Method and system for calculating battery capacity on the basis of charging data, terminal, and medium

Abstract

Provided in the present invention are a method and system for calculating battery capacity on the basis of charging data, a terminal and a medium. The method comprises: first collecting battery data for each charge and cleaning same to eliminate invalid information; then extracting from the cleaned data charging process data that meets a valid charging cycle, and, on this basis, calculating a single capacity for each charge; then using the single capacity values to calculate corresponding equivalent SOHs, and then, by means of sliding window technology, traversing the equivalent SOHs arranged in a time sequence to acquire a normal equivalent SOH in each window; and finally, on the basis of sliding window data that meets a sample quantity requirement, calculating the SOHs of a target battery at different time points. Compared with existing solutions, the present invention is not only simple and convenient to operate, but also applicable to various complex scenarios in battery swap stations, and can accurately and effectively obtain SOH results of batteries.

Description

Methods, systems, terminals, and media for calculating battery capacity based on charging data Technical Field

[0001] This invention relates to the field of battery health technology, and in particular to a method, system, terminal, and medium for calculating battery capacity based on charging data. Background Technology

[0002] Battery State of Health (SOH) is a core indicator for measuring battery performance and expected lifespan. Obtaining the true SOH of a battery is crucial for ensuring the safe, reliable, and efficient operation of battery systems and optimizing the operational profitability of battery swapping stations. First, the SOH directly impacts battery safety; batteries in poor health may lead to overheating, swelling, or even explosions. Accurate SOH understanding can effectively prevent these risks. Second, SOH reflects the battery's remaining capacity and charge / discharge efficiency, serving as a key basis for optimizing battery usage, extending lifespan, and improving equipment performance. Third, by monitoring SOH, signs of battery aging or failure can be detected promptly, enabling preventative maintenance, reducing unexpected failures, and lowering maintenance costs. In applications such as electric vehicles and energy storage systems, the battery is a core component, and its SOH directly affects the system's reliability and stability. Accurate SOH assessment helps prevent system downtime or performance degradation. Furthermore, SOH can help predict the remaining battery lifespan, guiding users to replace batteries in a timely manner and preventing performance degradation from affecting system operation. For battery operators and managers, SOH data is an important reference for formulating operational strategies, optimizing management plans, and making investment decisions. In particular, the State of Health (SOH) results directly affect revenue in the operation of battery swapping stations, so accurate SOH assessment is of great significance for improving the economic benefits of battery swapping stations.

[0003] Assessing battery state health typically relies on sophisticated algorithmic models. These SOH (State of Health) models are diverse, ranging from precise modeling based on physical principles to machine learning methods driven by big data. Empirical models are a common approach, estimating SOH using formulas or statistical methods based on actual battery usage records and historical experience. The advantage of such models lies in their simplicity, intuitiveness, and ease of implementation. However, their accuracy is highly dependent on data quality and the applicability of historical experience, thus limiting their widespread adoption in the complex and ever-changing scenarios within battery swapping stations. Besides empirical models, electrochemical models are also an important means of assessing SOH. Based on electrochemical reaction principles or equivalent circuit theory, they can simulate the dynamic behavior of batteries more deeply. Although electrochemical models have theoretical advantages, their calculation process is relatively complex and requires detailed battery parameters, increasing the model's complexity and implementation difficulty. In recent years, machine learning models have gained significant attention due to their powerful data processing capabilities. By training on labeled data, machine learning models can handle complex nonlinear relationships, demonstrating extremely high adaptability. However, this approach also faces challenges, namely the need for a large amount of high-quality labeled data and the relatively poor interpretability of the model.

[0004] In a battery swapping station environment, the charging conditions of batteries are particularly complex, including differences in charging temperature, battery type, and charging strategy. In addition, the data acquisition frequency is low (usually 30 seconds), which poses a more severe challenge to accurately estimating battery capacity. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, system, terminal and medium for calculating battery capacity based on charging data, which solves the problems of existing algorithms for assessing battery health, such as limited accuracy, high model complexity, need for a large amount of high-quality data and poor interpretability. Furthermore, the low data acquisition frequency under the complex charging conditions of battery swapping stations further exacerbates the technical problem of accurately estimating battery capacity.

[0006] To achieve the above and other related objectives, the present invention provides a method for calculating battery capacity based on charging data. The method includes: acquiring battery charging data for each charge of a target battery within a preset time period at a battery swapping station; wherein the battery charging data includes: charging process data and the rated capacity of the battery; performing data cleaning on each battery charging data to remove invalid data; extracting charging process data that conforms to an effective charging cycle based on the charging data of each target battery; and calculating the corresponding single charging capacity based on each charging process data that conforms to an effective charging cycle.

[0007] Based on the capacity of each single charge, the corresponding equivalent SOH is calculated; the equivalent SOH arranged in chronological order is traversed by sliding window to obtain the normal equivalent SOH of each sliding window; based on the normal equivalent SOH of the sliding window that meets the sample size requirement, the SOH of the target battery at different time points and the current SOH are calculated.

[0008] In one embodiment of the present invention, the charging process data includes: the current and SOC that change over time during the charging process.

[0009] In one embodiment of the present invention, the step of extracting charging process data that conforms to an effective charging cycle based on the charging data of each battery of the target battery includes: determining whether the SOC in the charging data of each battery of the target battery conforms to the conditions for an effective charging cycle, and extracting the charging process data that conforms to the conditions for an effective charging cycle as the charging process data that conforms to an effective charging cycle; wherein, the conditions for an effective charging cycle include: the ending SOC is 100%, and the starting SOC is not greater than a set threshold.

[0010] In one embodiment of the present invention, the step of calculating the corresponding single charging capacity based on the charging process data of each valid charging cycle includes: calculating the ampere-hour integral of the current changing with time in each charging process data of each valid charging cycle to obtain the single charging capacity corresponding to each charging process data.

[0011] In one embodiment of the present invention, the step of calculating the corresponding equivalent SOH based on each single charge capacity includes: calculating the equivalent capacity based on each single charge capacity and the initial SOC and final SOC in the corresponding charging process data; and calculating the corresponding equivalent SOH based on each equivalent capacity and the rated capacity of the corresponding battery.

[0012] In one embodiment of the present invention, the step of traversing the equivalent SOHs arranged in chronological order by a sliding window to obtain the normal equivalent SOH of each sliding window includes: traversing the equivalent SOHs arranged in chronological order by a sliding window to obtain each equivalent SOH of each sliding window; wherein the size of the sliding window is set to a set time period length; and the normal equivalent SOH of each sliding window is selected using the 2σ rule.

[0013] In one embodiment of the present invention, the step of calculating the SOH of the target battery at different time points based on the normal equivalent SOH of the sliding window that meets the sample number requirement includes: selecting a sliding window that includes at least a set threshold number of normal equivalent SOHs as the target sliding window, and obtaining the normal equivalent SOH of each target sliding window; calculating the average of the normal equivalent SOHs of each target sliding window to obtain the SOH at the corresponding time point of each target sliding window.

[0014] To achieve the above and other related objectives, the present invention provides a system for calculating battery capacity based on charging data. The system includes: a data acquisition module for acquiring battery charging data for each charge of a target battery within a preset time period at a battery swapping station; wherein the battery charging data includes charging process data and the battery's rated capacity; a data cleaning module connected to the data acquisition module for cleaning the battery charging data to remove invalid data; a valid data extraction module connected to the data cleaning module for extracting charging process data that conforms to a valid charging cycle based on the battery charging data of the target battery; and a single-charge capacity calculation module connected to... The effective data extraction module is used to calculate the corresponding single-charge capacity based on the charging process data that conforms to the effective charging cycle; the equivalent SOH calculation module is connected to the single-charge capacity calculation module and is used to calculate the corresponding equivalent SOH based on each single-charge capacity; the data traversal module is connected to the equivalent SOH calculation module and is used to traverse the equivalent SOH arranged in chronological order through a sliding window to obtain the normal equivalent SOH of each sliding window; the SOH calculation module is connected to the traversal module and is used to calculate the SOH of the target battery at different time points and the current SOH based on the normal equivalent SOH of the sliding window that meets the sample number requirement.

[0015] To achieve the above and other related objectives, the present invention provides an electronic terminal, comprising: one or more memories and one or more processors; the one or more memories being used to store a computer program; and the one or more processors being connected to the memories and used to run the computer program to execute the method for calculating battery capacity based on charging data.

[0016] To achieve the above and other related objectives, the present invention provides a computer storage medium storing a computer program, wherein the computer program, when executed, implements the method for calculating battery capacity based on charging data.

[0017] As described above, this invention provides a method, system, terminal, and medium for calculating battery capacity based on charging data, offering the following advantages: First, the invention collects and cleans battery data from each charge, removing invalid information. Then, it extracts charging process data that conforms to an effective charging cycle from the cleaned data and calculates the single-charge capacity based on this. Subsequently, it uses these single-charge capacity values ​​to calculate the corresponding equivalent state of charge (SOH). Next, it employs a sliding window technique to iterate through the temporally ordered equivalent SOHs to obtain the normal equivalent SOH within each window. Finally, based on the sliding window data that meets the sample size requirements, the SOH of the target battery at different time points is calculated. Compared to existing solutions, this invention is not only simple to operate but also applicable to various complex scenarios within battery swapping stations, accurately and effectively obtaining the battery's SOH results. Attached Figure Description

[0018] Figure 1 shows a flowchart of a method for calculating battery capacity based on charging data according to an embodiment of the present invention.

[0019] Figure 2 shows a schematic diagram of the structure of a system for calculating battery capacity based on charging data in one embodiment of the present invention.

[0020] Figure 3 shows a schematic diagram of the structure of an electronic terminal according to an embodiment of the present invention. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the invention. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of the invention. The following detailed description should not be considered limiting, and the scope of the embodiments of the invention is defined only by the claims of the published patents. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. Spatially related terms, such as “upper,” “lower,” “left,” “right,” “below,” “below,” “lower part,” “above,” “upper part,” etc., may be used herein to illustrate the relationship between one element or feature shown in the figures and another element or feature.

[0023] Throughout this specification, when it is said that a part is "connected" to another part, this includes not only "direct connection" but also "indirect connection" by placing other elements in between. Furthermore, when it is said that a part "includes" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather means that other constituent elements may also be included.

[0024] The terms "first," "second," and "third," etc., used herein are for the purpose of describing various parts, components, regions, layers, and / or segments, but are not limiting. These terms are used only to distinguish one part, component, region, layer, or segment from others. Therefore, the "first part," "component," "region," "layer," or "segment" described below may refer to a "second part," "component," "region," "layer," or "segment" without departing from the scope of this invention.

[0025] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some manner.

[0026] This invention provides a method for calculating battery capacity based on charging data. First, battery data from each charge is collected and cleaned to remove invalid information. Next, charging process data conforming to valid charging cycles is extracted from the cleaned data, and the single-charge capacity is calculated accordingly. Then, the corresponding equivalent state of charge (SOH) is calculated using these single-charge capacity values. A sliding window technique is then used to iterate through the temporally ordered equivalent SOHs to obtain the normal equivalent SOH within each window. Finally, based on the sliding window data meeting the required sample size, the SOH of the target battery at different time points is calculated. Compared to existing solutions, this invention is not only simple to operate but also applicable to various complex scenarios within battery swapping stations, accurately and effectively obtaining battery SOH results.

[0027] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0028] Figure 1 shows a flowchart of a method for calculating battery capacity based on charging data in an embodiment of the present invention.

[0029] The method includes:

[0030] Step S1: Obtain battery charging data for each charge of the target battery within the battery swapping station during a preset time period.

[0031] In detail, the process involves acquiring battery charging data for each charge of a target battery within a battery swapping station over a preset time period. The target battery can refer to one or more specific batteries within the swapping station that require health status assessment. The preset time period is a specific time range set according to requirements, such as the past year. The battery charging data includes charging process data and the battery's rated capacity. The collected battery charging data contains two types of information: first, charging process data, which records the battery's charging behavior in detail; and second, the battery's rated capacity, i.e., the maximum capacity specified by the battery at the time of manufacture, which is an important benchmark for assessing the battery's health status.

[0032] In one embodiment, the charging process data includes:

[0033] The changing current during charging reflects the battery's charging rate and behavior; current data records how the current value fluctuates over time from the start to the end of a single charge. This data is crucial for understanding the battery's charging efficiency, thermal effects, and potential signs of aging. For example, abnormal fluctuations in the current value during charging may indicate problems such as increased resistance or capacity decay within the battery.

[0034] State of Charge (SOC): Reflects the percentage of a battery's total capacity currently stored electrical energy, and is a key parameter in battery management systems. SOC data provides a visual representation of how the battery's charge changes during charging. By monitoring changes in SOC, we can understand the battery's charging efficiency and remaining capacity at different charging stages. This is crucial for assessing battery health, predicting remaining lifespan, and optimizing charging strategies. The SOC includes the initial SOC and the final SOC.

[0035] Step S2: Perform data cleaning on each battery charging data to remove invalid data.

[0036] In detail, check for abnormal data in the charging data of each battery, such as null or missing values, and remove them.

[0037] Step S3: Based on the charging data of each battery in the target battery, extract the charging process data that conforms to the effective charging cycle.

[0038] In one embodiment, step S3 includes:

[0039] Determine whether the SOC in the charging data of each battery of the target battery meets the valid charging cycle conditions, and extract the charging process data that meets the valid charging cycle conditions as the charging process data that meets the valid charging cycle conditions.

[0040] The effective charging cycle conditions include:

[0041] The final SOC is 100%, and the initial SOC is not greater than a set threshold. Preferably, the set threshold is within 20-25%.

[0042] The effective charging cycle conditions are set primarily for the following purposes: First, the SOC (i.e., 100%) at full charge is considered completely reliable because it represents the battery's maximum capacity; second, when the SOC is below 25%, the battery has already released most of its charge, and charging at this point allows for a more effective assessment of the battery's charging capability and health status; finally, setting this threshold also ensures that we have enough data points for accurate analysis and calculation.

[0043] Step S4: Calculate the corresponding single-charge capacity based on the charging process data that conforms to the valid charging cycle.

[0044] In one embodiment, step S4 includes: calculating the ampere-hour integral of the current changing with time in each charging process data that conforms to an effective charging cycle, and obtaining the single charging capacity corresponding to each charging process data.

[0045] The formula for calculating the ampere-hour integral is:

[0046] Where Q is the single charge capacity, t1 and t2 represent the start and end times of the charging process, respectively, and I(t) represents the current value at time t.

[0047] Step S5: Calculate the corresponding equivalent SOH based on the single charge capacity.

[0048] In one embodiment, step S5 includes:

[0049] Calculate the equivalent capacity based on the capacity of each single charge and the starting and ending SOCs in the corresponding charging process data.

[0050] Specifically, first, we calculate ΔSOC from the initial SOC and the final SOC: ΔSOC = Final SOC - Initial SOC. Since ΔSOC is in percentage form, we need to convert it to decimal form for calculation. Then, we calculate the equivalent capacity based on ΔSOC and the single-charge capacity: Equivalent capacity = Single-charge capacity / Equivalent ΔSOC (converted to decimal).

[0051] The equivalent SOH is calculated based on each equivalent capacity and the corresponding rated capacity of the battery.

[0052] Specifically, equivalent SOH is the ratio of equivalent capacity to rated capacity, representing the percentage of the battery's current actual capacity relative to its rated capacity. That is, equivalent SOH = equivalent capacity / rated capacity × 100%.

[0053] By following the steps above, we can calculate the equivalent capacity and equivalent state of health (SOH) using the single-charge capacity, initial state of charge (SOC), and final state of charge (SOC), thus more accurately assessing the battery's health status. This is of great significance for battery maintenance, management, and replacement decisions.

[0054] Step S6: Traverse the equivalent SOHs arranged in chronological order through a sliding window to obtain the normal equivalent SOH for each sliding window.

[0055] In one embodiment, step S6 includes:

[0056] The equivalent SOHs arranged in chronological order are traversed by a sliding window to obtain each equivalent SOH of the sliding window; wherein the size of the sliding window is set to a set time period length; the time point corresponding to each sliding window is the time corresponding to the last equivalent SOH traversed by the window;

[0057] Specifically, determine the size of the sliding window, i.e., set the time period length. This length can be set according to actual needs, such as one day, one week, or one month. Starting from the beginning of the time series, create a sliding window of the set time period length. Traverse the equivalent SOH data in chronological order, moving one data point in the sliding window each time, until the entire time series has been traversed. For each sliding window, record the values ​​of all equivalent SOHs within the window and calculate the corresponding time point (i.e., the time corresponding to the last equivalent SOH traversed by the window).

[0058] Using the 2σ rule, select the normal equivalent SOH for each sliding window.

[0059] Specifically, for the equivalent SOH data within each sliding window, its mean (μ) and standard deviation (σ) are calculated. According to the 2σ rule, the range of mean plus or minus twice the standard deviation is considered the normal range, i.e., [μ-2σ, μ+2σ]. The equivalent SOH data falling within this range in each sliding window is selected as the normal equivalent SOH.

[0060] Step S7: Based on the normal equivalent SOH of the sliding window that meets the sample size requirement, calculate the SOH of the target battery at different time points and the current SOH.

[0061] In one embodiment, step S7:

[0062] Filter a sliding window that includes at least a set threshold number of normal equivalent SOHs as the target sliding window, and obtain the normal equivalent SOH of each target sliding window;

[0063] Specifically, a threshold is defined, representing the minimum number of normal equivalent SOHs that must be included in each target sliding window. Sliding windows that contain at least the defined threshold number of normal equivalent SOHs are selected as target sliding windows.

[0064] The mean value of the normal equivalent SOH for each target sliding window is calculated to obtain the SOH at the corresponding time point of each target sliding window.

[0065] Specifically, the mean of the normal equivalent SOH value within each target sliding window is calculated, and this mean represents the SOH at the corresponding time point of that window.

[0066] For example, suppose we have equivalent SOH data for a time series, a sliding window size of one month (i.e., each window contains equivalent SOH data for one month), and a threshold of 3. If the number of normal equivalent SOHs within a certain window is greater than or equal to 3, then that window is considered the target sliding window. The mean of all normal equivalent SOHs within the target sliding window is calculated and used as the SOH at the corresponding time point of that window.

[0067] Obtaining the State of Health (SOH) of a target battery at different time points offers several advantages: First, the latest SOH directly reflects the current relative capacity of the target battery, providing a reliable basis for real-time performance evaluation. Second, by tracking the SOH's change over time, we can deeply analyze the battery's degradation rate and health status trends, providing important clues for predicting battery life. Third, significant fluctuations in SOH values ​​within a short period may indicate potential battery failures or anomalies; timely monitoring of SOH changes helps us quickly identify and address these potential problems, effectively preventing equipment downtime or safety accidents caused by battery malfunctions. Finally, based on the SOH's changing trends, we can flexibly adjust battery management strategies, including optimizing charging and discharging modes and strengthening temperature control, aiming to extend battery life and improve its overall performance.

[0068] For battery swapping station operations, obtaining the State of Health (SOH) of the target batteries at different points in time is also of great significance. By understanding the overall health status of the battery pack, swapping stations can more accurately plan battery maintenance and replacement, ensuring efficient and stable operation. Furthermore, based on SOH data analysis, swapping stations can optimize battery inventory management, reduce service interruptions caused by battery aging or malfunctions, and improve user experience and operational efficiency.

[0069] We selected two sets of batteries for actual testing to verify the accuracy of the method of this invention in assessing battery health. The SOH value calculated by the algorithm of this invention for each set of batteries was compared with the SOH value measured by professional equipment. The results are shown in the table below:

[0070] Table 1: Comparison of SOH values ​​between the two battery sets

[0071] By averaging the differences between the SOH (State of Health) results of the two battery algorithms and the measured results, we found an average error of 0.5, indicating that the algorithm of this invention maintains a high degree of consistency with the measured values ​​overall. In the comparison of the two battery sets, the maximum error was 0.9, which is close to the measured results and relatively small. This fully demonstrates the reliability and practicality of the method of this invention in battery health status assessment.

[0072] Similar to the principles of the above embodiments, the present invention provides a system for calculating battery capacity based on charging data.

[0073] The following specific embodiments are provided in conjunction with the accompanying drawings:

[0074] Figure 2 shows a schematic diagram of a system for calculating battery capacity based on charging data in an embodiment of the present invention.

[0075] The system includes:

[0076] The data acquisition module 1 is used to acquire battery charging data of the target battery in the battery swapping station for each charge within a preset time period; wherein, the battery charging data includes: charging process data and the rated capacity of the battery;

[0077] Data cleaning module 2, connected to the data acquisition module 1, is used to clean the charging data of each battery to remove invalid data;

[0078] The effective data extraction module 3 is connected to the data cleaning module 2 and is used to extract charging process data that conforms to the effective charging cycle based on the charging data of each battery of the target battery.

[0079] The single-charge capacity calculation module 4 is connected to the effective data extraction module 3 and is used to calculate the corresponding single-charge capacity based on the charging process data that conforms to the effective charging cycle.

[0080] The equivalent SOH calculation module 5 is connected to the single charge capacity calculation module 4 and is used to calculate the corresponding equivalent SOH based on each single charge capacity.

[0081] The data traversal module 6, connected to the equivalent SOH calculation module 5, is used to traverse the equivalent SOH arranged in chronological order through a sliding window to obtain the normal equivalent SOH for each sliding window.

[0082] SOH calculation module 7, connected to the data traversal module 6, is used to calculate the SOH of the target battery at different time points and the current SOH based on the normal equivalent SOH of the sliding window that meets the sample number requirements.

[0083] It should be noted that the division of the various modules in the system embodiment of Figure 2 is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these units can all be implemented in software through processing element calls; they can all be implemented in hardware; or some units can be implemented in software through processing element calls, while others are implemented in hardware.

[0084] Since the implementation principle of the system for calculating battery capacity based on charging data has been described in the foregoing embodiments, it will not be repeated here.

[0085] The method for calculating battery capacity based on charging data provided in this embodiment of the invention can be implemented on the terminal side or the server side. Regarding the hardware structure of the electronic terminal, please refer to Figure 3, which is a schematic diagram of an optional hardware structure of the electronic terminal 1000 provided in this embodiment of the invention. The electronic terminal 1000 can be a mobile phone, computer device, tablet device, personal digital processing device, factory back-end processing device, etc. The electronic terminal 1000 includes: at least one processor 1001, a memory 1002, at least one network interface 10010, and a user interface 1009. The various components in the device are coupled together through a bus system 1005. It is understood that the bus system 1005 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 1005 also includes a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as a bus system in Figure 3.

[0086] The user interface 1009 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0087] It is understood that memory 1002 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0088] In this embodiment of the invention, the memory 1002 is used to store various types of data to support the operation of the electronic terminal 1000. Examples of this data include: any executable program that operates on the electronic terminal 1000, such as the operating system 10021 and application programs 10022; the operating system 10021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 10022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The method for calculating battery capacity based on charging data provided in this embodiment of the invention can be included in the application program 10022.

[0089] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by the processor 1001. The processor 1001 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 1001 or by instructions in the form of software. The processor 1001 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 1001 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor 1001 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0090] In an exemplary embodiment, the electronic terminal 1000 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0091] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0092] In the embodiments provided in this application, the computer-readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of the medium. However, it should be understood that computer-readable and writable storage media and data storage media do not include connections, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0093] In summary, the method, system, terminal, and medium for calculating battery capacity based on charging data of the present invention first collect and clean battery data from each charge, removing invalid information; then, it extracts charging process data that conforms to an effective charging cycle from the cleaned data and calculates the single-charge capacity based on this; subsequently, it uses these single-charge capacity values ​​to calculate the corresponding equivalent state of charge (SOH); then, it uses a sliding window technique to traverse the equivalent SOH arranged in chronological order to obtain the normal equivalent SOH within each window; finally, based on the sliding window data that meets the sample size requirements, it calculates the SOH of the target battery at different time points. Compared with existing solutions, the present invention is not only simple to operate but also applicable to various complex scenarios within battery swapping stations, and can accurately and effectively obtain the battery's SOH results. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0094] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for calculating battery capacity based on charging data, characterized in that, The method includes: Acquire battery charging data for each charge of the target battery within a preset time period at the battery swapping station; wherein, the battery charging data includes: charging process data and the battery's rated capacity; Data cleaning is performed on the charging data of each battery to remove invalid data; Based on the charging data of each battery in the target battery, extract the charging process data that conforms to the effective charging cycle; Calculate the corresponding single-charge capacity based on the charging process data that conforms to the effective charging cycle. Calculate the corresponding equivalent SOH based on the capacity of each single charge; The normal equivalent SOH for each sliding window is obtained by traversing the equivalent SOH arranged in chronological order through a sliding window. Based on the normal equivalent SOH of the sliding window that meets the sample size requirement, the SOH of the target battery at different time points and the current SOH are calculated. The step of obtaining the normal equivalent SOH of each sliding window by traversing the equivalent SOH arranged in chronological order through a sliding window includes: obtaining each equivalent SOH of each sliding window by traversing the equivalent SOH arranged in chronological order through a sliding window; wherein the size of the sliding window is set to a set time period length; and the normal equivalent SOH of each sliding window is selected using the 2σ rule. The step of calculating the SOH of the target battery at different time points based on the normal equivalent SOH of the sliding window that meets the sample number requirement includes: selecting a sliding window that includes at least a set threshold number of normal equivalent SOHs as the target sliding window, and obtaining the normal equivalent SOH of each target sliding window; calculating the mean of the normal equivalent SOH of each target sliding window to obtain the SOH of each target sliding window at the corresponding time point.

2. The method for calculating battery capacity based on charging data according to claim 1, characterized in that, The charging process data includes: the current and SOC that change over time during the charging process.

3. The method for calculating battery capacity based on charging data according to claim 2, characterized in that, The extraction of charging process data that conforms to an effective charging cycle, based on the charging data of each battery in the target battery, includes: Determine whether the SOC in the charging data of each battery of the target battery meets the valid charging cycle conditions, and extract the charging process data that meets the valid charging cycle conditions as the charging process data that meets the valid charging cycle conditions. The effective charging cycle conditions include: The final SOC is 100%, and the initial SOC is not greater than the set threshold.

4. The method for calculating battery capacity based on charging data according to claim 2, characterized in that, The calculation of the corresponding single-charge capacity based on the charging process data that conforms to an effective charging cycle includes: For each charging process data that conforms to an effective charging cycle, calculate the ampere-hour integral of the current that changes with time in each charging process data to obtain the single charging capacity corresponding to each charging process data.

5. The method for calculating battery capacity based on charging data according to claim 2, characterized in that, The calculation of the corresponding equivalent SOH based on the capacity of each single charge includes: Calculate the equivalent capacity based on the capacity of each single charge and the starting and ending SOCs in the corresponding charging process data. The equivalent SOH is calculated based on each equivalent capacity and the corresponding rated capacity of the battery.

6. A system for calculating battery capacity based on charging data, characterized in that, The system includes: The data acquisition module is used to acquire battery charging data for each charge of the target battery within a preset time period in the battery swapping station; wherein, the battery charging data includes: charging process data and the rated capacity of the battery; The data cleaning module, connected to the data acquisition module, is used to clean the charging data of each battery to remove invalid data. An effective data extraction module, connected to the data cleaning module, is used to extract charging process data that conforms to an effective charging cycle based on the charging data of each battery of the target battery. A single-charge capacity calculation module, connected to the effective data extraction module, is used to calculate the corresponding single-charge capacity based on the charging process data that conforms to the effective charging cycle. An equivalent SOH calculation module, connected to the single-charge capacity calculation module, is used to calculate the corresponding equivalent SOH based on each single-charge capacity. The data traversal module, connected to the equivalent SOH calculation module, is used to traverse the equivalent SOH arranged in chronological order through a sliding window to obtain the normal equivalent SOH for each sliding window; The SOH calculation module, connected to the traversal module, is used to calculate the SOH of the target battery at different time points and the current SOH based on the normal equivalent SOH of the sliding window that meets the sample number requirement. The step of obtaining the normal equivalent SOH of each sliding window by traversing the equivalent SOH arranged in chronological order through a sliding window includes: obtaining each equivalent SOH of each sliding window by traversing the equivalent SOH arranged in chronological order through a sliding window; wherein the size of the sliding window is set to a set time period length; and the normal equivalent SOH of each sliding window is selected using the 2σ rule. The step of calculating the SOH of the target battery at different time points based on the normal equivalent SOH of the sliding window that meets the sample number requirement includes: selecting a sliding window that includes at least a set threshold number of normal equivalent SOHs as the target sliding window, and obtaining the normal equivalent SOH of each target sliding window; calculating the mean of the normal equivalent SOH of each target sliding window to obtain the SOH of each target sliding window at the corresponding time point.

7. An electronic terminal, characterized in that, include: One or more memories and one or more processors; The one or more memories are used to store computer programs; The one or more processors are connected to the memory and are used to run the computer program to perform the method as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed, implements the method as described in any one of claims 1 to 5.

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