Retired battery rapid screening method and device based on pulse detection technology

Abstract

The invention belongs to the technical field of retired battery screening, and provides a method and a device for rapidly screening retired batteries based on a pulse detection technology. The method comprises the steps that S1, dynamic characteristics of a plurality of decommissioned batteries at the current moment are obtained, direct-current pulse current signals are applied to the decommissioned batteries, and the dynamic characteristics comprise the voltage, the current and the temperature of the decommissioned batteries at the current moment; s2, the dynamic characteristics of each decommissioned battery at the current moment are input into a prediction model, the prediction state of the decommissioned battery at the current moment is obtained, and the prediction state comprises the charge state, the health state and the battery internal resistance; s3, processing the prediction states of the plurality of retired batteries at the current moment by adopting a clustering algorithm; and S4, calculating a target function value based on the clustering result, when the target function value reaches a preset condition, carrying out retired battery grading according to the clustering result, and when the target function value does not reach the preset condition, returning to S1. According to the invention, the accuracy and consistency of grading and grouping of the retired batteries are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of retired battery screening, and in particular to a method and device for quickly screening retired batteries based on pulse detection technology. Background Art

[0002] When screening and grading retired batteries, the relevant technology uses a dynamic characteristic sorting method, which is mainly based on the dynamic characteristics of the battery charging and discharging process to sort and group them. For example, the voltage and time on the battery charging and discharging characteristic curve are extracted as identification features, and the target membership function is established to complete the decision of the curve category based on the threshold criterion. Or multiple characteristic parameters are extracted from the charging curve, and the state of health (SOH) is estimated by combining the improved Gaussian regression model, thereby providing a basis for the subsequent battery recycling. The dynamic characteristic sorting method is based on the battery charging and discharging process, and can track the changes in various battery parameters in a timely and accurate manner. However, due to the different discharge levels of each single cell in the retired battery pack, the initial state of each battery cell is different, which will have an adverse effect on the consistency and accuracy of the retired battery screening. Summary of the Invention

[0003] The present application aims to at least solve the technical problems existing in the prior art and provide a method and device for quickly screening retired batteries based on pulse detection technology.

[0004] In a first aspect, the present application provides a method for rapid screening of retired batteries based on pulse detection technology, comprising:

[0005] Step S1, obtaining dynamic characteristics of a plurality of retired batteries at a current moment, wherein a DC pulse current signal is applied to the retired batteries, and the dynamic characteristics include at least one of voltage, current, and temperature of the retired batteries at the current moment;

[0006] Step S2: Inputting the current dynamic characteristics of each retired battery into a prediction model to obtain a predicted state of the retired battery at the current moment, wherein the predicted state includes the state of charge and the state of health, or the predicted state includes the state of charge, the state of health, and the battery internal resistance;

[0007] Step S3, using a clustering algorithm to process the predicted states of multiple retired batteries at the current moment to obtain a clustering result;

[0008] Step S4, calculate the objective function value based on the clustering result. When the objective function value reaches the preset condition, perform retired battery classification according to the clustering result. When the objective function value does not reach the preset condition, return to execute steps S1 to S4 at the next moment.

[0009] Further preferably, the prediction model obtains the predicted state of the retired battery at the current moment based on a Kalman filter algorithm.

[0010] Further preferably, the prediction model performs:

[0011] The state equation of the Kalman filter algorithm is constructed using the state of charge and health of the retired battery as the state;

[0012] The dynamic characteristics of retired batteries are used as observation values ​​for the Kalman filter algorithm;

[0013] Based on the Kalman filter algorithm, the constructed state equation and observation values ​​are used to obtain the predicted state of the retired battery at the current moment.

[0014] Further preferably, when the predicted state includes the state of charge and the state of health, the state equation of the Kalman filter algorithm is:

[0015]

[0016] Among them, SOC k Indicates the state of charge of the retired battery at the current time k; SOH k Indicates the health status of the retired battery at the current time k; SOC k-1 Indicates the state of charge of the retired battery at time k-1 before; SOH k-1 represents the health status of the retired battery at time k-1 before; Δt represents the sampling time interval; I k represents the current of the retired battery at the current moment k; C represents the rated capacity of the retired battery cell; ω 1,k represents the process noise of the state of charge of the retired battery at the current time k; ω 2,k The process noise representing the health status of the retired battery at the current time k.

[0017] Further preferably, in step S3, the clustering algorithm is a mean clustering algorithm.

[0018] Further preferably, the objective function is expressed as:

[0019]

[0020] Among them, K represents the number of clusters in the clustering result; j represents the index of the cluster, 1≤j≤K; x i represents the predicted state of the i-th retired battery in the j-th cluster at the current moment; c j represents the cluster center of the jth cluster; i∈j indicates that the i-th retired battery belongs to the j-th cluster; n j represents the number of retired batteries in the jth cluster.

[0021] In a second aspect, the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the steps of a method for rapid screening of retired batteries based on pulse detection technology provided in the first aspect of the present invention.

[0022] In a third aspect, the present application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for rapid screening of retired batteries based on pulse detection technology as described in the first aspect of the present invention.

[0023] In a fourth aspect, the present application provides a device for rapidly screening retired batteries based on pulse detection technology, comprising: a pulse power supply for applying DC pulse current signals to a plurality of retired batteries respectively; a data acquisition module for collecting dynamic characteristics of a plurality of retired batteries at a preset sampling frequency; a processing module connected to the data acquisition module, and completing the screening of retired batteries according to the method for rapidly screening retired batteries based on pulse detection technology as described in the first aspect of the present invention.

[0024] Further preferably, the frequency range of the DC pulse current signal is 10 Hz-3 kHz, and the current amplitude range is 0.1C-1.5C.

[0025] The beneficial technical effects of the present application are as follows: the present application uses the dynamic characteristics of retired batteries under DC pulse current to evaluate the charge and discharge performance of battery cells. Since the DC short-time pulse current acts on the battery cells for a short time, the pulse signal is much smaller than the interference of other signals. Moreover, due to the short-time and multiple characteristics, the battery characteristics can be comprehensively evaluated, thereby improving the detection accuracy. The state of charge, health state and battery internal resistance are used as the predicted states of retired batteries, and they are classified and marked according to the predicted states. This can achieve reasonable and accurate grouping of battery cells with similar performance, thereby effectively improving the consistency and overall service life of the battery pack, and providing reliable guarantees for cascade utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a method for rapid screening of retired batteries based on pulse detection technology in a preferred embodiment of the present invention;

[0027] Figure 2 This is a schematic structural diagram of a device for rapid screening of retired batteries based on pulse detection technology in a preferred embodiment of the present invention;

[0028] Figure 3 It is a structural diagram of an electronic device in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0030] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0031] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal communication between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0032] The method for rapid screening of retired batteries based on pulse detection technology provided herein can be executed by at least one electronic device, such as a server or a terminal, that can be configured to execute the method provided in the embodiments of this application. In other words, the method for rapid screening of retired batteries based on pulse detection technology can be executed by software or hardware installed on a terminal or server device.

[0033] This application provides a method for rapid screening of retired batteries based on pulse detection technology. In a preferred embodiment, see Figure 1 , the method comprising:

[0034] Step S1 : obtaining dynamic characteristics of multiple retired batteries at a current moment, wherein a DC pulse current signal is applied to the retired batteries, and the dynamic characteristics include at least one of voltage, current, and temperature of the retired batteries at a current moment.

[0035] In this embodiment, if a traditional single pulse current signal is applied to a retired battery, it only has one pulse, and the battery information obtained is relatively small, which limits its applicability to some detection scenarios that require a large amount of data for comprehensive analysis and statistical evaluation. If a sinusoidal half-wave pulse current signal is applied to a retired battery, when it is necessary to detect the response of the measured object at multiple different frequencies, a single sinusoidal half-wave pulse current signal may not meet the requirements. Based on this, the present application applies a DC pulse current signal to the retired battery, which can better evaluate the performance of the battery cell under charge and discharge, such as key data such as internal resistance characteristics, capacity and self-discharge rate, and health status.

[0036] DC pulse current signal is an effective method. Since the DC short-time pulse current signal acts on the battery cell for a short time, the pulse current signal is far less than the interference of other signals. In addition, due to its short and multiple characteristics, it can comprehensively evaluate the battery characteristics, thereby improving the detection accuracy.

[0037] In this embodiment, the DC pulse current signal can be provided by an existing pulse power supply, and is not limited to using cables to connect the positive and negative poles of the retired battery cell to the positive and negative output terminals of the pulse power supply respectively, and then applying a short-term DC pulse current signal. The frequency range of the short-term DC pulse current signal is 10Hz-3KHz, and the current amplitude range is 0.1C-1.5C, where C represents the rated capacity of the retired battery cell. Under the action of the short-term DC pulse current signal, the voltage response of the battery cell can reflect its internal state and performance differences. Therefore, a high-precision sensor can be used to collect dynamic characteristics on the battery cell at a preset sampling frequency, such as once per second. The dynamic characteristics include at least one of the voltage, current, and temperature of the retired battery at the current moment. Preferably, the dynamic characteristics include the voltage, current, and temperature of the retired battery at the current moment.

[0038] Step S2: Input the current dynamic characteristics of each retired battery into the prediction model to obtain the predicted state of the retired battery at the current moment, wherein the predicted state includes the state of charge and the state of health, or the predicted state includes the state of charge, the state of health and the battery internal resistance.

[0039] In this embodiment, the prediction model is preferably, but not limited to, an existing multi-layer perceptron (MLP) for implementing a many-to-many mapping of dynamic features to predicted states. Specifically, a dynamic feature-predicted state sample set can be constructed to train the MLP network, and a prediction model can be obtained after training.

[0040] In this embodiment, the state of charge, State of Charge, abbreviated as SOC, represents the percentage of the current remaining capacity of the battery to the rated capacity, reflecting the "remaining capacity" of the battery. SOC changes in real time with charging and discharging, and is affected by factors such as temperature, load current, and aging. The state of health, State of Health, abbreviated as SOH, reflects the degree of attenuation of the current performance of the battery relative to a new battery, usually measured by capacity or internal resistance attenuation. The internal resistance of the battery is the equivalent resistance inside the battery that hinders the flow of current, including ohmic internal resistance (contact resistance) and polarization internal resistance (electrochemical polarization). As the internal resistance of the battery increases, the energy efficiency decreases, the heat generation increases, and the output power decreases.

[0041] Step S3: Using a clustering algorithm to process the predicted states of multiple retired batteries at the current moment to obtain a clustering result.

[0042] In this embodiment, the clustering algorithm is preferably a mean clustering algorithm, which calculates the distance between the predicted states of any two retired batteries and clusters them according to the size of the distance. The obtained clustering results include K clusters, each cluster corresponds to a performance level, and K is a positive integer greater than 1.

[0043] Step S4, calculate the objective function value based on the clustering result. When the objective function value reaches the preset condition, perform retired battery classification according to the clustering result. When the objective function value does not reach the preset condition, return to step S1 to enter the next moment processing.

[0044] In this embodiment, preferably, the objective function is expressed as:

[0045]

[0046] Where K represents the number of clusters in the clustering result; j represents the index of the cluster, 1≤j≤K; x i represents the predicted state of the i-th retired battery in the j-th cluster at the current moment; c j represents the cluster center of the jth cluster; i∈j indicates that the i-th retired battery belongs to the j-th cluster; n j represents the number of retired batteries in the jth cluster.

[0047] In this embodiment, the preset condition is not limited to the objective function value reaching a minimum, or the objective function value being less than or equal to a preset minimum objective function value. The objective function value is calculated once at each moment. If the objective function value reaches a minimum, or the objective function value is less than or equal to the preset minimum objective function value, the clustering result is considered to have met the preset condition. At this time, each cluster in the clustering result obtained in step S3 is regarded as a performance level. If the objective function value continues to increase, or the objective function value is greater than the preset minimum objective function value, the clustering result is considered to have not met the preset condition, and dynamic features need to be collected at the next moment, and the process returns to steps S1-S4.

[0048] Exemplarily, the clustering result includes three levels, that is, K is 3, and the three levels specifically include: a high performance level, a medium performance level, and a low performance level.

[0049] In a preferred embodiment, to achieve better clustering and grading effects, after step S2 and before step S3, the process further includes the following steps: using pre-established screening criteria to perform screening based on the predicted state of retired batteries. Specifically, if the current health state of a retired battery is lower than 0.8, the battery cell of the retired battery is considered severely aged and is removed. If the estimated state of charge error of a retired battery at the current moment exceeds the range of ±0.05, it indicates that its performance is unstable and is also removed. All retired batteries are traversed and removed according to the above screening criteria. Step S3 is then executed based on the predicted current state of the remaining retired batteries after removal.

[0050] In a preferred embodiment, in order to obtain a more accurate predicted state, the prediction model obtains the predicted state of the retired battery at the current moment based on a Kalman filter algorithm.

[0051] In this embodiment, preferably, the prediction model performs:

[0052] The state equation of the Kalman filter algorithm is constructed using the state of charge and health of the retired battery as the state;

[0053] The dynamic characteristics of retired batteries are used as observation values ​​for the Kalman filter algorithm;

[0054] Based on the Kalman filter algorithm, the constructed state equation and observation values ​​are used to obtain the predicted state of the retired battery at the current moment.

[0055] In this embodiment, further preferably, when the predicted state includes the state of charge and the state of health, the state equation of the Kalman filter algorithm is:

[0056]

[0057] Among them, SOC kIndicates the state of charge of the retired battery at the current time k; SOH k Indicates the health status of the retired battery at the current time k; SOC k-1 Indicates the state of charge of the retired battery at time k-1 before; SOH k-1 represents the health status of the retired battery at the moment k-1 before; Δt represents the sampling time interval, that is, the time interval between the current moment and the previous moment; I k represents the current of the retired battery at the current moment k; C represents the rated capacity of the retired battery cell; ω 1,k represents the process noise of the state of charge of the retired battery at the current time k; ω 2,k The process noise representing the health status of the retired battery at the current time k.

[0058] In this embodiment, the state equation is based on the relationship between the voltage measurement value of the battery cell and the SOC and SOH. The SOC is used to calculate the remaining power based on the rated capacity of the retired battery, and the SOH evaluation is also closely related to the capacity of the retired battery. Capacity decay is one of the important manifestations of SOH decline.

[0059] In another preferred embodiment, the specific process of the prediction model obtaining the predicted state of the retired battery at the current moment based on the Kalman filter algorithm is as follows:

[0060] 1. Initialize the Kalman filter initial state estimate.

[0061] Based on the nominal parameters or pre-experimental data of the battery cell, set the initial SOC and SOH estimates, as well as their corresponding initial estimation error covariance matrices. When setting the initial SOC and SOH estimates and the initial estimation error covariance matrix, the initial SOC is typically set to 50% (if a clear initial state is available, set according to the actual setting), the initial SOH for new batteries is set to 100% (for used batteries, set based on historical or test results), and the initial SOC estimation error covariance matrix is ​​set to 0.01 to 0.25 depending on the level of certainty. The initial SOH estimation error covariance matrix is ​​set to 0.01 for new batteries and 0.1 for used batteries. Reflecting the uncertainty of the initial state estimate, the current SOC and SOH are predicted based on the state equation, and the prediction error covariance matrix is ​​updated.

[0062] 2. Kalman gain calculation

[0063] The Kalman gain K at the current moment k is calculated according to the following formula k for:

[0064]

[0065] in, is the prior estimation error covariance matrix of k at the current moment, H kis the observation matrix at the current moment k, R K is the covariance matrix of the observation noise at the current time k. Kalman gain K at the previous time k k It determines how to use new observations to update the state estimate and then makes predictions for each retired battery to be screened.

[0066] 3. State estimation update:

[0067]

[0068] in, It is the a priori predicted state estimate, which is the state estimate at the current moment obtained based on the state estimate at the previous moment and the state equation of the system; It is the updated predicted state estimate, which uses Kalman filtering to estimate key parameters such as SOC, SOH and battery internal resistance. Compare the estimated parameters with the screening criteria. For the battery cells that meet the screening criteria, they are classified and marked according to performance indicators so that the battery cells can be reasonably allocated in the subsequent battery pack integration, such as battery cells with similar performance can be combined into battery packs to improve the overall performance and consistency of the battery pack. By combining the state model of the new battery and comparing it with the existing battery detection status, the health status of the retired battery cells can be preliminarily judged and the service life can be estimated. The predicted state at the current moment k is then output through Kalman filtering as SOC k 、SOH k and R k (representing the state of charge, health status, and internal resistance of the battery cell at the kth moment, respectively), and the voltage, current, temperature and other battery characteristics in the original battery cell data are used as calibration values. The calibrated voltage, resistance, current and other characteristic data are normalized for subsequent modeling. The trained model is used to re-verify the performance prediction of the retired battery cell data to obtain the predicted value and uncertainty estimate. The output results of the battery cell are then processed according to the Kalman filter technology to obtain the predicted state, and the inspection data of the battery cell is judged again. Through pulse detection technology, using Kalman filtering combined with Gaussian process regression method, the screening accuracy of the battery cell can be increased to 95%, and the screening time of a single battery cell is reduced to 3 hours, achieving efficient and accurate screening of the battery cell and improving the accuracy of the estimation.

[0069] The present invention also discloses a rapid screening device for retired batteries based on pulse detection technology, please refer to Figure 2 In a preferred embodiment, the device comprises:

[0070] A pulse power supply is configured to apply a DC pulse current signal to each of the multiple retired batteries. The pulse power supply may have multiple DC pulse current signal output terminals, each of which is connected to each of the multiple retired batteries in a one-to-one correspondence. The pulse power supply may also output a single DC pulse current signal, which is applied simultaneously to the battery terminals of the multiple retired batteries.

[0071] The data acquisition module collects dynamic characteristics of multiple retired batteries according to a preset sampling frequency.

[0072] In this embodiment, the data acquisition module includes multiple voltage sensors, multiple current sensors, and multiple temperature sensors. Each retired battery's voltage output terminal is equipped with a voltage sensor and a current sensor. Each retired battery also has a temperature sensor. The temperature sensor can be an internal NTC resistor or a surface-mounted temperature sensor.

[0073] In this embodiment, the data acquisition module also includes an acquisition card, which is connected to multiple voltage sensors, multiple current sensors, and multiple temperature sensors. The output of the acquisition card is connected to the signal input of the processing module to transmit dynamic characteristic data, thereby reducing the I / O interface of the processing module. The acquisition card collects the signals output by each sensor and converts the collected analog signals into digital signals for processing by the processing module.

[0074] The processing module is connected to the data acquisition module and completes the retired battery screening according to the retired battery rapid screening method based on the pulse detection technology provided by the present invention.

[0075] In this embodiment, the processing module is connected to the sensor output terminal of the data acquisition module, or the processing module is connected to the acquisition card of the data acquisition module. The processing module is preferably but not limited to a single chip microcomputer, ARM or computer host.

[0076] In this embodiment, further preferably, the frequency range of the DC pulse current signal is 10 Hz-3 kHz, and the amplitude range is 0.1C-1.5C, where C represents the rated capacity of the retired battery cell.

[0077] The present invention also discloses a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for rapid screening of retired batteries based on pulse detection technology provided by the present invention. The computer program product should be understood as a software product that mainly implements its solution through a computer program, such as a program product integrated in the cloud or a software library.

[0078] The present invention also discloses an electronic device. In one embodiment, the electronic device includes at least one processor; and a memory connected to the at least one processor; wherein,

[0079] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute a method for quickly screening retired batteries based on pulse detection technology provided by the present invention.

[0080] like Figure 3 FIG2 is a schematic diagram of an electronic device for implementing a method for rapidly screening retired batteries based on pulse detection technology, according to one embodiment of the present invention. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for rapidly screening retired batteries based on pulse detection technology.

[0081] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing or executing programs or modules stored in the memory 11 (for example, executing a method for quickly screening retired batteries based on pulse detection technology, etc.), as well as calling data stored in the memory 11, to perform various functions of the electronic device and process data.

[0082] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash memory card (FlashCard), etc. equipped on the electronic device. Furthermore, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a retired battery rapid screening method program based on pulse detection technology, but can also be used to temporarily store data that has been output or is to be output.

[0083] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0084] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, for displaying information processed in the electronic device and for displaying a visual user interface.

[0085] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3The structure shown does not limit the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0086] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0087] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0088] Furthermore, if the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0089] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "example," "specific example," "one implementation," "a preferred implementation," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0090] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for rapid screening of retired batteries based on pulse detection technology, characterized in that: include: Step S1, obtaining dynamic characteristics of a plurality of retired batteries at a current moment, wherein a DC pulse current signal is applied to the retired batteries, and the dynamic characteristics include at least one of voltage, current, and temperature of the retired batteries at the current moment; Step S2: Inputting the current dynamic characteristics of each retired battery into a prediction model to obtain a predicted state of the retired battery at the current moment, wherein the predicted state includes the state of charge and the state of health, or the predicted state includes the state of charge, the state of health, and the battery internal resistance; Step S3, using a clustering algorithm to process the predicted states of multiple retired batteries at the current moment to obtain a clustering result; Step S4, calculate the objective function value based on the clustering result. When the objective function value reaches the preset condition, perform retired battery classification according to the clustering result. When the objective function value does not reach the preset condition, return to execute steps S1 to S4 at the next moment.

2. The method for rapid screening of retired batteries based on pulse detection technology according to claim 1, characterized in that: The prediction model obtains the current predicted state of the retired battery based on a Kalman filter algorithm.

3. The method for rapid screening of retired batteries based on pulse detection technology according to claim 2, characterized in that: The prediction model performs: The state equation of the Kalman filter algorithm is constructed using the state of charge and health of the retired battery as the state; The dynamic characteristics of retired batteries are used as observation values ​​for the Kalman filter algorithm; Based on the Kalman filter algorithm, the constructed state equation and observation values ​​are used to obtain the predicted state of the retired battery at the current moment.

4. The method for rapid screening of retired batteries based on pulse detection technology according to claim 3, characterized in that: When the predicted state includes the state of charge and the state of health, the state equation of the Kalman filter algorithm is: Among them, SOC k Indicates the state of charge of the retired battery at the current time k; SOH k Indicates the health status of the retired battery at the current time k; SOC k-1 Indicates the state of charge of the retired battery at time k-1 before; SOH k-1 represents the health status of the retired battery at time k-1 before; Δt represents the sampling time interval; I k represents the current of the retired battery at the current moment k; C represents the rated capacity of the retired battery cell; ω 1,k represents the process noise of the state of charge of the retired battery at the current time k; ω 2,k The process noise representing the health status of the retired battery at the current time k.

5. The method for rapid screening of retired batteries based on pulse detection technology according to claim 1, characterized in that: In step S3, the clustering algorithm is a mean clustering algorithm.

6. The method for rapid screening of retired batteries based on pulse detection technology according to claim 5, characterized in that: The objective function is expressed as: Among them, K represents the number of clusters in the clustering result; j represents the index of the cluster, 1≤j≤K; x i represents the predicted state of the i-th retired battery in the j-th cluster at the current moment; c j represents the cluster center of the jth cluster; i∈j indicates that the i-th retired battery belongs to the j-th cluster; n j represents the number of retired batteries in the jth cluster.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of any one of the methods of claims 1 to 6 are implemented.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, A memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for rapid screening of retired batteries based on pulse detection technology as described in any one of claims 1 to 6.

9. A rapid screening device for retired batteries based on pulse detection technology, characterized in that: include: a pulse power supply, used for applying a DC pulse current signal to the plurality of retired batteries respectively; A data acquisition module collects dynamic characteristics of multiple retired batteries according to a preset sampling frequency; The processing module is connected to the data acquisition module and completes the retired battery screening according to the retired battery rapid screening method based on pulse detection technology according to any one of claims 1 to 6.

10. The device for rapid screening of retired batteries based on pulse detection technology according to claim 9, characterized in that: The frequency range of the DC pulse current signal is 10 Hz-3 kHz, and the current amplitude range is 0.1C-1.5C.

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