A lithium battery capacity compensation method and system, electronic device and medium thereof
By constructing a lithium battery capacity compensation model and using the correlation between voltage and temperature data, the problem of temperature fluctuations affecting capacity calibration was solved, resulting in more accurate capacity estimation and improved data reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING SANYUANJU TECH CO LTD
- Filing Date
- 2022-12-26
- Publication Date
- 2026-07-10
AI Technical Summary
During the formation and capacity testing of lithium batteries, temperature fluctuations cause changes in the activity of chemical substances, affecting the capacity curve. This leads to deviations in the cell capacity calibration during the formation and capacity testing stages from the actual capacity, reducing data reliability.
By constructing a capacity compensation model, a correlation model is established using the voltage and temperature data of lithium batteries. The model outputs the influence rate of temperature on capacity increment, determines the target capacity, and compensates the capacity curve, thereby improving data reliability.
This makes the estimated capacity of the battery cells closer to the actual capacity, improving the reliability of the data generated during the formation and capacity testing stages.
Smart Images
Figure CN115980587B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of lithium battery technology and relates to a lithium battery capacity compensation method, system, electronic device and its medium. Background Technology
[0002] In the battery manufacturing process, battery cells undergo formation and capacity testing before leaving the factory. Formation aims to initiate the chemical reaction for charging. If a lithium battery skips formation and is directly charged normally, excessive constant current may damage the battery. Therefore, formation is performed first to activate the active materials within the battery before normal charging. Capacity testing involves charging and discharging the lithium battery cells during production to determine the cell's capacity based on its discharge capacity at full charge. Both the formation and capacity testing stages generate a large amount of cell data, such as current, voltage, temperature, and capacity curves at various time points. This data is used in big data analysis and prediction, providing valuable information such as battery quality performance, faults, capacity calibration, and health status. However, during the formation and capacity testing of batteries, the temperature fluctuates significantly. Temperature fluctuations from 25 degrees to 35 degrees are very common on some production lines. As a result, the activity of chemical substances in lithium batteries changes, affecting the capacity curve. This causes the calibration of the cell capacity during the formation and capacity testing stages to deviate from the actual capacity of the cell. These biased data will also interfere with subsequent cell data analysis and modeling, thereby reducing the reliability of the data generated during the capacity testing and formation stages. Summary of the Invention
[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a lithium battery capacity compensation method, system, electronic device, and medium, so that the estimated capacity of the battery cell can be closer to the actual capacity of the battery cell, thereby improving the reliability of the data generated during the capacity assessment and formation stages.
[0004] To achieve the above objectives, the present invention adopts the following technical solution:
[0005] The first aspect of this invention is to provide a lithium battery capacity compensation method, comprising the following steps:
[0006] Obtain the current feature dataset of the lithium battery to be compensated at different time points, the current feature dataset including: current voltage dataset and current temperature dataset;
[0007] Input the current feature dataset into the pre-built capacity compensation model and output the rate of influence of temperature on capacity increment;
[0008] The target capacity to be compensated is determined based on the rate of influence of temperature on capacity increment.
[0009] The target capacity to be compensated is applied to the pre-obtained current capacity curve to obtain the compensated target capacity curve.
[0010] Furthermore, the specific method for constructing the capacity compensation model is as follows:
[0011] Collect historical voltage and temperature data during the charging process of the same type of lithium battery;
[0012] The historical voltage data is divided into multiple consecutive voltage intervals, and the historical starting voltage value and historical ending voltage value of each interval are determined.
[0013] Based on the historical starting voltage value and the historical ending voltage value, determine the historical first capacity and historical first temperature data corresponding to the starting voltage and the historical second capacity and historical second temperature data corresponding to the ending voltage in each interval;
[0014] Based on the first historical capacity and the second historical capacity, determine the corresponding capacity increment data for each interval; based on the first historical temperature data and the second historical temperature data, determine the temperature change data.
[0015] Based on the capacity increment data and the temperature change data, establish a correlation model between the capacity increment data and the temperature change data;
[0016] Regression analysis was performed on the aforementioned correlation model to obtain the correlation curve between the capacity increment data and the temperature change data;
[0017] All the correlation curves are integrated to generate a capacity compensation model.
[0018] Furthermore, the current feature dataset is input into a pre-built capacity compensation model, and the output voltage dataset shows the rate of influence of temperature on capacity increment, specifically including:
[0019] Based on the current voltage dataset, determine the voltage range corresponding to each voltage data point in the current voltage dataset;
[0020] Based on the voltage range corresponding to each voltage data point, determine the current temperature data corresponding to each voltage data point;
[0021] Input the current temperature data corresponding to each voltage data point into a pre-built capacity compensation model, and output the influence rate of temperature on capacity increment in the voltage data set.
[0022] Furthermore, determining the target capacity to be compensated based on the rate of influence of temperature on capacity increment specifically includes:
[0023] Divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and current ending voltage value for each interval;
[0024] Based on the current starting voltage value and the current ending voltage value, determine the current voltage increment for each interval;
[0025] Multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
[0026] Furthermore, before dividing the historical voltage data into multiple consecutive voltage intervals and determining the historical starting voltage value and historical ending voltage value of each interval, the method further includes: establishing a voltage-time correlation curve based on the obtained voltage dataset.
[0027] A second aspect of the present invention is to provide a lithium battery capacity compensation system, characterized in that it comprises:
[0028] The acquisition module is used to acquire the current feature dataset of the lithium battery to be compensated at different time points, wherein the current feature dataset includes the current voltage dataset and the current temperature dataset.
[0029] The input / output module is used to input the current feature dataset into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment.
[0030] The first determining module is used to determine the target capacity to be compensated based on the influence rate of temperature on capacity increment.
[0031] An application module is used to apply the target capacity to be compensated to a pre-obtained current capacity curve to obtain a compensated target capacity curve.
[0032] Furthermore, the input / output module includes:
[0033] The second determining module is used to determine the voltage range corresponding to each voltage data in the current voltage dataset based on the current voltage dataset.
[0034] The third determining module is used to determine the current temperature data corresponding to each voltage data according to the voltage range corresponding to each voltage data.
[0035] The input / output submodule is used to input the current temperature data corresponding to each voltage data into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment in the voltage data.
[0036] Furthermore, the input / output module includes:
[0037] A partitioning module is used to divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and the current ending voltage value of each interval.
[0038] The first sub-determination module is used to determine the current voltage increment of each interval based on the current starting voltage value and the current ending voltage value.
[0039] The calculation module is used to multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
[0040] A third aspect of the present invention is to provide an electronic device, characterized in that the electronic device includes at least one processor; and,
[0041] A memory communicatively connected to the at least one processor; wherein,
[0042] The memory stores instructions that can be executed by the at least one processor, which, when executed, enable the at least one processor to perform the lithium battery capacity compensation method.
[0043] A fourth aspect of the present invention is to provide a non-volatile computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the lithium battery capacity compensation method.
[0044] The beneficial effects of this invention are:
[0045] By acquiring the current feature dataset of the lithium battery to be compensated at different time points, including the current voltage dataset and the current temperature dataset; inputting the current feature dataset into a pre-built capacity compensation model, the model outputs the influence rate of temperature on capacity increment; determining the target capacity to be compensated based on the influence rate of temperature on capacity increment; and applying the target capacity to be compensated to the pre-obtained current capacity curve to obtain the compensated target capacity curve. This approach can solve the problem of discrepancies between the actual capacity and the estimated capacity caused by temperature changes, making the estimated capacity of the battery cell closer to its actual capacity and improving the reliability of the data generated during the capacity grading and formation stages. Attached Figure Description
[0046] Appendix Figure 1 This is a schematic diagram of the overall process of the lithium battery capacity compensation method in this invention;
[0047] Appendix Figure 2 This is a schematic diagram of the structure of the present invention;
[0048] Appendix Figure 3 This is a schematic diagram illustrating the change of cell voltage over time without capacity compensation in this invention;
[0049] Appendix Figure 4 This is a distribution image showing the correlation between temperature and capacity increment data in this invention;
[0050] Appendix Figure 5 This is the correlation curve between capacity increment data and temperature change data in this invention;
[0051] Appendix Figure 6 This is a schematic diagram illustrating the change in capacity over time before and after capacity compensation in this invention;
[0052] Appendix Figure 7 It is attached Figure 6 A magnified diagram illustrating the change in capacity over time before and after capacity compensation.
[0053] Appendix Figure 8 This is a schematic diagram of the electronic device in this invention. Detailed Implementation
[0054] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0055] During the battery formation and capacity testing process, temperature fluctuations from 25°C to 35°C are very common on a certain production line. This alters the activity of the chemical substances in the lithium battery, affecting the capacity curve and causing the actual electrode potential to deviate from the equilibrium electrode potential. Consequently, the calibrated cell capacity during formation and capacity testing deviates from the cell's true capacity. Therefore, the first aspect of this invention is to provide a lithium battery capacity compensation method, as detailed in the appendix. Figure 1 The method of the present invention includes the following steps:
[0056] S100: Obtain the current feature dataset of the lithium battery to be compensated at different time points, wherein the current feature dataset includes the current voltage dataset and the current temperature dataset;
[0057] It should be noted that the current voltage dataset is a set of corresponding capacity values of lithium batteries at different times during charging, and the current temperature dataset is a set of corresponding temperature values of lithium batteries at different times during charging.
[0058] S200: Input the current feature dataset into the pre-built capacity compensation model and output the influence rate of temperature on capacity increment;
[0059] S300: Determine the target capacity to be compensated based on the rate of influence of temperature on capacity increment;
[0060] S400: Apply the target capacity to be compensated to the pre-obtained current capacity curve to obtain the compensated target capacity curve.
[0061] By acquiring the current feature dataset of the lithium battery to be compensated at different time points, including the current voltage dataset and the current temperature dataset; inputting the current feature dataset into a pre-built capacity compensation model, the model outputs the influence rate of temperature on capacity increment; determining the target capacity to be compensated based on the influence rate of temperature on capacity increment; and applying the target capacity to be compensated to the pre-obtained current capacity curve to obtain the compensated target capacity curve. This approach can solve the problem of discrepancies between the actual capacity and the estimated capacity caused by temperature changes, making the estimated capacity of the battery cell closer to its actual capacity and improving the reliability of the data generated during the capacity grading and formation stages.
[0062] In one embodiment, the specific method for constructing the capacity compensation model is as follows:
[0063] Collect historical voltage and temperature data during the charging process of the same type of lithium battery;
[0064] When building the model, it is necessary to obtain a large number of battery cells of the same model as samples. Therefore, in this technical solution, temperature data and voltage data of no less than 10,000 battery cells of the same model are randomly extracted for modeling to make the model more accurate. It should be understood that historical voltage data and historical temperature data are both datasets.
[0065] The historical voltage data is divided into multiple consecutive voltage intervals, and the historical starting voltage value and historical ending voltage value of each interval are determined.
[0066] In this model construction, the voltage data is divided into multiple consecutive voltage intervals. This facilitates easy lookup for subsequent cell compensation, allowing the model to be found and used within the corresponding small interval based on the data points. In this embodiment, the capacity is divided into 20 intervals, with starting voltages of 0mV, 300mV, 600mV...5700, 6000mV. Each interval is divided into 300mV intervals, and the ending voltage of each interval is the same as the starting voltage of the next interval. For example, the first voltage interval is 0mV-300mV, and the second voltage interval is 300mV-600mV.
[0067] Based on the historical starting voltage value and the historical ending voltage value, determine the historical first capacity and historical first temperature data corresponding to the starting voltage in each interval, and the historical second capacity and historical second temperature data corresponding to the ending voltage; it should be noted that the historical first temperature data and the historical second temperature data are obtained from the historical temperature data.
[0068] When the starting and ending voltage values for each interval are determined, although the length of each interval is the same, the capacity of the battery cell increases continuously during charging until it reaches the maximum voltage value of the battery cell; the capacity at the starting and ending voltage values will also be different during the charging process.
[0069] Based on the first historical capacity and the second historical capacity, determine the corresponding capacity increment data for each interval; based on the first historical temperature data and the second historical temperature data, determine the temperature change data.
[0070] It should be noted that the capacity increment data for each interval = historical second capacity - historical first capacity; the temperature change data = (historical first temperature data + historical second temperature data) / 2. In some other embodiments, the temperature change data can also be set to the historical first temperature data corresponding to the starting voltage value of each interval, and the temperature change data can also be set to the historical second temperature data corresponding to the ending voltage value of each interval.
[0071] Based on the capacity increment data and the temperature change data, establish a correlation model between the capacity increment data and the temperature change data;
[0072] Once the capacity increment data is obtained, the corresponding capacity increment data can be determined based on the historical temperature data. The historical temperature data is used as the horizontal axis of the model, and the capacity increment data is used as the vertical axis of the model to establish a correlation model between the capacity increment data and the temperature change data.
[0073] Reference Appendix Figure 4 - Appendix Figure 7 The correlation model is then subjected to regression analysis to obtain the correlation curve between the capacity increment data and the temperature change data.
[0074] Because a large amount of sample data was used when building the model, it is possible to obtain a distribution image of the correlation between temperature and capacity increment data within a certain temperature range, which is the correlation model obtained above. At this time, regression analysis of the correlation model can be performed to obtain the correlation curve between capacity increment data and temperature change data.
[0075] All the correlation curves are integrated to generate a capacity compensation model.
[0076] Since a correlation curve between capacity increment data and temperature change data can be obtained for each voltage range, it is necessary to integrate the correlation curves obtained from all the voltage ranges to obtain the capacity compensation model.
[0077] It should be noted that the effect of temperature on capacity increment is not perfectly linear; please refer to the appendix. Figure 5 Based on extensive data, there is an inflection point around 26.5 degrees Celsius, where the slope after the inflection point is less than the slope before it. Therefore, the model attenuates the slope after this point (as shown by the dotted line in the figure below). The slope of the entire line after attenuation represents the rate of temperature's influence on capacity increment. In this embodiment, the attenuation process involves directly multiplying the slope after this point by the attenuation rate.
[0078] In one embodiment, the current feature dataset is input into a pre-built capacity compensation model, and the output voltage dataset shows the rate of influence of temperature on capacity increment, specifically including:
[0079] S210: Based on the current voltage dataset, determine the voltage range corresponding to each voltage data point in the current voltage dataset;
[0080] S220: Determine the current temperature data corresponding to each voltage data based on the voltage range corresponding to each voltage data.
[0081] S230: Input the current temperature data corresponding to each voltage data into the pre-built capacity compensation model, and output the influence rate of the temperature in the voltage data on the capacity increment.
[0082] In one embodiment, determining the target capacity to be compensated based on the rate of influence of temperature on capacity increment specifically includes:
[0083] S310: Divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and current ending voltage value for each interval.
[0084] By dividing the current voltage dataset into multiple voltage ranges, the compensation effect can be made more granular and closer to the true value, thereby improving the accuracy of capacity compensation.
[0085] It should be understood that in step S310, the size of the divided voltage range is set to be the same as the size of the divided voltage range during the establishment of the capacity compensation model.
[0086] S320: Determine the current voltage increment for each interval based on the current starting voltage value and the current ending voltage value.
[0087] S330: Multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
[0088] It should be noted that the current temperature change data = (current first temperature data + current second temperature data) / 2, where the current first temperature data is the temperature data corresponding to the current starting voltage value, and the current second temperature data is the temperature data corresponding to the current ending voltage value. In some other embodiments, the temperature change data can also be set to the current first temperature data corresponding to the starting voltage value of each interval, and the temperature change data can also be set to the current second temperature data corresponding to the ending voltage value of each interval.
[0089] Reference Appendix Figure 3 In one embodiment, before dividing the historical voltage data into multiple consecutive voltage intervals and determining the historical starting voltage value and historical ending voltage value of each interval, the method further includes: establishing a voltage-time correlation curve based on the obtained voltage dataset.
[0090] Since the voltage and capacity of lithium batteries change over time during pre-charging or discharging, it is necessary to establish a voltage-time correlation curve in order to better understand the voltage characteristics and trends of the target battery and ensure the accuracy of compensation.
[0091] Reference Appendix Figure 2 A second aspect of the present invention is to provide a lithium battery capacity compensation system, characterized in that it comprises:
[0092] The acquisition module is used to acquire the current feature dataset of the lithium battery to be compensated at different time points, wherein the current feature dataset includes the current voltage dataset and the current temperature dataset.
[0093] The input / output module is used to input the current feature dataset into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment.
[0094] The first determining module is used to determine the target capacity to be compensated based on the influence rate of temperature on capacity increment.
[0095] An application module is used to apply the target capacity to be compensated to a pre-obtained current capacity curve to obtain a compensated target capacity curve.
[0096] By setting up an acquisition module, an input / output module, a first determination module, and an application module, the acquisition module is used to acquire the current feature dataset of the lithium battery to be compensated at different time points, wherein the current feature dataset includes the current voltage dataset and the current temperature dataset; the input / output module is used to input the current feature dataset into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment; the determination module is used to determine the target capacity to be compensated based on the influence rate of temperature on capacity increment; the application module is used to apply the target capacity to be compensated to the pre-obtained current capacity curve to obtain the compensated target capacity curve; this can solve the problem of inconsistency between the actual capacity and the estimated capacity caused by temperature changes, making the estimated capacity of the cell closer to the actual capacity of the cell, and improving the reliability of the data generated by the cell during the capacity grading and formation stages.
[0097] In one embodiment, the input / output module includes:
[0098] The second determining module is used to determine the voltage range corresponding to each voltage data in the current voltage dataset based on the current voltage dataset.
[0099] The third determining module is used to determine the current temperature data corresponding to each voltage data according to the voltage range corresponding to each voltage data.
[0100] The input / output submodule is used to input the current temperature data corresponding to each voltage data into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment in the voltage data.
[0101] In one embodiment, the input / output module includes:
[0102] A partitioning module is used to divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and the current ending voltage value of each interval.
[0103] The first sub-determination module is used to determine the current voltage increment of each interval based on the current starting voltage value and the current ending voltage value.
[0104] The calculation module is used to multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
[0105] Reference Appendix Figure 8 A third aspect of the present invention is to provide an electronic device,
[0106] It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the lithium battery capacity compensation method.
[0107] Memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for the functions, etc.; the data storage area can store data created based on the use of the device, etc. Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.
[0108] The internal structure of the computer device may include, but is not limited to, a processor, a network interface, and a memory. The processor, network interface, and memory within the in-vehicle occupant health monitoring terminal can be connected via a bus or other means, as illustrated in the embodiments of this specification. Figure 8 Taking the example of a connection between China and Israel via a bus.
[0109] The processor (or CPU, Central Processing Unit) is the computing and control core of the in-vehicle occupant health monitoring terminal. The network interface may optionally include a standard wired interface or a wireless interface (such as Wi-Fi, mobile communication interface, etc.). Memory is a storage device in a computer device used to store programs and data. It is understood that the memory here can be a high-speed RAM storage device or a non-volatile memory device, such as at least one disk storage device; optionally, it may also be at least one storage device located remotely from the aforementioned processor. The memory provides storage space, which stores the operating system of the computer device, including but not limited to: Windows (an operating system), Linux (an operating system), etc., which are not limited in this invention; and the storage space also stores one or more instructions suitable for loading and execution by the processor, which can be one or more computer programs (including program code). In the embodiments of this specification, the processor loads and executes one or more instructions stored in the memory to implement the lithium battery capacity compensation method provided in the above method embodiments.
[0110] Embodiments of the present invention also provide a computer-readable storage medium, which can be disposed in an electronic terminal device to store at least one instruction, at least one program, code set, or instruction set related to implementing the lithium battery capacity compensation method in the method embodiments. The at least one instruction, the at least one program, the code set, or the instruction set can be loaded and executed by the processor of the electronic device to implement the lithium battery capacity compensation method provided in the above method embodiments.
[0111] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0112] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, and of course, it can also be implemented using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can exist in a computer-readable storage medium, such as ROM / RAM, disk, optical disk, etc., including several methods for causing a computer electronic device (which may be a personal computer, server, or network electronic device, etc.) to execute the various embodiments or some parts of the embodiments.
[0113] Among other things, conditional language such as “can,” “may,” “may,” or “may,” unless otherwise specifically stated or otherwise understood in the context in which they are used, is generally intended to convey that a particular implementation may include (but not others) certain features, elements, and / or operations. Therefore, such conditional language is also generally intended to imply that features, elements, and / or operations are necessary for one or more implementations in any way, or that one or more implementations must include logic for determining, with or without input or prompting, whether such features, elements, and / or operations are included or will be performed in any particular implementation.
[0114] The embodiments described above are merely one of the preferred embodiments of the present invention. Ordinary variations and substitutions made by those skilled in the art within the scope of the technical solutions of the present invention should be included within the protection scope of the present invention.
Claims
1. A lithium battery capacity compensation method, characterized in that, Includes the following steps: Obtain the current feature dataset of the lithium battery to be compensated at different time points, the current feature dataset including: current voltage dataset and current temperature dataset; Input the current feature dataset into the pre-built capacity compensation model and output the rate of influence of temperature on capacity increment; The target capacity to be compensated is determined based on the rate of influence of temperature on capacity increment. The target capacity to be compensated is applied to the pre-obtained current capacity curve to obtain the compensated target capacity curve. The specific method for constructing the capacity compensation model is as follows: Collect historical voltage and temperature data during the charging process of the same type of lithium battery; The historical voltage data is divided into multiple consecutive voltage intervals, and the historical starting voltage value and historical ending voltage value of each interval are determined. Based on the historical starting voltage value and the historical ending voltage value, determine the historical first capacity and historical first temperature data corresponding to the starting voltage and the historical second capacity and historical second temperature data corresponding to the ending voltage in each interval; Based on the first historical capacity and the second historical capacity, determine the corresponding capacity increment data for each interval; based on the first historical temperature data and the second historical temperature data, determine the temperature change data. Based on the capacity increment data and the temperature change data, establish a correlation model between the capacity increment data and the temperature change data; Regression analysis was performed on the aforementioned correlation model to obtain the correlation curve between the capacity increment data and the temperature change data; All the correlation curves are integrated to generate a capacity compensation model.
2. The lithium battery capacity compensation method according to claim 1, characterized in that, The current feature dataset is input into a pre-built capacity compensation model, which outputs the rate of influence of temperature on capacity increment in the voltage dataset, specifically including: Based on the current voltage dataset, determine the voltage range corresponding to each voltage data point in the current voltage dataset; Based on the voltage range corresponding to each voltage data point, determine the current temperature data corresponding to each voltage data point; Input the current temperature data corresponding to each voltage data point into a pre-built capacity compensation model, and output the influence rate of temperature on capacity increment in the voltage data set.
3. The lithium battery capacity compensation method according to claim 1, characterized in that, The step of determining the target capacity to be compensated based on the rate of influence of temperature on capacity increment specifically includes: Divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and current ending voltage value for each interval; Based on the current starting voltage value and the current ending voltage value, determine the current voltage increment for each interval; Multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
4. The lithium battery capacity compensation method according to claim 1, characterized in that, Before dividing the historical voltage data into multiple continuous voltage intervals and determining the historical starting voltage value and historical ending voltage value of each interval, the method further includes: establishing a voltage-time correlation curve based on the obtained voltage dataset.
5. A lithium battery capacity compensation system, characterized in that, The system uses the lithium battery voltage compensation method according to claim 1, including: The acquisition module is used to acquire the current feature dataset of the lithium battery to be compensated at different time points. The current feature dataset includes: current voltage dataset and current temperature dataset. The input / output module is used to input the current feature dataset into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment. The first determining module is used to determine the target capacity to be compensated based on the influence rate of temperature on capacity increment. An application module is used to apply the target capacity to be compensated to a pre-obtained current capacity curve to obtain a compensated target capacity curve.
6. A lithium battery capacity compensation system according to claim 5, characterized in that, The input / output module includes: The second determining module is used to determine the voltage range corresponding to each voltage data in the current voltage dataset based on the current voltage dataset. The third determining module is used to determine the current temperature data corresponding to each voltage data according to the voltage range corresponding to each voltage data. The input / output submodule is used to input the current temperature data corresponding to each voltage data into a pre-built capacity compensation model and output the influence rate of temperature on capacity increment in the voltage data.
7. A lithium battery capacity compensation system according to claim 5, characterized in that, The input / output module includes: A partitioning module is used to divide the current voltage dataset into multiple voltage intervals and determine the current starting voltage value and the current ending voltage value of each interval. The first sub-determination module is used to determine the current voltage increment of each interval based on the current starting voltage value and the current ending voltage value. The calculation module is used to multiply the current voltage increment by the rate of temperature effect on capacity increment to obtain the target capacity to be compensated.
8. An electronic device, characterized in that, The electronic device includes at least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the lithium battery capacity compensation method according to any one of claims 1-4.
9. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the lithium battery capacity compensation method according to any one of claims 1-4.