Battery model parameter identification method and device

Abstract

The invention provides a battery model parameter identification method and device, and the method comprises the steps: reading battery data in a specific time period from a cloud platform database, extracting a plurality of pieces of charging and discharging segment data according to each piece of battery data, and carrying out the discrete Fourier transform of each piece of charging and discharging segment data according to the descending order of timestamps, thereby generating a spectrogram. And determining a frequency interval to which each piece of charging and discharging fragment data belongs according to the spectrogram, and carrying out filtering processing to obtain target charging and discharging fragment data. And estimating charging and discharging parameters according to the target charging and discharging fragment data and a preset frequency interval. The scheme does not depend on a large-scale training set and long-time training, the battery model parameters can be obtained with high precision without a neural network algorithm, and it is ensured that the cloud BMS updates the parameters to the vehicle BMS in real time. The frequency analysis can reveal the parameter change of the battery in the whole life cycle, the fault diagnosis and safety early warning requirements under the long time scale are met, and the battery parameter identification method of the cloud BMS is enriched.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence big data technology, and in particular to a method and device for identifying battery model parameters. Background Art

[0002] As the core of the battery system, the Battery Management System (BMS) is widely used in various new energy vehicles and energy storage power plants. By establishing battery models and calculating model parameters, the BMS monitors the battery system status and ensures its safe operation. With the introduction of big data technology, BMS development has gradually entered the cloud stage, bringing innovations to battery model parameter identification algorithms.

[0003] At present, most of the application layer algorithms of cloud-based BMS for new energy vehicles use algorithms such as neural networks and machine learning that require pre-training. The accuracy of such algorithms is strongly related to the size and quality of the training set data set, which limits the coverage capability of the algorithm to a certain extent; secondly, these algorithms cover more fault diagnosis, safety warning and state estimation, and involve less parameter identification; in addition, the development of the field of artificial intelligence and big data will inevitably lead to more and more functions being added to the cloud-based BMS for new energy vehicles. Large-scale neural network training will consume a lot of cloud computing resources, which will have a certain impact on the real-time performance of the cloud-based BMS.

[0004] Therefore, how to improve the efficiency and real-time performance of the battery model parameter identification algorithm of the cloud-based BMS has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and apparatus for identifying battery model parameters to solve the current problem that battery model parameter identification is highly dependent on training data and has poor real-time performance.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of the present invention discloses a method for identifying battery model parameters, the method comprising:

[0008] Read multiple battery data within a preset time period from the cloud platform database;

[0009] If the plurality of battery data satisfy the identification requirements, a plurality of charge and discharge segment data are determined according to the voltage, current sign, charging flag state and timestamp in each of the battery data; each of the charge and discharge segment data corresponds to a target timestamp;

[0010] Performing discrete Fourier transform processing on all charge and discharge segment data in descending order of the target timestamps to obtain a frequency spectrum corresponding to each charge and discharge segment data;

[0011] Determining a preset frequency threshold interval to which each of the charge and discharge segment data belongs according to the frequency spectrum, and filtering all the charge and discharge segment data using a filter to obtain a plurality of target charge and discharge segment data;

[0012] If the preset frequency threshold interval to which any of the target charge and discharge segment data belongs is a low-frequency interval, marking the target charge and discharge segment data as first charge and discharge segment data, and calculating the battery ohmic internal resistance based on the first charge and discharge segment data and the battery n-order RC model state equation;

[0013] Multiple charging parameters and multiple discharging parameters are estimated based on the battery ohmic internal resistance and all second charge and discharge segment data; the preset frequency threshold interval to which the second charge and discharge segment data belongs is a non-low-frequency interval, and the target timestamp corresponding to the second charge and discharge segment data is greater than the target timestamp corresponding to the first charge and discharge segment data.

[0014] Preferably, if the plurality of battery data meet the identification requirements, before determining the plurality of charge and discharge segment data according to the voltage, current sign, charging flag state and timestamp in each of the battery data, the method further includes:

[0015] Querying whether there is first battery data in all battery data, wherein a charging flag in the first battery data is in an on state;

[0016] If the first battery data exists, query whether the second battery data exists in all the battery data; the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in the off state;

[0017] If the second battery data exists, it is determined that the plurality of pieces of battery data meet the identification requirement.

[0018] Preferably, the determining of a plurality of charge and discharge segment data according to the voltage, current sign, charging mark status and timestamp in each piece of the battery data includes:

[0019] Calculating the voltage change gradient direction according to the voltage and timestamp in each piece of battery data;

[0020] Determine a plurality of charge and discharge segment data by combining the current sign, the state of the charge flag and the voltage change gradient direction of each piece of the battery data; each of the charge and discharge segment data includes a plurality of the battery data;

[0021] For each of the charge and discharge segment data, a timestamp with the smallest value among the multiple battery data included in the charge and discharge segment data is marked as a target timestamp corresponding to the charge and discharge segment data.

[0022] Preferably, determining the preset frequency threshold interval to which each charge and discharge segment data belongs according to the frequency spectrum includes:

[0023] Obtain multiple preset frequency threshold intervals;

[0024] For each of the charge and discharge segment data, obtaining a frequency spectrum of the charge and discharge segment data; the frequency spectrum includes multiple frequency components;

[0025] For each of the preset frequency threshold intervals, calculating the number of frequency components in the frequency spectrum that are located in the preset frequency threshold interval, and calculating the ratio between the number and the total number of frequency components in the frequency spectrum;

[0026] If the ratio is greater than the preset proportion threshold, it is determined that the charge and discharge segment data belongs to the preset frequency threshold interval, and the preset frequency threshold interval to which each charge and discharge segment data belongs is obtained.

[0027] Preferably, the calculating of the battery ohmic internal resistance according to the first charge and discharge segment data and the battery n-order RC model state equation includes:

[0028] Obtain the state equation of the battery n-order RC model;

[0029] The value of n is set to 1, and the state equation of the battery n-order RC model is simplified to obtain a pure resistance model state equation;

[0030] The ohmic internal resistance of the battery is calculated based on the pure resistance model state equation and the first charge and discharge segment data.

[0031] A second aspect of the present invention discloses a device for identifying battery model parameters, the device comprising:

[0032] A reading unit, used to read multiple pieces of battery data within a preset time period from a cloud platform database;

[0033] a first determining unit, configured to determine, if the plurality of battery data satisfy the identification requirement, a plurality of charge and discharge segment data according to the voltage, current sign, charge flag state, and timestamp in each piece of battery data; each charge and discharge segment data corresponds to a target timestamp;

[0034] a discrete Fourier transform processing unit, configured to perform discrete Fourier transform processing on all charge and discharge segment data in descending order of the target timestamps, to obtain a frequency spectrum corresponding to each charge and discharge segment data;

[0035] a filtering processing unit, configured to determine, based on the frequency spectrum, a preset frequency threshold interval to which each of the charge and discharge segment data belongs, and to filter all of the charge and discharge segment data using a filter to obtain a plurality of target charge and discharge segment data;

[0036] a calculation unit, configured to mark the target charge and discharge segment data as first charge and discharge segment data if the preset frequency threshold interval to which any target charge and discharge segment data belongs is a low-frequency interval, and calculate the battery ohmic internal resistance based on the first charge and discharge segment data and a battery n-order RC model state equation;

[0037] an estimating unit, configured to estimate a plurality of charging parameters and a plurality of discharging parameters based on the battery ohmic internal resistance and all second charge and discharge segment data; wherein the preset frequency threshold interval to which the second charge and discharge segment data belongs is a non-low-frequency interval, and a target timestamp corresponding to the second charge and discharge segment data is greater than a target timestamp corresponding to the first charge and discharge segment data.

[0038] Preferably, the device further comprises:

[0039] a first query unit, configured to query whether first battery data exists in all battery data, wherein a charging flag in the first battery data is in an on state;

[0040] a second query unit, configured to query whether second battery data exists in all battery data if the first battery data exists; the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in an off state;

[0041] The second determining unit is configured to determine whether the plurality of pieces of second battery data meet the identification requirement if the second battery data exists.

[0042] Preferably, the first determining unit includes:

[0043] a calculation module, configured to calculate a voltage change gradient direction based on the voltage and timestamp in each piece of battery data;

[0044] a determination module, configured to determine a plurality of charge and discharge segment data by combining the current sign, the state of the charge flag, and the voltage change gradient direction of each piece of the battery data; each of the charge and discharge segment data includes a plurality of pieces of the battery data;

[0045] The marking module is configured to mark, for each of the charge and discharge segment data, a timestamp with the smallest value among the multiple battery data included in the charge and discharge segment data as a target timestamp corresponding to the charge and discharge segment data.

[0046] Preferably, the filtering processing unit is specifically used to:

[0047] Acquire a plurality of preset frequency threshold intervals; for each of the charge and discharge segment data, acquire a frequency spectrum of the charge and discharge segment data; the frequency spectrum includes a plurality of frequency components; for each of the preset frequency threshold intervals, calculate the number of frequency components in the frequency spectrum that are within the preset frequency threshold interval, and calculate a ratio between the number and the total number of frequency components in the frequency spectrum;

[0048] If the ratio is greater than the preset proportion threshold, it is determined that the charge and discharge segment data belongs to the preset frequency threshold interval, and the preset frequency threshold interval to which each charge and discharge segment data belongs is obtained.

[0049] Preferably, the computing unit is specifically configured to:

[0050] Obtain a battery n-order RC model state equation; set the value of n to 1, simplify the battery n-order RC model state equation to obtain a pure resistance model state equation; and calculate the battery ohmic internal resistance based on the pure resistance model state equation and the first charge and discharge segment data.

[0051] Based on the above-mentioned embodiment of the present invention, a method and device for identifying battery model parameters are provided. Battery data within a specific time period is read from the cloud platform database, multiple charge and discharge segment data are extracted based on each battery data, and each charge and discharge segment data is arranged in descending order by timestamp and discrete Fourier transform is performed to generate a spectrum diagram. The frequency interval to which each charge and discharge segment data belongs is determined based on the spectrum diagram, and filtering is performed to obtain the target charge and discharge segment data. The charging and discharging parameters are estimated based on the target charge and discharge segment data and the preset frequency interval. This solution does not rely on large-scale training sets and long-term training. It can obtain battery model parameters with high precision without the need for neural network algorithms, ensuring that the cloud-side BMS updates parameters to the vehicle-side BMS in real time. Frequency analysis can reveal the parameter changes of the battery throughout its life cycle, meet the needs of fault diagnosis and safety warning on a long time scale, and enrich the battery parameter identification method of the cloud-side BMS. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0053] Figure 1 A flow chart of a method for identifying battery model parameters provided by an embodiment of the present invention;

[0054] Figure 2 A flowchart for determining multiple charge and discharge segment data provided by an embodiment of the present invention;

[0055] Figure 3 A first schematic diagram of a method for identifying battery model parameters provided by an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of converting the charge and discharge segment data from the time domain to the frequency domain according to an embodiment of the present invention;

[0057] Figure 5 A schematic diagram of a method for determining the preset frequency threshold interval to which each charge and discharge segment data belongs, provided by an embodiment of the present invention;

[0058] Figure 6 A flowchart of determining a preset frequency threshold interval to which each charge and discharge segment data belongs based on a frequency spectrum provided by an embodiment of the present invention;

[0059] Figure 7 A schematic diagram of the process for calculating the ohmic internal resistance of a battery according to an embodiment of the present invention;

[0060] Figure 8 A schematic diagram of an n-order RC model of a battery provided by an embodiment of the present invention;

[0061] Figure 9 This is a structural block diagram of a battery model parameter identification device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0063] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0064] As we've seen in the background, the application-layer algorithms of cloud-based BMSs for new energy vehicles have several issues, including a reliance on high-quality training data, a lack of effective battery parameter identification methods, real-time issues resulting from high cloud computing resource requirements, and poor interpretability of deep learning models. These issues can impact the accuracy, responsiveness, and maintenance efficiency of battery management systems, and require improvements through algorithm optimization, data quality enhancement, and model interpretability.

[0065] Therefore, an embodiment of the present invention provides a method and device for identifying battery model parameters, which reads battery data within a specific time period from a cloud platform database, extracts multiple charge and discharge segment data based on each battery data, and performs discrete Fourier transform on each charge and discharge segment data in descending order according to timestamps to generate a spectrum diagram. The frequency interval to which each charge and discharge segment data belongs is determined based on the spectrum diagram, and filtering is performed to obtain the target charge and discharge segment data. The charging and discharging parameters are estimated based on the target charge and discharge segment data and the preset frequency interval. This solution does not rely on large-scale training sets and long-term training, and can obtain battery model parameters with high precision without the need for neural network algorithms, reducing cloud computing power requirements and ensuring that the cloud BMS updates parameters to the vehicle-side BMS in real time. In addition, frequency analysis can reveal the parameter changes of the battery throughout its life cycle, meet the fault diagnosis and safety warning requirements on a long time scale, and enrich the battery parameter identification method of the cloud BMS.

[0066] See also Figure 1 , shows a flow chart of a battery model parameter identification method provided by an embodiment of the present invention, the method comprising:

[0067] Step S101: Read multiple pieces of battery data within a preset time period from a cloud platform database.

[0068] It should be noted that the cloud platform database includes multiple pieces of real vehicle operation data.

[0069] In the specific implementation of step S101, multiple pieces of battery data within a preset time period are read from multiple pieces of actual vehicle operation data in the cloud platform database, wherein each piece of battery data includes but is not limited to voltage data, current data, charging flag, and timestamp.

[0070] For example, read multiple pieces of battery data for 24 hours from multiple pieces of real vehicle operation data in the cloud platform database.

[0071] It's understandable that collecting 24 hours of battery data for parameter identification covers battery performance under different operating conditions, such as charge and discharge processes and temperature fluctuations. This helps capture changes in the battery's state over extended use, improving the accuracy and reliability of the model. Long-term data collection can identify potential battery issues under different operating conditions, such as the effects of temperature fluctuations or prolonged high loads, thereby enhancing the stability of parameter identification results and supporting battery management system optimization.

[0072] It should be noted that the collected multiple pieces of battery data must first be evaluated to see if they meet the identification criteria to ensure they effectively reflect the battery's true state, thereby providing a reliable basis for subsequent parameter identification. Based on this, referring to Processes A1 to A3 shown in the embodiments of the present invention, the specific details of evaluating whether multiple pieces of battery data meet the identification criteria are explained in detail.

[0073] Process A1: Query whether the first battery data exists in all battery data.

[0074] It should be noted that the charging flag in the first battery data is in an on state.

[0075] When specifically implementing process A1, it is checked whether there is first battery data with a charging flag turned on in all battery data; if the first battery data exists, process A2 is executed; if the first battery data does not exist, the process ends.

[0076] Process A2: If the first battery data exists, query all battery data to see whether the second battery data exists.

[0077] It should be noted that the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in the off state.

[0078] When specifically implementing process A2, if the first battery data exists, then query whether the second battery data exists in all battery data; if the second battery data exists, execute process A3; if the second battery data does not exist, then end the process.

[0079] Process A3: If the second battery data exists, determine whether the plurality of battery data satisfy the identification requirement.

[0080] That is, when the multiple pieces of battery data include complete charging process data, it is determined that the multiple pieces of battery data meet the identification requirement.

[0081] It is understandable that the complete charging process data can provide full cycle data of the battery from low power to full charge, covering different working states of the battery, thereby improving the accuracy and reliability of parameter identification.

[0082] Step S102: If the plurality of battery data satisfy the identification requirement, a plurality of charge and discharge segment data are determined according to the voltage, current sign, charging flag state and timestamp in each battery data.

[0083] In the specific implementation of step S102, when multiple pieces of battery data read from the cloud platform database meet the identification requirements, multiple charge and discharge segment data are determined from all the battery data based on the voltage, current sign, charging flag status, and timestamp in each piece of battery data. The multiple charge and discharge segment data specifically include multiple charging segment data and multiple discharging segment data.

[0084] It is understood that the specific process of determining multiple charge and discharge segment data is detailed in the embodiment of the present invention. Figure 2 The content in Figure 2 include:

[0085] Step S201: Calculate the voltage change gradient direction based on the voltage and timestamp in each battery data.

[0086] In the specific implementation of step S201 , the voltage variation of all battery data over time is calculated based on the voltage and timestamp in each battery data, so as to determine the voltage variation gradient direction based on the sign of the voltage variation.

[0087] Step S202: Determine a plurality of charge and discharge segment data by combining the current sign, the state of the charge flag, and the voltage change gradient direction of each piece of battery data.

[0088] It is understandable that the charging current and the discharging current defined in each piece of battery data have different signs, for example, the sign of the current during charging is negative, and the sign of the current during discharging is positive.

[0089] In the specific implementation of step S202, according to the current sign, the state of the charging flag and the voltage change gradient direction of each battery data, that is, as the time indicated by the timestamp changes, the voltage increases or decreases, the current is charging or discharging, and the state of the charging flag is on, multiple charging segment data (such as B1, B2, ..., B i ,…,B n ) and multiple discharge segment data (such as C1, C2, ..., C i ,…,C n ).

[0090] It should be noted that a plurality of charge segment data and a plurality of discharge segment data are expressed as a plurality of charge and discharge segment data.

[0091] It is understandable that each charge and discharge segment data includes multiple pieces of battery data, that is, each charge and discharge segment data is battery data lasting for a period of time.

[0092] Step S203: for each charge and discharge segment data, mark the timestamp with the smallest value among the multiple battery data included in the charge and discharge segment data as the target timestamp corresponding to the charge and discharge segment data.

[0093] In the specific implementation of step S203 , for each charge and discharge segment data, the timestamp corresponding to the first battery data piece (ie, the timestamp with the smallest value) among the multiple battery data pieces contained in the charge and discharge segment data is marked as the target timestamp corresponding to the charge and discharge segment data.

[0094] That is to say, each charging and discharging segment data corresponds to a target timestamp.

[0095] It is understandable that determining multiple charge and discharge segment data from multiple battery data can improve identification accuracy, achieve real-time and online identification, reduce computing resource consumption, and improve identification accuracy.

[0096] Step S103: performing discrete Fourier transform processing on all charge and discharge segment data in descending order of target timestamps to obtain a frequency spectrum corresponding to each charge and discharge segment data.

[0097] In the specific implementation of step S103, all charge and discharge segment data are input into the data storage system according to the order of target timestamps from large to small. Figure 3 In the actuator 1 shown, discrete Fourier transform processing is performed to analyze the frequency of each charge and discharge segment data to obtain a frequency spectrum corresponding to each charge and discharge segment data.

[0098] It should be noted that, in the embodiment of the present invention Figure 3 In the actuator 1 shown, discrete Fourier transform is used to transform the data of each charge and discharge segment from the time domain to the frequency domain (for example, in the embodiment of the present invention Figure 4 shown).

[0099] For example: All the charging segment data (B1, B2, ..., B i ,…,B n ) According to the order of target timestamp from large to small, input into the embodiment of the present invention Figure 3 In the actuator 1 shown in the figure, the spectrum diagram corresponding to each charging segment data (BSpe1, BSpe2, ..., BSpe n ).

[0100] Another example: the discharge segment data (C1, C2, ..., C i ,…,C n ) According to the order of target timestamp from large to small, input into the embodiment of the present invention Figure 3 In the actuator 1 shown in FIG, the spectrum diagram corresponding to each charging segment data (CSpe1, CSpe2, ..., CSpe n ).

[0101] In practical applications, frequency analysis can also be performed on the data of each charge and discharge segment through other transformation methods. The discrete Fourier transform proposed in the embodiment of the present invention is only used as an example.

[0102] Step S104: determining the preset frequency threshold interval to which each charge and discharge segment data belongs according to the frequency spectrum, and performing filtering processing on all charge and discharge segment data using a filter to obtain a plurality of target charge and discharge segment data.

[0103] In combination with the embodiment of the present invention Figure 5 The content shown is based on the frequency spectrum corresponding to each charge and discharge segment data, multiple preset frequency threshold intervals and data frequency division criteria, to determine the preset frequency threshold interval to which each charge and discharge segment data belongs, and to use a filter to filter all charge and discharge segment data to obtain multiple target charge and discharge segment data.

[0104] It can be understood that a filter (such as a high-pass filter, a low-pass filter, a band-pass filter, a band-stop filter, etc.) is used to filter all charge and discharge segment data belonging to each preset frequency threshold interval, so that only the frequency components of each charge and discharge segment data that meet the preset frequency threshold interval are retained, and other frequency components that do not meet the preset frequency threshold interval are filtered out, thereby obtaining multiple target charge and discharge segment data.

[0105] That is, the plurality of target charge and discharge segment data include a plurality of target discharge segment data Cf1, Cf2, ..., Cf n and multiple target charging segment data Bf1, Bf2, ..., Bf n .

[0106] It should be noted that each target charge and discharge segment data corresponds to a preset frequency threshold interval to which each charge and discharge segment data belongs.

[0107] It is understood that the specific process of determining the preset frequency threshold interval to which each charge and discharge segment data belongs according to the spectrum diagram corresponding to each charge and discharge segment data, multiple preset frequency threshold intervals and data frequency division criteria is detailed in the embodiments of the present invention. Figure 6 The content in Figure 6 include:

[0108] It is understandable that the data frequency division criteria are as follows: setting the preset ratio threshold of the spectrum graph as Ratio, the spectrum graph (BSpe i or CSpe i ) in a certain frequency threshold interval accounts for the total number of frequency components ≥Ratio, the spectrum diagram (BSpe i or CSpe i ) corresponding charge and discharge segment data B i (or C i ) is within the frequency threshold range.

[0109] Step S601: Acquire multiple preset frequency threshold intervals.

[0110] It should be noted that the multiple preset frequency threshold intervals, such as [0, ω1], (ω1, ω2], (ω2, ω3], ..., (ω m ,+∞).

[0111] It should be noted that [0,ω1] is defined as a low-frequency interval.

[0112] Step S602: For each charge and discharge segment data, obtain a frequency spectrum diagram of the charge and discharge segment data.

[0113] It can be understood that steps S602 to S605 are performed for each charge and discharge segment data, that is, each charge segment data and each discharge segment data.

[0114] It should be noted that the frequency spectrum of each charge and discharge segment data includes multiple frequency components.

[0115] Step S603: for each preset frequency threshold interval, calculating the number of frequency components in the frequency spectrum that are within the preset frequency threshold interval, and calculating the ratio between the number and the total number of frequency components in the frequency spectrum.

[0116] It can be understood that for each preset frequency threshold interval [0, ω1], (ω1, ω2], (ω2, ω3], ..., (ω m ,+∞), calculate the number of frequency components in the frequency spectrum of each charging segment data that are located in the preset frequency threshold interval, and calculate the ratio between the number and the total number of frequency components in the frequency spectrum of the current charging segment data.

[0117] It should be noted that it is determined whether the ratio is greater than a preset ratio threshold Ratio; if the ratio is greater than the preset ratio threshold Ratio, step S604 is executed.

[0118] Step S604: If the ratio is greater than the preset proportion threshold, it is determined that the charge and discharge segment data belongs to the preset frequency threshold interval, and the preset frequency threshold interval to which each charge and discharge segment data belongs is obtained.

[0119] In the specific implementation of step S604 , when the ratio is greater than the preset proportion threshold Ratio, it is determined that the current charge and discharge segment data belongs to the current preset frequency threshold interval, and finally the preset frequency threshold interval to which each charge and discharge segment data belongs is obtained.

[0120] Step S105: If the preset frequency threshold interval to which any target charge and discharge segment data belongs is a low-frequency interval, the target charge and discharge segment data is marked as first charge and discharge segment data, and the battery ohmic internal resistance is calculated based on the first charge and discharge segment data and the battery n-order RC model state equation;

[0121] It is understandable that [0, ω1] is predefined as a low frequency interval. Figure 3 The content shown is that the battery model parameter identification and battery model parameter classification are performed using the actuator 2, which will be explained in detail below in conjunction with the content in step S105 and step S106.

[0122] In combination with the embodiment of the present invention Figure 7 The content shown in the figure shows that there are two ways to calculate the battery ohmic internal resistance: one is that if the preset frequency threshold interval to which any target discharge segment data belongs is a low-frequency interval, the target discharge segment data is marked as the first discharge segment data, and the battery ohmic internal resistance is calculated based on the first discharge segment data and the battery n-order RC model state equation. The other is that if the preset frequency threshold interval to which any target charge segment data belongs is a low-frequency interval, the target charge segment data is marked as the first charge segment data, and the battery ohmic internal resistance is calculated based on the first charge segment data and the battery n-order RC model state equation.

[0123] It can be understood that the state equation of the battery n-order RC model is simplified, and the battery ohmic internal resistance is calculated using a parameter identification algorithm (including but not limited to Kalman filtering, recursive least squares, target optimization, etc.).

[0124] It should be noted that the specific method of simplifying the state equation of the battery n-order RC model and calculating the battery ohmic internal resistance is detailed in process B1 to process B3 shown in the embodiment of the present invention.

[0125] Process B1: Obtain the state equation of the battery n-order RC model.

[0126] It should be noted that the state equation of the battery n-order RC model (i.e., the state equation of the battery n-RC model) is shown in Formula (1).

[0127] (1)

[0128] Where i represents the serial number of the RC network (i=1,2,3,…,n), R p Represents the polarization internal resistance of the RC network, C p represents the polarization voltage in the RC network; t represents time, represents the polarization voltage, R p,i Represents polarization resistance, C p,i represents polarization capacitance, I(t) represents current, U(t) represents terminal voltage, OCV(SOC(t)) represents open circuit voltage (which is a function of SOC), and R0 represents the battery ohmic internal resistance.

[0129] Process B2: Set the value of n to 1, simplify the battery n-order RC model state equation, and obtain the pure resistance model state equation.

[0130] It is understandable that, in combination with the embodiments of the present invention Figure 8 As shown in Figure 2, under low-frequency excitation, the capacitor elements of the RC network are considered to be approximately short-circuited, and the 1-RC model can be simplified to a pure resistance model. Therefore, the simplified battery 1-RC model state equation (i.e., the pure resistance model state equation) is shown in Formula (2).

[0131] (2)

[0132] Among them, Q max Indicates the maximum available capacity of the battery, SOC(t) indicates the SOC value at time t, and SOC(0) indicates the SOC value at the start time (i.e., the SOC value at time 0).

[0133] Process B3: Based on the pure resistance model state equation and the first charge and discharge segment data, the battery ohmic internal resistance is calculated.

[0134] In the specific implementation process B3, the battery ohmic internal resistance R0 is calculated based on the pure resistance model state equation shown in formula (2) and the first charge and discharge segment data.

[0135] Step S106: estimating charging parameters and discharging parameters based on the battery ohmic internal resistance and all second charge and discharge segment data.

[0136] It should be noted that the preset frequency threshold interval to which the second charge and discharge segment data belongs is a non-low frequency interval, and the target timestamp corresponding to the second charge and discharge segment data is greater than the target timestamp of the first charge and discharge segment data.

[0137] In the specific implementation of step S106, the battery ohmic internal resistance R0 is regarded as a known quantity, and a parameter identification algorithm (including but not limited to Kalman filtering, recursive least squares, and target optimization algorithms) is used. According to the different responsiveness of each RC network in the n-RC model to each frequency interval, all the second charge and discharge segment data are combined to estimate the RC network parameters with high responsiveness to a certain frequency interval in the battery model, thereby obtaining multiple charging parameters and multiple discharging parameters.

[0138] In some specific embodiments, see Figure 3 and Figure 7 As shown in the content, the embodiment of the present invention can also store multiple charging parameters and multiple discharging parameters in the cloud platform database.

[0139] In an embodiment of the present invention, this solution obtains battery charge and discharge data within a specific time period from the cloud platform database through the cloud BMS, extracts and arranges each charge and discharge segment in chronological order, and adds a timestamp to each data segment. Then, these data are converted from the time domain to the frequency domain, a frequency spectrum is generated, and the data is divided according to the set frequency interval. Compared with the existing cloud BMS algorithm, this solution does not rely on large-scale, high-quality training sets and long-term training. It can obtain battery model parameters with high precision without the need for a neural network algorithm, reduces the cloud computing power requirements, and ensures that the cloud BMS updates parameters to the vehicle-side BMS in real time. At the same time, frequency analysis can reveal the parameter changes of the battery throughout its life cycle, meet the fault diagnosis and safety warning needs on a long time scale, and enrich the battery parameter identification method of the cloud BMS.

[0140] Corresponding to the battery model parameter identification method proposed in the above embodiment of the present invention, see Figure 9 , which shows a structural block diagram of a battery model parameter identification device provided by an embodiment of the present invention. The device includes: a reading unit 901, a first determination unit 902, a discrete Fourier transform processing unit 903, a filtering processing unit 904, a calculation unit 905, and an estimation unit 906.

[0141] The reading unit 901 is used to read multiple pieces of battery data within a preset time period from the cloud platform database.

[0142] The first determining unit 902 is configured to determine a plurality of charge and discharge segment data according to the voltage, current sign, charge flag state and timestamp in each battery data if the plurality of battery data satisfy the identification requirements; each charge and discharge segment data corresponds to a target timestamp.

[0143] The discrete Fourier transform processing unit 903 is configured to perform discrete Fourier transform processing on all charge and discharge segment data in descending order of target timestamps to obtain a frequency spectrum corresponding to each charge and discharge segment data.

[0144] The filtering processing unit 904 is configured to determine the preset frequency threshold interval to which each charge and discharge segment data belongs according to the frequency spectrum diagram, and perform filtering processing on all charge and discharge segment data using a filter to obtain a plurality of target charge and discharge segment data.

[0145] The filtering processing unit 904 is specifically configured to: obtain multiple preset frequency threshold intervals; for each charge / discharge segment data, obtain a frequency spectrum of the charge / discharge segment data; the frequency spectrum includes multiple frequency components; and for each preset frequency threshold interval, calculate the number of frequency components in the frequency spectrum that fall within the preset frequency threshold interval, and calculate the ratio of the number of frequency components to the total number of frequency components in the frequency spectrum. If the ratio is greater than a preset ratio threshold, the charge / discharge segment data is determined to fall within the preset frequency threshold interval, and the preset frequency threshold interval to which each charge / discharge segment data falls is obtained.

[0146] The calculation unit 905 is configured to mark any target charge and discharge segment data as first charge and discharge segment data if the preset frequency threshold interval to which any target charge and discharge segment data belongs is a low-frequency interval, and calculate the battery ohmic internal resistance based on the first charge and discharge segment data and the battery n-order RC model state equation.

[0147] The calculation unit 905 is specifically used to: obtain the battery n-order RC model state equation; set the value of n to 1, simplify the battery n-order RC model state equation to obtain a pure resistance model state equation; based on the pure resistance model state equation and the first charge and discharge segment data, calculate the battery ohmic internal resistance.

[0148] an estimating unit 906 for estimating a plurality of charging parameters and a plurality of discharging parameters based on the battery ohmic internal resistance and all second charge and discharge segment data; the preset frequency threshold interval to which the second charge and discharge segment data belongs is a non-low-frequency interval, and the target timestamp corresponding to the second charge and discharge segment data is greater than the target timestamp corresponding to the first charge and discharge segment data.

[0149] In an embodiment of the present invention, this solution obtains battery charge and discharge data within a specific time period from the cloud platform database through the cloud BMS, extracts and arranges each charge and discharge segment in chronological order, and adds a timestamp to each data segment. Then, these data are converted from the time domain to the frequency domain, a frequency spectrum is generated, and the data is divided according to the set frequency interval. Compared with the existing cloud BMS algorithm, this solution does not rely on large-scale, high-quality training sets and long-term training. It can obtain battery model parameters with high precision without the need for a neural network algorithm, reduces the cloud computing power requirements, and ensures that the cloud BMS updates parameters to the vehicle-side BMS in real time. At the same time, frequency analysis can reveal the parameter changes of the battery throughout its life cycle, meet the fault diagnosis and safety warning needs on a long time scale, and enrich the battery parameter identification method of the cloud BMS.

[0150] Combine Figure 9 The content shown, the device also includes: a first query unit, a second query unit and a second determination unit.

[0151] The first query unit is configured to query whether first battery data exists in all battery data, wherein a charging flag in the first battery data is in an on state.

[0152] The second query unit is used to query whether there is second battery data in all battery data if the first battery data exists; the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in the off state.

[0153] The second determining unit is configured to determine whether the plurality of battery data satisfy the identification requirement if the second battery data exists.

[0154] Combine Figure 9 As shown in the content, the first determining unit 902 includes: a calculation module, a determination module and a marking module.

[0155] The calculation module is used to calculate the voltage change gradient direction based on the voltage and timestamp in each battery data.

[0156] The determination module is used to determine multiple charge and discharge segment data by combining the current sign, the state of the charge mark and the voltage change gradient direction of each battery data; each charge and discharge segment data includes multiple battery data.

[0157] The marking module is used to mark, for each charge and discharge segment data, the timestamp with the smallest value among the multiple battery data contained in the charge and discharge segment data as the target timestamp corresponding to the charge and discharge segment data.

[0158] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0159] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0160] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying battery model parameters, characterized in that: The method comprises: Read multiple pieces of battery data within a preset time from the cloud platform database; If the plurality of battery data satisfy the identification requirements, a plurality of charge and discharge segment data are determined according to the voltage, current sign, state of the charge mark and timestamp in each of the battery data; each of the charge and discharge segment data corresponds to a target timestamp; According to the descending order of the target timestamps, discrete Fourier transform processing is performed on all the charge and discharge segment data to obtain a frequency spectrum corresponding to each of the charge and discharge segment data; Determine the preset frequency threshold interval to which each of the charge and discharge segment data belongs according to the frequency spectrum, and use a filter to filter all the charge and discharge segment data to obtain a plurality of target charge and discharge segment data; If the preset frequency threshold interval to which any of the target charge and discharge segment data belongs is a low frequency interval, the target charge and discharge segment data is marked as the first charge and discharge segment data, and the battery ohmic internal resistance is calculated according to the first charge and discharge segment data and the battery n-order RC model state equation; A plurality of charging parameters and a plurality of discharging parameters are estimated based on the battery ohmic internal resistance and all the second charging and discharging segment data; the preset frequency threshold interval to which the second charging and discharging segment data belongs is a non-low-frequency interval, and the target timestamp corresponding to the second charging and discharging segment data is greater than the target timestamp corresponding to the first charging and discharging segment data.

2. The method according to claim 1, characterized in that If the plurality of battery data meet the identification requirements, before determining the plurality of charge and discharge segment data according to the voltage, current sign, charging mark state and timestamp in each of the battery data, the method further includes: Querying whether there is first battery data in all battery data, wherein a charging flag in the first battery data is in an on state; If the first battery data exists, query whether the second battery data exists in all the battery data; the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in the off state; If the second battery data exists, it is determined that the plurality of pieces of the battery data meet the identification requirement.

3. The method according to claim 1, characterized in that Determining a plurality of charge and discharge segment data according to the voltage, current sign, state of the charge mark and timestamp in each piece of the battery data includes: Calculate the voltage change gradient direction according to the voltage and timestamp in each piece of battery data; Determine a plurality of charge and discharge segment data by combining the current sign, the state of the charge mark and the voltage change gradient direction of each piece of the battery data; each of the charge and discharge segment data includes a plurality of the battery data; For each of the charge and discharge segment data, a timestamp with the smallest value among the multiple battery data included in the charge and discharge segment data is marked as a target timestamp corresponding to the charge and discharge segment data.

4. The method according to claim 1, characterized in that: The step of determining the preset frequency threshold interval to which each of the charge and discharge segment data belongs according to the frequency spectrum diagram includes: Obtain multiple preset frequency threshold intervals; For each of the charge and discharge segment data, a frequency spectrum of the charge and discharge segment data is obtained; the frequency spectrum includes a plurality of frequency components; For each of the preset frequency threshold intervals, calculating the number of frequency components in the frequency spectrum that are located in the preset frequency threshold interval, and calculating the ratio between the number and the total number of frequency components in the frequency spectrum; If the ratio is greater than the preset proportion threshold, it is determined that the charge and discharge segment data belongs to the preset frequency threshold interval, and the preset frequency threshold interval to which each of the charge and discharge segment data belongs is obtained.

5. The method according to claim 1, characterized in that The calculating the battery ohmic internal resistance according to the first charge and discharge segment data and the battery n-order RC model state equation includes: Obtain the state equation of the battery n-order RC model; The value of n is set to 1, and the state equation of the battery n-order RC model is simplified to obtain a pure resistance model state equation; The ohmic internal resistance of the battery is calculated based on the pure resistance model state equation and the first charge and discharge segment data.

6. A battery model parameter identification device, characterized in that: The device comprises: A reading unit, used to read multiple pieces of battery data within a preset time period from a cloud platform database; A first determination unit is used to determine a plurality of charge and discharge segment data according to the voltage, current sign, charging mark state and timestamp in each of the battery data if the plurality of battery data meet the identification requirements; each of the charge and discharge segment data corresponds to a target timestamp; A discrete Fourier transform processing unit, configured to perform discrete Fourier transform processing on all charge and discharge segment data in descending order of the target timestamps, to obtain a frequency spectrum corresponding to each charge and discharge segment data; A filtering processing unit, used to determine the preset frequency threshold interval to which each of the charge and discharge segment data belongs according to the frequency spectrum, and to filter all the charge and discharge segment data using a filter to obtain a plurality of target charge and discharge segment data; a calculation unit, configured to mark the target charge and discharge segment data as first charge and discharge segment data if the preset frequency threshold interval to which any of the target charge and discharge segment data belongs is a low-frequency interval, and calculate the battery ohmic internal resistance according to the first charge and discharge segment data and the battery n-order RC model state equation; an estimating unit, for estimating a plurality of charging parameters and a plurality of discharging parameters based on the battery ohmic internal resistance and all second charging and discharging segment data; the preset frequency threshold interval to which the second charging and discharging segment data belongs is a non-low-frequency interval, and the target timestamp corresponding to the second charging and discharging segment data is greater than the target timestamp corresponding to the first charging and discharging segment data.

7. The device according to claim 6, characterized in that The device also includes: A first query unit, used for querying whether there is first battery data in all battery data, wherein a charging flag in the first battery data is in an on state; A second query unit is used to query whether there is second battery data in all battery data if the first battery data exists; the timestamp carried by the second battery data is greater than the timestamp carried by the first battery data, and the charging flag in the second battery data is in an off state; The second determining unit is configured to determine, if the second battery data exists, whether the plurality of battery data satisfy the identification requirement.

8. The device according to claim 6, characterized in that The first determining unit includes: A calculation module, used for calculating the voltage change gradient direction according to the voltage and timestamp in each piece of battery data; A determination module, configured to determine a plurality of charge and discharge segment data by combining the current sign, the state of the charge mark and the voltage change gradient direction of each piece of the battery data; each of the charge and discharge segment data includes a plurality of pieces of the battery data; The marking module is used to mark, for each of the charge and discharge segment data, a timestamp with the smallest value among the multiple battery data included in the charge and discharge segment data as a target timestamp corresponding to the charge and discharge segment data.

9. The device according to claim 6, characterized in that The filtering processing unit is specifically used for: Acquire multiple preset frequency threshold intervals; for each of the charge and discharge segment data, acquire a spectrum diagram of the charge and discharge segment data; the spectrum diagram includes multiple frequency components; for each of the preset frequency threshold intervals, calculate the number of frequency components in the spectrum diagram that are located in the preset frequency threshold interval, and calculate the ratio between the number and the total number of frequency components in the spectrum diagram; If the ratio is greater than the preset proportion threshold, it is determined that the charge and discharge segment data belongs to the preset frequency threshold interval, and the preset frequency threshold interval to which each of the charge and discharge segment data belongs is obtained.

10. The device according to claim 6, characterized in that The computing unit is specifically used for: Obtain a battery n-order RC model state equation; set the value of n to 1, simplify the battery n-order RC model state equation to obtain a pure resistance model state equation; calculate the battery ohmic internal resistance based on the pure resistance model state equation and the first charge and discharge segment data.

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