Battery parameter identification method, identification device and identification equipment

Abstract

The invention provides a battery parameter identification method, device and equipment, and relates to the technical field of batteries, and the battery parameter identification method comprises the steps: obtaining a target electrical parameter of a to-be-detected battery; identifying the target electrical parameter based on a parameter identification algorithm, and generating a corresponding parameter identification result; and displaying the parameter identification result to a display interface. According to the method, the target electrical parameters of the to-be-detected battery are obtained, the target electrical parameters are key indexes such as voltage, current, resistance and capacity, and then the target electrical parameters are identified based on the parameter identification algorithm, so that the performance characteristics of the battery can be effectively identified, and the corresponding parameter identification result is generated; and finally, the parameter identification result is displayed on a display interface, so that the accuracy of battery parameter identification can be improved to a greater extent, the problems of precision reduction and zero drift in the battery identification process are reduced, and the battery parameter identification is more accurate and reliable.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery parameter identification method, an identification device, and an identification equipment. Background Art

[0002] Existing battery parameter identification sensors often experience problems such as decreased accuracy and zero point drift over time, resulting in errors in the collected battery data, which in turn affects the accuracy of parameter identification. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method for identifying battery parameters, aiming to improve the accuracy of battery parameter identification.

[0004] To achieve the above object, the present invention provides a battery parameter identification method, which is applied to an identification device. The identification method includes: Obtain target electrical parameters of the battery to be tested; Identifying the target electrical parameters based on a parameter identification algorithm and generating corresponding parameter identification results; Display the parameter identification result on the display interface.

[0005] Optionally, identifying the target electrical parameters based on a parameter identification algorithm and generating corresponding parameter identification results includes: Selecting a preset battery equivalent circuit model corresponding to the battery to be tested; Importing the target electrical parameters into a preset battery equivalent circuit model to generate corresponding battery model parameters; The battery model parameters are identified based on a parameter identification algorithm to generate corresponding parameter identification results.

[0006] Optionally, the identification device includes a plurality of electrical sensors, and the plurality of electrical sensors are electrically connected to the battery to be tested respectively to synchronously obtain electrical parameters of the battery to be tested; The step of obtaining target electrical parameters of the battery to be tested includes: Acquiring a plurality of first electrical parameters of the battery to be tested; pre-processing a plurality of the first electrical parameters; performing time alignment on the plurality of pre-processed first electrical parameters based on a linear interpolation algorithm to generate an aligned data sequence; The data sequence is averaged to determine the target electrical parameter.

[0007] Optionally, obtaining a plurality of first electrical parameters of the battery to be tested includes: The plurality of electrical sensors are triggered by a global clock source to synchronously sample data from the battery to be tested, so as to obtain the plurality of the first electrical parameters.

[0008] Optionally, the pre-processing of the plurality of first electrical parameters includes: Obtaining a zero drift compensation parameter and a gain error parameter of the electrical sensor; Calculating a difference between a first electrical parameter acquired by the electrical sensor and a zero drift compensation parameter and a gain error parameter to perform dynamic error correction on the first electrical parameter and generate a calibrated first electrical parameter; The calibrated first electrical parameter is filtered.

[0009] Optionally, the electrical data includes voltage data and current data, and a sampling frequency of the voltage data is higher than a sampling frequency of the current data; The step of performing time alignment on the plurality of pre-processed first electrical parameters based on a linear interpolation algorithm to generate an aligned data sequence includes: Taking the voltage data as a reference time point, at the time point corresponding to each voltage data, determining the corresponding target current data; An interpolated current between the target current data and adjacent current data is calculated to align a time point of the voltage data with a time point of the current data, thereby generating an aligned data sequence.

[0010] Optionally, before averaging the data sequence to determine the target electrical parameter, the method further includes: Get the preset deviation threshold; Comparing the aligned data sequences with the preset deviation thresholds one by one, and determining abnormal measurement values exceeding the preset deviation thresholds; The abnormal measurement values are removed and the data sequence is rearranged.

[0011] Optionally, the identification method further includes: The parameter recognition result is stored in a local storage device or the cloud, and displayed on the display interface in response to a user's call instruction.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also provides an identification device, which includes: a memory, a processor, and an identification of battery parameters stored in the memory and executable on the processor, wherein the identification of the battery parameters is configured to implement the battery parameter identification method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also provides an identification device, including the identification apparatus as described above.

[0014] The embodiment of the present invention obtains the target electrical parameters of the battery to be tested, where the target electrical parameters are key indicators such as voltage, current, resistance, and capacity, and then identifies the target electrical parameters based on a parameter identification algorithm. These target electrical parameters are deeply analyzed and processed. Through the operation of the algorithm, the performance characteristics of the battery can be effectively identified to generate corresponding parameter identification results. Finally, the parameter identification results are displayed on the display interface. This can greatly improve the accuracy of battery parameter identification, reduce the problems of accuracy degradation and zero point drift during the battery identification process, and solve the problem of asynchronous acquisition of multiple physical quantities, making battery parameter identification more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0016] 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 1 is a flow chart of a method for identifying battery parameters according to an embodiment of the present invention; Figure 2 1 is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention; Figure 3 This is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention; Figure 4 A flowchart of a method for identifying battery parameters according to another embodiment of the present invention is shown; Figure 5 This is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention; Figure 6 1 is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention; Figure 7 This is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention; Figure 8 FIG. 4 is a flow chart of a method for identifying battery parameters according to another embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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, and the well-known modules, units and their connections, links, communications or operations are not shown or described in detail. In addition, the described features, architectures or functions can be combined in any way in one or more embodiments. It should be understood by those skilled in the art that the various embodiments described below are only for illustration and are not intended to limit the scope of protection of the present invention. It can also be easily understood that the modules or units or processing methods in the various embodiments described herein and shown in the drawings can be combined and designed according to various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] The definitions of various nouns or methods in the following embodiments, except for those that are logically untenable, are generally based on the broad concepts that can be implemented under the premise of the disclosure in the embodiments. Under such an understanding, the various specific subordinate specific definitions of the nouns or methods should be regarded as the inventive content of the present invention, and should not be narrowly understood or interpreted in a biased manner on the grounds that the specification does not disclose such specific definitions. Similarly, under the premise that it can be logically implemented, the order of the steps in the method is flexible and changeable, and the specific subordinate specific definitions of the broad concepts of various nouns or methods are all within the scope of protection of the present invention.

[0021] The main solution of the embodiment of the present application is: by obtaining the target electrical parameters of the battery to be tested, where the target electrical parameters are key indicators such as voltage, current, resistance, and capacity, and then identifying the target electrical parameters based on the parameter identification algorithm, these target electrical parameters are deeply analyzed and processed. Through the operation of the algorithm, the performance characteristics of the battery can be effectively identified to generate corresponding parameter identification results, and finally the parameter identification results are displayed on the display interface.

[0022] In this embodiment, for ease of description, the following description is made with the identification device as the execution subject.

[0023] As the battery parameter identification sensors of the prior art often experience problems such as reduced accuracy and zero point drift over time, errors may occur in the collected battery data, thereby affecting the accuracy of parameter identification.

[0024] The present application provides a solution that can greatly improve the accuracy of battery parameter identification, reduce the problems of accuracy degradation and zero point drift during the battery identification process, and solve the problem of asynchronous acquisition of multiple physical quantities, making battery parameter identification more accurate and reliable.

[0025] To this end, the present invention proposes a method for identifying battery parameters; it can be understood that an identification device for storing and executing the following method is provided in the identification device, and the identification device can be implemented using a main controller, such as an MCU (Microcontroller Unit), a DSP (Digital Signal Process), an FPGA (Field Programmable Gate Array), a SOC (System On Chip), etc.

[0026] It needs to be understood that a battery is a device for energy conversion and storage, which mainly converts chemical energy, light energy and other forms of energy into electrical energy through chemical reactions or physical effects.

[0027] Battery parameters are important indicators for measuring battery performance. In a battery management system, accurate identification of battery parameters is required to ensure the safety of battery use, optimize battery performance, and extend battery life.

[0028] Traditional battery parameter identification methods require the use of electrical sensors such as temperature sensors, voltage sensors, and current sensors for parameter collection. However, with long-term use, the sensors will experience decreased accuracy and zero-point drift over time. When the sensors have the above problems, errors will occur in the collected battery data, which will affect the accuracy of parameter identification. In addition, battery parameter identification requires the collection of multiple physical quantities such as voltage, current, and temperature. However, different sensors have certain differences in the time it takes to affect the parameters, making it difficult to achieve synchronous collection. This often leads to time deviations between the data, thus affecting the identification accuracy.

[0029] To solve the above problems, refer to Figure 1 In one embodiment of the present invention, a battery parameter identification method is applied to an identification device. The battery parameter identification method includes steps S100-S300, wherein: S100, obtaining target electrical parameters of the battery to be tested; S200, identifying the target electrical parameters based on a parameter identification algorithm, and generating corresponding parameter identification results; S300: Display the parameter recognition result on a display interface.

[0030] Target electrical parameters may include open-circuit voltage, internal resistance, capacity, charge-discharge rate, temperature, and cycle life. These parameters comprehensively reflect the battery's operating status and health. For example, open-circuit voltage reflects the battery's electromotive force and remaining charge; internal resistance reflects changes in the battery's internal structure and aging; capacity and charge-discharge rate are directly related to the battery's energy storage and release capabilities; temperature parameters help monitor the battery's thermal management status and prevent safety hazards caused by overheating; and cycle life is a key indicator for evaluating the battery's long-term economic viability.

[0031] Parameter identification algorithms can be advanced machine learning algorithms such as Kalman filters, neural networks, or support vector machines. These algorithms possess powerful data processing and pattern recognition capabilities, enabling them to extract underlying patterns and characteristics of battery parameters from complex data, thereby improving the accuracy and reliability of parameter identification. During the identification process, the algorithm analyzes and calculates the target electrical parameters of the battery under test based on pre-set models and rules, ultimately generating the corresponding parameter identification results.

[0032] The display interface, which can be an LCD or touchscreen, intuitively displays parameter identification results. Users can clearly view various battery parameters, such as open-circuit voltage, internal resistance, and capacity, as well as their changing trends and historical records. This design not only improves the transparency of parameter identification but also facilitates user monitoring and management of battery status.

[0033] In this embodiment, for example, there is a battery that needs to be monitored. After the battery to be tested is connected to the identification device, the identification device in the identification device will start working immediately, first obtaining the target electrical parameters of the battery to be tested through the internal sensor or interface. These parameters include but are not limited to open circuit voltage, internal resistance, capacity, charge and discharge rate, temperature, and cycle life, which can fully reflect the current working status and health of the battery. After obtaining these parameters, the identification device will call the built-in parameter identification algorithm to conduct in-depth analysis and calculation of the target electrical parameters. These algorithms may be advanced machine learning algorithms such as Kalman filtering algorithms, neural network algorithms, or support vector machine algorithms. They can mine the potential laws and characteristics of battery parameters from complex data to ensure the accuracy and reliability of parameter identification.

[0034] After algorithmic processing, the identification device generates corresponding parameter identification results and intuitively presents them to the user through a display interface. Users can clearly view various battery parameters, along with their changing trends and historical records, on the LCD or touchscreen. This design not only allows users to monitor battery status at all times but also provides strong support for battery maintenance and management. This identification method effectively addresses the challenges inherent in traditional battery parameter identification methods, improving their accuracy and reliability and providing strong assurance for safe battery use and optimized performance.

[0035] This embodiment obtains target electrical parameters of the battery to be tested, where the target electrical parameters are key indicators such as voltage, current, resistance, and capacity, and then identifies the target electrical parameters based on a parameter identification algorithm. These target electrical parameters are deeply analyzed and processed. Through the operation of the algorithm, the performance characteristics of the battery can be effectively identified to generate corresponding parameter identification results. Finally, the parameter identification results are displayed on a display interface. This can greatly improve the accuracy of battery parameter identification, reduce the problems of accuracy degradation and zero point drift during the battery identification process, and solve the problem of asynchronous acquisition of multiple physical quantities, making battery parameter identification more accurate and reliable.

[0036] This embodiment effectively solves the problems of insufficient sensor accuracy, zero-point drift, and asynchronous acquisition of multiple physical quantities by adopting high-precision sensors, synchronous sampling technology, redundant sensor averaging, and linear interpolation algorithms, which is conducive to improving the accuracy of battery data acquisition.

[0037] Optionally, refer to Figure 2 Another embodiment of the present invention provides a method for identifying battery parameters. Figure 1 In the embodiment shown, the target electrical parameters are identified based on the parameter identification algorithm and the corresponding parameter identification results are generated, including steps S210-S230, wherein: S210, selecting a preset battery equivalent circuit model corresponding to the battery to be tested; S220, importing the target electrical parameters into a preset battery equivalent circuit model to generate corresponding battery model parameters; S230 : Identify the battery model parameters based on a parameter identification algorithm and generate corresponding parameter identification results.

[0038] The preset battery equivalent circuit model can be a Thevenin model, an RC model or a PNGV model, etc. These models can simulate the electrical behavior of the battery under different working conditions. By selecting a preset battery equivalent circuit model corresponding to the battery to be tested, the actual working state of the battery can be more accurately reflected. After importing the target electrical parameters into the preset battery equivalent circuit model, the model will generate corresponding battery model parameters based on these parameters. These battery model parameters can reflect the internal characteristics and dynamic response of the battery. Subsequently, the battery model parameters are identified based on the parameter identification algorithm to generate more accurate and reliable parameter identification results. This method not only improves the accuracy of parameter identification, but also can better adapt to batteries of different types and specifications, providing more comprehensive support for battery management and maintenance. Among them, the preset battery equivalent circuit model can be established according to the physical characteristics and working principle of the battery.

[0039] Furthermore, the pre-set battery equivalent circuit model can be replaced with an electrochemical model, which provides a more in-depth description of the chemical reactions within the battery. This model can better define the electrochemical reaction mechanisms during the battery's charge and discharge processes, leading to more accurate prediction and identification of battery parameters.

[0040] In electrochemical models, battery parameter identification often involves complex electrochemical reaction kinetics and material transport processes. To simplify the problem, simplified electrochemical models such as the single-particle model (SPM) or pseudo-two-dimensional model (P2D) can be used. These models can balance computational complexity and model accuracy to a certain extent, providing practical tools for battery parameter identification.

[0041] Parameter Identification Algorithm In this embodiment, the Kalman filter algorithm is selected as the identification algorithm. The processed data is input into the Kalman filter algorithm to perform real-time identification of the battery model parameters.

[0042] It should be noted that the identification device includes multiple electrical sensors, and the multiple electrical sensors are electrically connected to the battery to be tested respectively to synchronously obtain the electrical parameters of the battery to be tested. Among them, the multiple electrical sensors include voltage sensors, current sensors, temperature sensors and internal resistance sensors. The voltage sensor is used to measure the voltage value of the battery, the current sensor is used to measure the current value of the battery, the temperature sensor is used to measure the temperature of the battery, and the internal resistance sensor is used to measure the internal resistance value of the battery. These sensors can obtain various electrical parameters of the battery in real time and accurately. Among them, the temperature sensor can be arranged at at least two key positions in the battery surface, the middle and bottom of the shell.

[0043] The electrical sensors can be designed with redundancy based on actual design needs. For example, for voltage measurement, 3-5 high-precision voltage sensors can be used; for current measurement, 3-5 high-precision current sensors can be used; and for temperature measurement, multiple high-precision temperature sensors can be placed at key locations. These sensors offer high precision, high stability, and low drift, enabling accurate acquisition of battery physical data.

[0044] Among them, since the sensor adopts a redundant design, for the data collected by each group of redundant sensors, it is necessary to calculate its average value as the final measurement value of the physical quantity. For example, for voltage measurement, if there are n redundant voltage sensors, and their measurement values are V1, V2, ···, Vn respectively, then the final voltage measurement value Vavg = ,In this way, the influence of single sensor error on the ,measurement results can be effectively reduced, and the accuracy of data ,acquisition can be improved.

[0045] Among them, by adopting a multi-channel synchronous sampling chip or designing a special synchronous sampling circuit, it can be ensured that each sensor can start sampling at the same time and ensure that the collected data is synchronized in time.

[0046] Based on the above circuit structure, refer to Figure 3 Another embodiment of the present invention provides a method for identifying battery parameters. Figure 1 In the embodiment shown, obtaining the target electrical parameters of the battery to be tested includes steps S110-S140, wherein: S110, obtaining a plurality of first electrical parameters of the battery to be tested; S120, pre-processing a plurality of the first electrical parameters; S130, performing time alignment on the plurality of pre-processed first electrical parameters based on a linear interpolation algorithm to generate an aligned data sequence; S140: Take an average value of the data sequence to determine the target electrical parameter.

[0047] The first electrical parameters refer to the raw data of battery parameters directly measured by various electrical sensors. These data may contain errors and deviations due to differences in sensor accuracy, measurement environments, and data acquisition time. To improve the accuracy of parameter identification, these raw data need to be preprocessed. Preprocessing steps may include filtering to remove high-frequency noise and interference in the data and calibration to correct sensor measurement errors. After preprocessing, the first electrical parameters can more closely approximate the actual state of the battery, providing a reliable basis for subsequent analysis and calculations.

[0048] However, even after preprocessing, time deviations may still exist between the collected first electrical parameters due to differences in the time at which different sensors affect the parameters. To address this issue, this embodiment uses a linear interpolation algorithm to time-align multiple preprocessed first electrical parameters. The linear interpolation algorithm is a simple and effective numerical analysis method that can estimate the values of unknown data points based on the linear relationship between known data points. This alignment ensures that each parameter is collected and analyzed at the same time point, thereby eliminating the impact of time deviation on the data.

[0049] To improve parameter accuracy, the present invention also uses an averaging method to determine the target electrical parameters within the aligned data sequence. By averaging multiple aligned data points, the impact of random errors can be reduced, resulting in more stable and reliable parameter values. This design not only improves the accuracy of parameter identification but also enhances the robustness and adaptability of the identification method.

[0050] It should be understood that during the sampling process of electrical sensors, there will often be deviations in the sampling time, resulting in data errors. In order to provide a unified clock signal for the synchronous sampling circuit, this embodiment ensures that the sampling time of all sensors is consistent, further improving the data synchronization accuracy, referring to Figure 4 Another embodiment of the present invention provides a method for identifying battery parameters based on the above Figure 1 In the embodiment shown, obtaining a plurality of first electrical parameters of the battery to be tested includes step S111, wherein: S111 , triggering the plurality of electrical sensors to synchronously sample data from the battery to be tested by using a global clock source to obtain the plurality of the first electrical parameters.

[0051] A global clock source is a high-precision clock signal generator that generates a stable and synchronized clock signal with a synchronization error of less than 1 microsecond. By triggering multiple electrical sensors to synchronously sample data using the global clock source, each sensor can ensure that the battery parameters are measured at the same time. This approach effectively addresses the issue of asynchronous sensor data acquisition in traditional methods and avoids parameter identification errors caused by time skew. The design and use of a global clock source makes battery parameter identification more accurate and reliable, providing strong support for safe battery use and performance optimization. Furthermore, the introduction of a global clock source simplifies the circuit structure of the identification device, reducing system complexity and cost. In practical applications, the global clock source can be implemented using hardware circuits or simulated and generated using software algorithms. Regardless of the approach used, the core function of the global clock source is to ensure that each sensor can synchronously and accurately acquire the battery's electrical parameters, providing a reliable data foundation for subsequent parameter identification.

[0052] Optionally, refer to Figure 5 Another embodiment of the present invention provides a method for identifying battery parameters. Figure 1 In the embodiment shown, pre-processing the plurality of first electrical parameters includes steps S121-S123, wherein: S121, obtaining a zero drift compensation parameter and a gain error parameter of the electrical sensor; S122. Calculating a difference between a first electrical parameter acquired by the electrical sensor and a zero drift compensation parameter and a gain error parameter to perform dynamic error correction on the first electrical parameter to generate a calibrated first electrical parameter. S123: Perform filtering processing on the calibrated first electrical parameter.

[0053] Zero drift compensation is used to compensate for the zero-point offset of electrical sensors caused by environmental changes, component aging, and other factors during long-term use. Gain error is used to correct for any deviations in the proportional relationship between the sensor's output signal and the actual physical quantity. By obtaining these compensation and correction parameters, sensor measurement errors can be dynamically corrected, thereby improving the accuracy of measurement data.

[0054] During the calculation process, the difference between the first electrical parameter obtained by the electrical sensor and the zero-drift compensation parameter is first calculated to eliminate the impact of zero offset on the measurement result. Subsequently, the first electrical parameter is proportionally corrected based on the gain error parameter so that the sensor's output signal truly reflects the battery's actual electrical parameters. After these two correction steps, the calibrated first electrical parameters are obtained, which are closer to the battery's actual state.

[0055] However, the calibrated first electrical parameters may still be affected by some high-frequency noise and interference. To improve data accuracy, the calibrated first electrical parameters need to be filtered. This filtering can be achieved using various filtering algorithms, such as low-pass filtering, high-pass filtering, and band-pass filtering. The specific filtering algorithm to be used depends on the actual application scenario and noise characteristics. Filtering can effectively remove high-frequency noise and interference from the data, making the measured data smoother and more stable.

[0056] It should be noted that electrical data includes voltage and current data. The sampling frequency of the voltage data is higher than that of the current data to more accurately reflect the dynamic changes of the battery during the charging and discharging process. For example, the sampling frequency of the voltage sensor is 1000Hz, and the sampling frequency of the current sensor is 500Hz.

[0057] Since voltage parameters are more sensitive to changes in battery status, a higher sampling frequency can capture more detailed information. While the sampling frequency of current parameters is relatively low, it can still meet the basic monitoring needs of the battery charging and discharging process.

[0058] Based on this, refer to Figure 6 Another embodiment of the present invention provides a method for identifying battery parameters. Figure 1 In the embodiment shown, the time alignment of the plurality of pre-processed first electrical parameters based on the linear interpolation algorithm to generate an aligned data sequence includes steps S131-S132, wherein: S131, using the voltage data as a reference time point, determining corresponding target current data at a time point corresponding to each voltage data; S132 : Calculate an interpolated current between the target current data and adjacent current data to align the time points of the voltage data with the time points of the current data, and generate an aligned data sequence.

[0059] The target current data refers to the current data calculated by the linear interpolation algorithm at the time point corresponding to each voltage data. Since the sampling frequency of the voltage data is higher than the sampling frequency of the current data, there may be no direct current data corresponding to it at certain time points. In order to solve this problem, the present invention adopts a linear interpolation algorithm to calculate the target current data at these time points based on the adjacent current data. Among them, the time point corresponding to each voltage data is first determined, and then the current data adjacent to it is found at this time point. Then, the linear interpolation formula is used to calculate the value of the target current data based on the values of the two adjacent current data and their corresponding time points. In this way, the time point of the voltage data can be aligned with the time point of the current data to generate an aligned data sequence. Such a design not only improves the time synchronization of the data, but also ensures the accuracy of subsequent parameter identification.

[0060] In practical applications, first determine the known data points. Assume that the voltage sensor sampling frequency is f V , the current sensor sampling frequency is f I , and f V >f I , the voltage sensor data sequence is {(t V1 , V1), (t V2 , V2),…,(t Vm , V m )}, the current sensor data sequence is {(t I1 , I1), (t I2 , I2),…,(t In , I n)}, then select the alignment benchmark, take the voltage sensor data with a higher sampling frequency as the benchmark, align the current sensor data to the time point of the voltage sensor, and then traverse the benchmark time points. For each time point t of the voltage sensor Vi , find two adjacent time points t in the current sensor data sequence Ij and t I(j+1) , so that t Ij ≤t Vi ≤t I(j+1) ; Then calculate the interpolation result: According to the linear interpolation formula I =Ij+ , calculate the current sensor at time point t Vi The interpolation result I ; Regenerate the aligned data sequence: interpolate the I The time point t of the voltage sensor Vi Correspondingly, generate the aligned data sequence {(t V1 , V1, I ), (t V2 , V2, I ),…,(t Vm , V m , I)}; Finally, the data collected by each group of redundant sensors are fused and processed, and the average value is calculated as the final measurement value of the physical quantity.

[0061] Optionally, refer to Figure 7 Another embodiment of the present invention provides a method for identifying battery parameters. Figure 1 In the embodiment shown, before averaging the data sequence and determining the target electrical parameter, steps S150 to S170 are further included, wherein: S150, obtaining a preset deviation threshold; S160, comparing the aligned data sequences with the preset deviation thresholds one by one, and determining abnormal measurement values exceeding the preset deviation thresholds; S170: Eliminate the abnormal measurement values and rearrange the data sequence.

[0062] The preset deviation threshold is a reasonable range of values set based on the actual application scenario and battery characteristics. This value is used to measure the accuracy and reliability of the measured data. During the battery parameter identification process, the collected data may have certain deviations due to various factors such as sensor errors and environmental changes. To ensure the accuracy of subsequent parameter identification, this data needs to be screened and outliers removed.

[0063] Each data point in the aligned data sequence is compared to a preset deviation threshold. If the deviation of a data point exceeds the preset deviation threshold, it is considered an outlier. These outliers may be caused by sensor failure, data acquisition errors, or other unknown factors, and their presence can adversely affect subsequent parameter identification results. Therefore, these outliers need to be removed from the data sequence.

[0064] After removing outliers, the data sequence must be rearranged to ensure data continuity and integrity. This rearranged data sequence is used for subsequent average calculation and determination of target electrical parameters. This process improves the accuracy and reliability of battery parameter identification, providing stronger support for safe battery use and performance optimization.

[0065] Optionally, refer to Figure 8 Another embodiment of the present invention provides a method for identifying battery parameters based on the above Figure 1 In the illustrated embodiment, the identification method further includes step S400, wherein: S400: Storing the parameter recognition result in a local storage device or a cloud, and displaying it on the display interface in response to a user's call instruction.

[0066] The local storage device can be the internal memory of the identification device or a connected external storage device, such as a hard drive or flash drive. The cloud can be a remote server or data center that exchanges data with the identification device via a network connection. Storing parameter identification results in the local storage device or cloud allows users to easily view and manage battery status information at any time.

[0067] By storing parameter identification results on a local storage device or in the cloud, data persistence and security can be ensured. Appropriate data formats and encoding methods can be used for storage to facilitate subsequent data processing and retrieval. When the user needs to view the battery parameter identification results, they can trigger the identification device to display the results on a display interface by calling a command. The display interface can be the display screen of the identification device or an external monitor connected to it. When displaying, appropriate display methods and formats, such as tables and charts, can be selected according to actual needs to facilitate intuitive understanding and analysis of the data.

[0068] The present invention also proposes an identification device, which includes: the identification device includes: a memory, a processor, and an identification of battery parameters stored in the memory and executable on the processor, wherein the identification of the battery parameters is configured to implement the battery parameter identification method as described above.

[0069] It is worth noting that since the identification device of the present invention is based on the above-mentioned battery parameter identification method, the embodiments of the identification device of the present invention include all technical solutions of all embodiments of the above-mentioned battery parameter identification method, and the technical effects achieved are also exactly the same, which will not be repeated here.

[0070] The present invention further provides an identification device, which includes the identification apparatus described in the above embodiment.

[0071] It is worth noting that since the identification device of the present invention is based on the above-mentioned identification device, the embodiments of the identification device of the present invention include all technical solutions of all embodiments of the above-mentioned identification device, and the technical effects achieved are also exactly the same, which will not be repeated here.

[0072] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0073] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0074] Through the above description of the embodiments, those skilled in the art will clearly understand that the methods of the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) as described above and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0075] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying battery parameters, applied to an identification device, characterized in that: The identification method comprises: Obtain target electrical parameters of the battery to be tested; Identifying the target electrical parameters based on a parameter identification algorithm and generating corresponding parameter identification results; Display the parameter identification result on the display interface.

2. The method for identifying battery parameters according to claim 1, wherein: The identifying the target electrical parameters based on the parameter identification algorithm and generating corresponding parameter identification results include: Selecting a preset battery equivalent circuit model corresponding to the battery to be tested; Importing the target electrical parameters into a preset battery equivalent circuit model to generate corresponding battery model parameters; The battery model parameters are identified based on a parameter identification algorithm to generate corresponding parameter identification results.

3. The method for identifying battery parameters according to claim 1, wherein: The identification device includes a plurality of electrical sensors, each of which is electrically connected to a battery to be tested to synchronously obtain electrical parameters of the battery to be tested; The step of obtaining target electrical parameters of the battery to be tested includes: Acquiring a plurality of first electrical parameters of the battery to be tested; pre-processing a plurality of the first electrical parameters; Time-aligning the plurality of pre-processed first electrical parameters based on a linear interpolation algorithm to generate an aligned data sequence; The data sequence is averaged to determine the target electrical parameter.

4. The method for identifying battery parameters according to claim 3, wherein: The obtaining of a plurality of first electrical parameters of the battery to be tested includes: The plurality of electrical sensors are triggered by a global clock source to synchronously sample data from the battery to be tested, so as to obtain the plurality of the first electrical parameters.

5. The method for identifying battery parameters according to claim 3, wherein: The pre-processing of the plurality of the first electrical parameters includes: Obtaining a zero drift compensation parameter and a gain error parameter of the electrical sensor; Calculating a difference between a first electrical parameter acquired by the electrical sensor and a zero drift compensation parameter and a gain error parameter to perform dynamic error correction on the first electrical parameter and generate a calibrated first electrical parameter; The calibrated first electrical parameter is filtered.

6. The method for identifying battery parameters according to claim 3, wherein: The electrical data includes voltage data and current data, and the sampling frequency of the voltage data is higher than the sampling frequency of the current data; The step of performing time alignment on the plurality of pre-processed first electrical parameters based on a linear interpolation algorithm to generate an aligned data sequence includes: Taking the voltage data as a reference time point, at the time point corresponding to each voltage data, determining the corresponding target current data; An interpolated current between the target current data and adjacent current data is calculated to align a time point of the voltage data with a time point of the current data, thereby generating an aligned data sequence.

7. The method for identifying battery parameters according to claim 3, wherein: Before averaging the data sequence to determine the target electrical parameter, the method further includes: Get the preset deviation threshold; Comparing the aligned data sequences with the preset deviation thresholds one by one, and determining abnormal measurement values exceeding the preset deviation thresholds; The abnormal measurement values are removed and the data sequence is rearranged.

8. The method for identifying battery parameters according to claim 1, wherein: The identification method further includes: The parameter recognition result is stored in a local storage device or the cloud, and displayed on the display interface in response to a user's call instruction.

9. An identification device, characterized in that: The identification device includes: a memory, a processor, and a battery parameter identification program stored in the memory and executable on the processor. The battery parameter identification is configured to implement the battery parameter identification method according to any one of claims 1 to 8.

10. An identification device, characterized in that: Comprising the identification device as claimed in claim 9.

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