear model of battery dynamic characteristics achieves the best balance between accuracy and complexity. When , the linear model of battery dynamic characteristics is:

[0076] (2)

[0077] in, , , , , are the polynomial coefficients of the linear model of battery dynamic characteristics.

[0078] For online modeling, the polynomial coefficients of the battery dynamic characteristics linear model will change under different working conditions and temperatures. In order to track the changes in the parameters of the battery dynamic characteristics linear model (i.e., the polynomial coefficients), the recursive least squares method with forgetting factor is used to perform online parameter identification on the battery dynamic characteristics linear model (i.e., formula (2)) and dynamically adjust , , , , , adapt to data changes and reduce overfitting to describe the dynamic characteristics of the battery. Before the first online parameter identification, , , , , Perform offline initialization.

[0079] Step 3: Estimate the real-time SOC value of the battery based on the parameters of the linear model of the battery dynamic characteristics.

[0080] In a specific embodiment of the present invention, an adaptive Kalman filter algorithm is used to estimate the real-time SOC value of the battery, and the specific estimation process includes:

[0081] Step 3.1: Based on the ampere-hour integration method and the parameters of the linear model of the battery dynamic characteristics, the state space equation of the battery is constructed.

[0082] The specific formula of the ampere-hour integration method is:

[0083] (3)

[0084] in, is the Coulomb efficiency, Indicates the rated capacity of the battery.

[0085] Therefore, the state space equation of the battery is specifically:

[0086] (4)

[0087] (5)

[0088] in, Indicates The battery SOC value at a certain moment, Indicates The battery charge and discharge current value at each moment, Indicates The battery charge and discharge current value at each moment, Indicates Zero-mean system noise at time instant, Indicates The battery terminal voltage value at a moment, , , , , All are The polynomial coefficients of the linear model of the battery dynamic characteristics at each moment (i.e., the parameters obtained by online parameter identification in step 2), Indicates Zero-mean measurement noise at each time instant.

[0089] Step 3.2: Substitute the parameters obtained by online parameter identification in step 2 and the parameters calculated by formula (4) into Substituting into formula (5), we can calculate The battery terminal voltage at the moment .

[0090] Step 3.3: Calculate prior estimates of the state of charge and covariance matrix , , the specific calculation formula is:

[0091] (6)

[0092] (7)

[0093] in, Indicates The prior estimate of the battery SOC at the moment, Indicates The battery SOC posterior estimate at the moment, Indicates The prior estimate of the covariance matrix at time instants, represents the identity matrix, Indicates The posterior estimate of the covariance matrix at time instant, the superscript T indicates transposition, Indicates The system noise covariance matrix at each moment.

[0094] Step 3.4: Calculate the The battery SOC posterior estimate at the moment and The posterior estimate of the covariance matrix at time , with the The battery SOC posterior estimate at the moment As the real-time SOC value of the battery (i.e. The specific calculation formula is:

[0095] (8)

[0096] (9)

[0097] (10)

[0098] (11)

[0099] (12)

[0100] (13)

[0101] (14)

[0102] in, Indicates The battery SOC posterior estimate at the moment, Indicates The Kalman gain matrix at the moment is: Indicates The innovation matrix at each moment is Indicates The posterior estimate of the covariance matrix at time instant, Indicates The terminal voltage measurement value at a moment, Indicates The measurement noise covariance at time instant is, Indicates The measurement noise covariance at time instant is, Indicates The system noise covariance matrix at time instant is: Indicates The new information real-time estimated covariance function is obtained by the window estimation principle at each moment, and M represents the window size.

[0103] Step 4: Calculate the continuous peak charge and discharge power of the battery under multiple constraints based on the battery dynamic characteristic linear model and the real-time SOC value of the battery.

[0104] In this embodiment, the constraints include voltage constraints, current constraints and SOC constraints. In a specific embodiment of the present invention, the specific calculation process of the continuous peak charge and discharge power of the battery under multiple constraints includes:

[0105] Step 4.1: Calculate the continuous peak charge and discharge current under voltage constraints.

[0106] Within the specified sampling times L, the battery is charged and discharged at a constant current at peak value, that is, , the parameters of the linear model of battery dynamic characteristics remain unchanged during peak charge and discharge. During the charge and discharge process, the voltage and current are in the sampling interval In order to simplify the calculation process, it is assumed that the voltage and current values ​​remain constant at adjacent time points, that is, , , then formula (2) is approximately equivalent to:

[0107] (15)

[0108] In the The terminal voltage at a moment for:

[0109] (16)

[0110] Battery from Starting from the first moment, the battery is charged and discharged at a constant current within a given time interval. Battery state of charge at a moment It can be expressed as:

[0111] (17)

[0112] in, represents the Coulomb efficiency; Indicates the rated capacity of the battery.

[0113] Substituting formula (17) into formula (16), we can obtain:

[0114] (18)

[0115] Therefore, in the The charge and discharge current at each moment for:

[0116] (19)

[0117] The actual terminal voltage of the battery should be between the cut-off voltages to maintain the battery in normal working condition. The moment to The calculation formula for the peak charge and discharge current within a time range is:

[0118] (20)

[0119] (twenty one)

[0120] in, It means that under the voltage constraint, A sampling cycle starting at time Peak discharge current within Indicates the battery discharge cut-off voltage; It means that under the voltage constraint, A sampling cycle starting at time Peak charging current within Indicates the battery charging cut-off voltage.

[0121] Step 4.2: Calculate the continuous peak charge and discharge current under SOC constraints.

[0122] Set the upper and lower limits of the battery SOC, and the maximum state of charge is set to , the minimum state of charge is set to When charging, The value of ; When discharging, The value of The SOC is calculated by accumulating the integral of the battery current using the ampere-hour integration method. Then, one sampling cycle The calculation formula for the continuous peak charge and discharge current is:

[0123] (twenty two)

[0124] (twenty three)

[0125] in, It means that under the SOC constraint, A sampling cycle starting at time Peak discharge current within It means that under the SOC constraint, A sampling cycle starting at time The peak charging current within.

[0126] Step 4.3: Establish current constraints, which are the current limits set by the manufacturer during battery design. Indicates the peak discharge current designed for the battery; Indicates the peak charging current for which the battery is designed.

[0127] Step 4.4: Calculate the continuous peak charge and discharge current under voltage constraint, current constraint and SOC constraint. The specific calculation formula is:

[0128] (twenty four)

[0129] (25)

[0130] in, It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time Peak discharge current within It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time The peak charging current within.

[0131] Step 4.5: Calculate the continuous peak charge and discharge power according to the continuous peak charge and discharge current under the voltage constraint, current constraint and SOC constraint. The specific calculation formula is:

[0132] (26)

[0133] (27)

[0134] in, Indicates the A sampling cycle starting at time The peak discharge power within Indicates the A sampling cycle starting at time Peak charging power within.

[0135] For At each moment, the battery dynamic characteristics linear model parameters are first obtained according to the online parameter identification in step 2. , , , , , and then calculate the first The battery SOC value at the moment is calculated according to step 4. The moment to The continuous peak charge and discharge power within a time range is cycled sequentially to achieve real-time joint estimation of battery SOC and SOP.

[0136] In order to verify the effectiveness and practicality of the method of the present invention, the charge and discharge data of the INR18650-20R battery under the dynamic stress test (DST) condition in the open data set of the University of Maryland were selected for experimental verification. Figure 2 The terminal voltage prediction curve based on the battery dynamic characteristic linear model is shown. Figure 3 The terminal voltage prediction error (i.e., the difference between the predicted terminal voltage and the true terminal voltage) curve is shown in Figure 2 and Figure 3 It can be seen that the maximum value of the terminal voltage prediction error is less than 5.5 mV, indicating that the battery dynamic characteristic linear model constructed by the present invention has high accuracy and the battery terminal voltage can be accurately predicted using this model.

[0137] Figure 4 and Figure 5 The SOC and SOC prediction error curves estimated based on the adaptive Kalman filter algorithm are shown respectively. Except for the large SOC initial prediction error caused by the SOC initial value, the maximum values ​​of other SOC prediction errors are less than 1.2%, indicating that the method of the present invention can accurately predict the real-time SOC value of the battery.

[0138] Figure 6 and Figure 7 The continuous peak discharge and charging power curves are shown respectively, and 30s is the sampling period. Figure 6 and Figure 7 It can be seen that in most cases, the peak charging power of the battery is close to 20W, and the peak discharging power is close to 35W. It is worth noting that when the battery SOC is close to 0.13, the peak discharging power of the battery decreases rapidly to zero, which is helpful to avoid over-discharging of the battery. It can be inferred that accurate SOC estimation is crucial to ensure the accuracy of the battery SOP, which is essential to maintain the stability and safety of the battery.

[0139] Embodiment 2

[0140] An embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the battery SOC and SOP joint estimation method in the embodiment of the present application.

[0141] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) or the programs and / or data loaded from the storage part into the random access memory (RAM). The processor can be a multi-core processor or can include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM, various programs and data required for device operation are also stored. The processor, ROM, and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0142] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0143] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the battery SOC and SOP joint estimation method in the embodiment of the present application.

[0144] Readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0145] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the battery SOC and SOP joint estimation method in the embodiment of the present application.

[0146] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A battery SOC and SOP joint estimation method, characterized in that: The estimation method includes: Construct a linear model of battery dynamic characteristics; Performing online parameter identification on the battery dynamic characteristic linear model to obtain parameters of the battery dynamic characteristic linear model; Estimate the real-time SOC value of the battery according to the parameters of the battery dynamic characteristic linear model; Calculate the continuous peak charge and discharge power of the battery under multiple constraints according to the battery dynamic characteristic linear model and the real-time SOC value of the battery; Among them, the battery dynamic characteristic linear model is: ； in, Indicates The battery terminal voltage value at a moment, Indicates The battery terminal voltage value at a moment, Indicates The battery charge and discharge current value at each moment, Indicates The battery SOC value at a certain moment, , , and represents the polynomial coefficient, and m and n represent the order of regression terms.

2. The battery SOC and SOP joint estimation method according to claim 1, characterized in that: Before performing online parameter identification on the battery dynamic characteristic linear model, the estimation method further includes: The order of the regression term of the battery dynamic characteristic linear model is determined based on the Akaike information criterion.

3. The battery SOC and SOP joint estimation method according to claim 1, characterized in that: The recursive least square method with forgetting factor is used to perform online parameter identification on the battery dynamic characteristic linear model.

4. The battery SOC and SOP combined estimation method according to claim 1, characterized in that: Adaptive Kalman filter algorithm is used to estimate the real-time SOC value of the battery, including: According to the parameters of the linear model of the battery dynamic characteristics, the state space equation of the battery is constructed, which is specifically: ； ； in, Indicates The battery SOC value at a certain moment, Indicates The battery SOC value at a certain moment, is the Coulomb efficiency, Indicates the rated capacity of the battery. Indicates The battery charge and discharge current value at each moment, Indicates The battery charge and discharge current value at each moment, Indicates Zero-mean system noise at time instant, Indicates The battery terminal voltage value at a moment, Indicates The battery terminal voltage value at a moment, , , , , All are The polynomial coefficients of the linear model of the battery dynamic characteristics at each moment, Indicates Zero-mean measurement noise at each moment; The linear model parameters of the battery dynamic characteristics obtained by online parameter identification and the state space equation are used to calculate the The battery terminal voltage at the moment ; Calculate the prior estimate of the state of charge and covariance matrix. The specific calculation formula is: ； ； in, Indicates The battery SOC prior estimate at the moment, Indicates The battery SOC posterior estimate at the moment, Indicates The prior estimate of the covariance matrix at time instants, represents the identity matrix, Indicates The posterior estimate of the covariance matrix at time instant, the superscript T indicates transposition, Indicates The system noise covariance matrix at each moment; Calculate the The battery SOC posterior estimate at the moment and the The posterior estimate of the covariance matrix at the moment is The battery SOC posterior estimation value at the moment is used as the real-time SOC value of the battery; wherein the specific calculation formula is: ； ； ； ； ； ； ； in, Indicates The battery SOC posterior estimate at the moment, Indicates The Kalman gain matrix at the moment is: Indicates The innovation matrix at each moment is Indicates The posterior estimate of the covariance matrix at time instant, Indicates The terminal voltage measurement value at a moment, Indicates The measurement noise covariance at time instant is, Indicates The measurement noise covariance at time instant is, Indicates The system noise covariance matrix at time instant is: Indicates The new information real-time estimated covariance function is obtained by the window estimation principle at each moment, and M represents the window size.

5. The battery SOC and SOP combined estimation method according to any one of claims 1 to 4, characterized in that: The specific calculation process of the continuous peak charge and discharge power of the battery under the multiple constraints includes: Calculate the continuous peak charge and discharge current under voltage constraints. The specific calculation formula is: ； ； in, It means that under the voltage constraint, A sampling cycle starting at time Peak discharge current within , , , , They are all polynomial coefficients of the linear model of battery dynamic characteristics; Indicates the battery discharge cut-off voltage; Indicates The battery SOC value at a certain moment; represents the sampling interval, L represents the number of sampling times in a sampling period; represents the Coulomb efficiency; Indicates the rated capacity of the battery; It means that under the voltage constraint, A sampling cycle starting at time Peak charging current within Indicates the battery charging cut-off voltage; Calculate the continuous peak charge and discharge current under SOC constraint. The specific calculation formula is: ； ； in, It means that under the SOC constraint, A sampling cycle starting at time Peak discharge current within Indicates the minimum state of charge of the battery; It means that under the SOC constraint, A sampling cycle starting at time Peak charging current within Indicates the maximum state of charge of the battery; Calculate the continuous peak charge and discharge current under voltage constraints, current constraints, and SOC constraints. The specific calculation formula is: ； ； in, It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time Peak discharge current within It means that under the constraints of voltage, current and SOC, A sampling cycle starting at time Peak charging current within Indicates the peak discharge current designed for the battery; Indicates the peak charging current designed for the battery; The continuous peak charge and discharge power is calculated according to the continuous peak charge and discharge current under the voltage constraint, current constraint and SOC constraint. The specific calculation formula is: ； ； in, Indicates the A sampling cycle starting at time The peak discharge power within Indicates the A sampling cycle starting at time Peak charging power within.

6. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the battery SOC and SOP joint estimation method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the battery SOC and SOP joint estimation method as described in any one of claims 1 to 5 is implemented.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the battery SOC and SOP joint estimation method as described in any one of claims 1 to 5 is implemented.

Images