Battery charge-discharge cycle index determination method and device, medium and equipment

Abstract

The invention belongs to the technical field of battery management, and provides a battery charge-discharge cycle index determination method and device, a medium and equipment, and the method comprises the steps: obtaining SOC data of a power battery at a plurality of continuous moments, and forming an SOC data sequence; sOC data in the SOC data sequence are extracted in sequence, the difference value between the currently extracted SOC data and the last extracted SOC data is calculated, and if the difference value is an increment, SOC increments are accumulated; when the accumulated SOC increment reaches an increment threshold value, recording as a charging cycle; sOC data in the SOC data sequence are extracted in sequence, the difference value between the currently extracted SOC data and the last extracted SOC data is calculated, and if the difference value is decrement, SOC decrement is accumulated; and when the accumulated SOC decrement reaches a decrement threshold value, recording as a discharge cycle. The statistical result of the cycle index is more accurate and comprehensive, and the overall performance and reliability of the battery management system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a method, device, medium and equipment for determining the number of battery charge and discharge cycles. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The health status and performance of power batteries are critical to electric vehicles and other energy storage applications. The decisions of the battery management system (BMS) rely on the comprehensiveness, accuracy and reliability of battery data.

[0004] Since the BMS generally does not record the number of battery charge and discharge cycles, and consumers' usage is not ideal for full charge and discharge, extracting the number of charge and discharge cycles from cloud data is a very important task, but there is currently no effective method.

[0005] Moreover, battery data may be affected by various factors during collection and transmission, resulting in abnormal data. Existing technologies have problems such as low efficiency and poor accuracy in identifying and processing abnormal data, which also affects the accuracy of extracting the number of charge and discharge cycles. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method, device, medium and equipment for determining the number of battery charge and discharge cycles, and provides a cycle counting method based on SOC increment and decrement, which can accurately extract the number of battery charge and discharge cycles. This method not only takes into account the change of SOC, but also combines the calculation of cumulative increment and decrement, ensuring that the statistical results of the cycle number are more accurate and comprehensive, thereby improving the overall performance and reliability of the battery management system.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a method for determining the number of charge and discharge cycles of a battery.

[0009] A method for determining the number of battery charge and discharge cycles, comprising:

[0010] Obtain the SOC data of the power battery at several consecutive moments to form an SOC data sequence;

[0011] Extract the SOC data in the SOC data sequence in order, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is an increment, accumulate the SOC increment; when the accumulated SOC increment reaches the increment threshold, it is recorded as a charging cycle;

[0012] The SOC data in the SOC data sequence is extracted in sequence, and the difference between the currently extracted SOC data and the last extracted SOC data is calculated. If the difference is a decrease, the SOC decrease is accumulated; when the accumulated SOC decrease reaches the decrease threshold, it is recorded as a discharge cycle.

[0013] Further, the cumulative SOC increment is expressed as:

[0014] accumulated_soc_increment=accumulated_soc_increment+soc_increment

[0015] Among them, soc_increment is the difference determined as an increment, and accumulated_soc_increment is the accumulated SOC increment.

[0016] Furthermore, the cumulative SOC reduction is expressed as:

[0017] accumulated_soc_decrement=accumulated_soc_decrement+soc_decrement

[0018] Here, soc_decrement is the difference determined to be a decrease, and accumulated_soc_decrement is the accumulated SOC decrease.

[0019] Furthermore, it also includes normalizing and centering the data in the SOC data sequence.

[0020] Furthermore, it also includes using a Z-score detection method, a clustering-based anomaly detection method, or a deep learning-based autoencoder method to perform anomaly detection on the SOC data sequence and filter out abnormal data.

[0021] Furthermore, it also includes repairing the filtered abnormal data by using linear interpolation, spline interpolation or Kriging interpolation.

[0022] Furthermore, it also includes setting an alarm threshold, and when the SOC data in the SOC data sequence exceeds the alarm threshold, an alarm is issued.

[0023] A second aspect of the present invention provides a device for determining the number of battery charge and discharge cycles.

[0024] A device for determining the number of battery charge and discharge cycles, comprising:

[0025] A data acquisition module is configured to: acquire SOC data of the power battery at a number of consecutive moments to form an SOC data sequence;

[0026] A charging cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is an increment, accumulate the SOC increment; when the accumulated SOC increment reaches an increment threshold, it is recorded as a charging cycle;

[0027] The discharge cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is a decrease, accumulate the SOC decrease; when the accumulated SOC decrease reaches a decrease threshold, it is recorded as a discharge cycle.

[0028] A third aspect of the present invention provides a computer-readable storage medium.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in a method for determining the number of battery charge and discharge cycles as described in the first aspect above.

[0030] A fourth aspect of the present invention provides a computer device.

[0031] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for determining the number of battery charge and discharge cycles as described in the first aspect above are implemented.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention provides a cycle counting method based on SOC increment and decrement, which can accurately extract the number of charge and discharge cycles of the battery. This method not only takes into account the change of SOC, but also combines the calculation of cumulative increment and decrement, ensuring that the statistical results of the cycle number are more accurate and comprehensive, thereby improving the overall performance and reliability of the battery management system.

[0034] The present invention uses a variety of anomaly detection algorithms (such as Z-score detection, clustering algorithm and autoencoder) to efficiently and accurately identify anomalies in battery data and perform timely and effective processing, greatly improving the accuracy and reliability of battery data.

[0035] The present invention further ensures the integrity and consistency of data through advanced interpolation methods (such as spline interpolation and Kriging interpolation) in the data cleaning process, and reduces errors caused by abnormal data.

[0036] By real-time monitoring of the SOC and SOH data of the battery, the present invention can immediately trigger an alarm mechanism when an abnormal situation is detected, and can notify the user or operation and maintenance personnel in real time to handle the battery abnormal situation in time to ensure the safe operation of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 is a flow chart of a method for determining the number of battery charge and discharge cycles shown in Embodiment 1 of the present invention;

[0039] Figure 2 It is a structural diagram of a computer device shown in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the methods and devices according to various embodiments of the present invention. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the module, the program segment, or a part of the code may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based device that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0044] Embodiment 1

[0045] This embodiment provides a method for determining the number of battery charge and discharge cycles.

[0046] Currently, the battery information collected by cloud batteries in the industry is data directly uploaded by customers, and there is less observable information. Before other cloud functions are realized, such as SOC, SOH, and RUL estimation, corresponding data processing work needs to be done. The present invention provides a processing method with high generalizability.

[0047] This embodiment provides a method for determining the number of battery charge and discharge cycles, which is used in a cloud BMS, such as Figure 1 As shown, the following steps are included:

[0048] Step 1: Data collection and preprocessing.

[0049] Step 101, real-time data collection: collect the power battery data of the vehicle in real time through the battery management software in the vehicle controller, including voltage, current, temperature, SOC (State of Charge), SOH (State of Health), etc.; update the power battery data to the big database, and the big database directly imports the multi-dimensional data into the cloud BMS.

[0050] In this embodiment, a PHEV vehicle battery is specifically used, and the following are the basic parameters of the battery: voltage range: 2.5V-4.2V; current range: -100A-100A; temperature range: -20℃-60℃; SOC range: 0%-100%; SOH range: 60%-100%.

[0051] In this embodiment, the collected data includes voltage, current, temperature, SOC and SOH, etc. The data format is shown in Table 1.

[0052] Table 1. Power battery data

[0053] Timestamp Voltage (V) Current (A) Temperature(℃) SOC(%) SOH(%) 2024-06-01 12:00:00 3.7 2.5 25 80 95 2024-06-01 12:01:00 3.6 2.6 24 79 94 … … … … …

[0054] Step 102, data preprocessing: perform data normalization and centralization to improve the efficiency and accuracy of subsequent model training and optimization.

[0055] For example, the Python code could be:

[0056] from sklearn.preprocessing import StandardScaler

[0057] #Assume that data is the collected battery data

[0058] scaler = StandardScaler()

[0059] normalized_data=scaler.fit_transform(data)

[0060] Step 2: Abnormal data detection: Apply advanced anomaly detection algorithms to filter abnormal data in real time.

[0061] In this embodiment, an anomaly detection method based on statistical models and machine learning algorithms is adopted, including but not limited to Z-score detection method, clustering-based anomaly detection and deep learning-based autoencoder method.

[0062] The steps of the Z-score detection method include:

[0063] (1) For the battery data of the same vehicle at different times, calculate the data mean μ and standard deviation σ;

[0064]

[0065] (2) For each data point xi, calculate its Z score:

[0066]

[0067] (3) A threshold is set (for example, Z>3 or Z<-3), and points exceeding the threshold are considered abnormal data.

[0068] For example, the Python code could be:

[0069] import numpy as np

[0070] # Calculate mean and standard deviation

[0071] mean=np.mean(normalized_data,axis=0)

[0072] std_dev=np.std(normalized_data,axis=0)

[0073] # Calculate the Z score

[0074] z_scores=(normalized_data-mean) / std_dev

[0075] #Set threshold

[0076] threshold=3

[0077] anomalies=np.where(np.abs(z_scores)>threshold)

[0078] Step 3: Abnormal data cleaning, data interpolation and repair. Abnormal data is filtered in real time, and the detected abnormal data is repaired based on a variety of interpolation methods (such as linear interpolation, spline interpolation, Kriging interpolation, etc.).

[0079] Perform linear interpolation repair on the detected abnormal data: Let the data point x i ,x i+1 ,...,x i+k Medium i+1 For abnormal data points, linear interpolation is performed:

[0080]

[0081] In this embodiment, linear interpolation is used, and its Python code may be:

[0082] import pandas as pd

[0083] #Assume data_series is the time series of battery data

[0084] data_series=pd.Series(normalized_data)

[0085] #Mark abnormal data

[0086] for index in anomalies[0]:

[0087] data_series.iloc[index]=np.nan

[0088] #Perform linear interpolation

[0089] cleaned_data=data_series.interpolate(method='linear')

[0090] Step 4: Intelligent alarm.

[0091] The BMS system monitors battery data in real time and immediately triggers an alarm mechanism once an abnormality is detected. The alarm mechanism can include multiple alarm levels and response strategies to ensure that battery abnormalities are handled promptly and effectively.

[0092] Monitor battery data in real time, set alarm thresholds (such as SOC below 20% or SOH below 50%), monitor data streams, and immediately trigger an alarm mechanism when data anomalies are detected.

[0093] For example, set the SOC and SOH alarm thresholds, and when the SOC or SOH in the battery data exceeds the alarm threshold, an alarm is issued. The Python code can be:

[0094] #Set the alarm threshold

[0095] soc_threshold=0.05

[0096] soh_threshold=0.5

[0097] #Assume that current_soc and current_soh are the currently monitored SOC and SOH values

[0098] if current_soc <soc_threshold or current_soh<soh_threshold:

[0099] send_alert("SOC or SOH is abnormal, check the battery status immediately")

[0100] Step 5: Extract the number of cycles.

[0101] Since the BMS generally does not record the number of battery charge and discharge cycles, and consumers' usage is not ideal, it is very important to extract the number of charge and discharge cycles from the cloud data. In addition, the number of charge and discharge cycles will be input as an important characteristic value when estimating the battery SOH and RUL.

[0102] In this embodiment, a cycle technology based on SOC increment is used. It is assumed that the initial value of the cycle count is 0 when the vehicle rolls off the production line, and the number of charge and discharge cycles is extracted based on the SOC change threshold.

[0103] In this embodiment, a SOC increment threshold is set, for example, 5%; the SOC increment is accumulated, and when the accumulated SOC increment reaches 100%, it is recorded as a charging cycle. The specific steps are as follows:

[0104] Step 301, assuming that soc_data is an array of SOC data (SOC data sequence), with a value range of 0 to 1 (i.e., 0% to 100%), for example, soc_data = np.array([0.4, 0.42, 0.45, 0.48, 0.5, 0.52, 0.55, 0.58, 0.6, 0.62, 0.65, 0.68, 0.7, 0.72, 0.75, 0.78, 0.8]);

[0105] Step 302: define an SOC increment threshold, for example, soc_increment_threshold=0.05;

[0106] Step 303, initialize the cycle count and the accumulated SOC increment, that is, total_cycles=0, accumulated_soc_increment=0;

[0107] Step 304: record the initial SOC, previous_soc=soc_data[0];

[0108] Step 305: traverse the SOC data sequence and count the number of cycles.

[0109] Extract the SOC data in the SOC data sequence in order, calculate the difference soc_increment between the currently extracted SOC data current_soc and the last extracted SOC data previous_soc, if the difference is an increment, then accumulate the SOC increment accumulated_soc_increment; when the accumulated SOC increment reaches the increment threshold, it is recorded as a charging cycle. Specifically:

[0110] for current_soc in soc_data[1:]:

[0111] #Calculate the difference between the current SOC and the previous SOC

[0112] soc_increment=current_soc-previous_soc

[0113] #If it is an increment, the cumulative SOC increment

[0114] if soc_increment>0:

[0115] accumulated_soc_increment+=soc_increment

[0116] #When the accumulated SOC increment reaches the increment threshold, it is counted as a charging cycle

[0117] if accumulated_soc_increment>=soc_increment_threshold:

[0118] total_cycles1+=1

[0119] accumulated_soc_increment-=soc_increment_threshold# reset the accumulated increment

[0120] previous_soc=current_soc

[0121] In this embodiment, a SOC reduction threshold is set, for example, 5%; the accumulated SOC reduction is calculated, and when the accumulated SOC reduction reaches 100%, it is recorded as a discharge cycle. The specific steps are as follows:

[0122] Step 301, #assuming soc_data is an array of SOC data (SOC data sequence), with a value range of 0 to 1 (i.e., 0% to 100%), for example, soc_data = np.array([0.8, 0.78, 0.75, 0.72, 0.7, 0.68, 0.65, 0.62, 0.6, 0.58, 0.55, 0.52, 0.5, 0.48, 0.45, 0.42, 0.4]);

[0123] Step 302, define the SOC decrement threshold, soc_decrement_threshold=0.05;

[0124] Step 303, initialize the cycle count and the accumulated SOC decrement, that is, total_cycles=0, accumulated_soc_decrement=0;

[0125] Step 304: record the initial SOC, previous_soc=soc_data[0];

[0126] Step 305: traverse the SOC data sequence and count the number of cycles.

[0127] Extract the SOC data in the SOC data sequence in order, calculate the difference soc_decrement between the currently extracted SOC data current_soc and the last extracted SOC data previous_soc, if the difference is a decrease, the accumulated SOC decrease is accumulated_soc_decrement; when the accumulated SOC decrease reaches the decrease threshold, it is recorded as a discharge cycle. Specifically:

[0128] #Traverse the data and count the number of loops

[0129] for current_soc in soc_data[1:]:

[0130] #Calculate the difference between the current SOC and the previous SOC

[0131] soc_decrement=previous_soc-current_soc

[0132] #If it is a reduction, the cumulative SOC reduction

[0133] if soc_decrement>0:

[0134] accumulated_soc_decrement+=soc_decrement

[0135] #When the accumulated SOC reduction reaches the reduction threshold, it is recorded as a discharge cycle

[0136] if accumulated_soc_decrement>=soc_decrement_threshold:

[0137] total_cycles2+=1

[0138] accumulated_soc_decrement-=soc_decrement_threshold# reset accumulated decrement

[0139] previous_soc=current_soc

[0140] After effectively extracting the number of charge and discharge cycles, the aging of the battery can be calculated based on the number of charge and discharge cycles. Instead of using a brand new battery, it is simpler and more efficient to record the number of battery cycles in a special battery test laboratory to estimate the aging.

[0141] The method for determining the number of battery charge and discharge cycles provided in this embodiment can efficiently and accurately identify abnormal points in battery data through multiple abnormality detection algorithms (such as Z-score detection, clustering algorithm and autoencoder), and perform timely and effective processing, thereby greatly improving the accuracy and reliability of battery data.

[0142] The present embodiment provides a method for determining the number of battery charge and discharge cycles, which further ensures the integrity and consistency of data through advanced interpolation methods (such as spline interpolation and Kriging interpolation) in the data cleaning process, and reduces errors caused by abnormal data.

[0143] The present embodiment provides a method for determining the number of battery charge and discharge cycles. By real-time monitoring of the SOC and SOH data of the battery, an alarm mechanism can be triggered immediately when an abnormal situation is detected, and the user or operation and maintenance personnel can be notified in real time to handle the battery abnormal situation in a timely manner to ensure the safe operation of the battery.

[0144] This embodiment provides a method for determining the number of battery charge and discharge cycles. The intelligent alarm can set multiple alarm levels and response strategies according to different abnormal situations, and promptly remind users or maintenance personnel to ensure the safety of battery use and maintenance efficiency.

[0145] The present embodiment provides a method for determining the number of battery charge and discharge cycles, and provides a cycle counting method based on SOC increments and decrements, which can accurately extract the number of battery charge and discharge cycles. This method not only takes into account the change in SOC, but also combines the calculation of cumulative increments and decrements, ensuring that the statistical results of the number of cycles are more accurate and comprehensive, thereby improving the overall performance and reliability of the battery management system.

[0146] This embodiment provides a method for determining the number of battery charge and discharge cycles, in which the SOC increment and decrement thresholds are set to 5% (modifiable), which can flexibly adapt to the working conditions and states of different batteries and help to accurately evaluate the health status and life of the battery.

[0147] The present embodiment provides a method for determining the number of battery charge and discharge cycles, which can significantly improve the performance of the BMS through efficient preprocessing and intelligent analysis of battery data, enabling it to more accurately judge the working status and health status of the battery, thereby optimizing the battery charge and discharge strategy and life management.

[0148] This embodiment provides a method for determining the number of battery charge and discharge cycles. High-quality data preprocessing and intelligent analysis help improve the decision-making ability and response speed of the BMS, thereby achieving more efficient battery management and energy utilization.

[0149] The method for determining the number of battery charge and discharge cycles provided in this embodiment is applicable to various types of battery data, including but not limited to electric vehicles, energy storage systems, consumer electronic devices and other fields, and has broad application prospects and market value.

[0150] The method for determining the number of battery charge and discharge cycles provided in this embodiment can be easily integrated into the existing cloud computing platform and big data analysis system through cloud processing and storage, so as to realize real-time analysis and intelligent management of data.

[0151] Embodiment 2

[0152] This embodiment provides a device for determining the number of battery charge and discharge cycles.

[0153] A device for determining the number of battery charge and discharge cycles, comprising:

[0154] A data acquisition module is configured to: acquire SOC data of the power battery at a number of consecutive moments to form an SOC data sequence;

[0155] A charging cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is an increment, accumulate the SOC increment; when the accumulated SOC increment reaches an increment threshold, it is recorded as a charging cycle;

[0156] The discharge cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is a decrease, accumulate the SOC decrease; when the accumulated SOC decrease reaches a decrease threshold, it is recorded as a discharge cycle.

[0157] It should be noted that the above embodiment provides a device for determining the number of battery charge and discharge cycles, and only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device is divided into different functional modules to complete all or part of the functions described above. In addition, the device for determining the number of battery charge and discharge cycles provided in the above embodiment and the method for determining the number of battery charge and discharge cycles are of the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0158] Embodiment 3

[0159] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for determining the number of battery charge and discharge cycles as described in the first embodiment above are implemented.

[0160] Embodiment 4

[0161] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for determining the number of battery charge and discharge cycles as described in the first embodiment are implemented.

[0162] Figure 2 The schematic diagram of the structure of a computer device provided by an exemplary embodiment of the present application is shown. The computer device includes: a processor and a memory.

[0163] The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field Programmable Gate Array), and PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0164] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one computer program, which is used to be used by the processor to implement the vehicle occupant detection method provided in the method embodiment of the present application.

[0165] Those skilled in the art will appreciate that the illustrated structure does not constitute a limitation on the computer device, and may include more or fewer components than illustrated, or combine certain components, or adopt a different component arrangement.

[0166] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining the number of battery charge and discharge cycles, characterized in that: include: Obtain the SOC data of the power battery at several consecutive moments to form an SOC data sequence; Extract the SOC data in the SOC data sequence in order, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is an increment, accumulate the SOC increment; When the accumulated SOC increment reaches the increment threshold, it is counted as one charging cycle; The SOC data in the SOC data sequence is extracted in sequence, and the difference between the currently extracted SOC data and the last extracted SOC data is calculated. If the difference is a decrease, the SOC decrease is accumulated; when the accumulated SOC decrease reaches the decrease threshold, it is recorded as a discharge cycle.

2. A method for determining the number of battery charge and discharge cycles according to claim 1, characterized in that: The cumulative SOC increment is expressed as: accumulated_soc_increment=accumulated_soc_increment+soc_increment Among them, soc_increment is the difference determined as an increment, and accumulated_soc_increment is the accumulated SOC increment.

3. A method for determining the number of battery charge and discharge cycles according to claim 1, characterized in that: The cumulative SOC reduction is expressed as: accumulated_soc_decrement=accumulated_soc_decrement+soc_decrement Here, soc_decrement is the difference determined to be a decrease, and accumulated_soc_decrement is the accumulated SOC decrease.

4. A method for determining the number of battery charge and discharge cycles according to claim 1, characterized in that: It also includes normalizing and centering the data in the SOC data sequence.

5. A method for determining the number of battery charge and discharge cycles according to claim 1, characterized in that: It also includes using Z-score detection method, clustering-based anomaly detection method or deep learning-based automatic encoder method to detect anomalies in SOC data sequences and filter out abnormal data.

6. A method for determining the number of battery charge and discharge cycles according to claim 5, characterized in that: It also includes repairing the filtered abnormal data using linear interpolation, spline interpolation or Kriging interpolation.

7. A method for determining the number of battery charge and discharge cycles according to claim 1, characterized in that: It also includes setting an alarm threshold, and issuing an alarm when the SOC data in the SOC data sequence exceeds the alarm threshold.

8. A device for determining the number of battery charge and discharge cycles, characterized in that: include: A data acquisition module is configured to: acquire SOC data of the power battery at a number of consecutive moments to form an SOC data sequence; A charging cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is an increment, accumulate the SOC increment; when the accumulated SOC increment reaches an increment threshold, it is recorded as a charging cycle; The discharge cycle determination module is configured to: sequentially extract SOC data in the SOC data sequence, calculate the difference between the currently extracted SOC data and the last extracted SOC data, and if the difference is a decrease, accumulate the SOC decrease; when the accumulated SOC decrease reaches a decrease threshold, it is recorded as a discharge cycle.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in a method for determining the number of battery charge and discharge cycles as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for determining the number of battery charge and discharge cycles as described in any one of claims 1-7 are implemented.

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