SOH calculation method of battery, calculation equipment and computer readable storage medium

Abstract

The invention provides an SOH calculation method of a battery, calculation equipment and a computer readable storage medium. The method comprises the steps of obtaining a calculation value and a true value of a target algorithm; calculating an error between the calculated value and the true value, and calculating a mean value and a standard deviation of the plurality of errors; determining a state distribution of the error based on the mean value and the standard deviation; determining the coverage degree of the state distribution in a preset precision range, and obtaining the confidence degree of the target algorithm based on the coverage degree; and calculating the target SOH of the battery through the target algorithm based on the confidence coefficient. Therefore, the SOH of the battery can be accurately calculated, and the service life management and the safety management of the battery can be better carried out.

Description

Technical Field

[0001] The present application belongs to the field of power batteries and computers, and specifically relates to a battery SOH calculation method, a computing device, and a computer-readable storage medium. Background Art

[0002] In recent years, the new energy industry has flourished. Power batteries (hereinafter referred to as batteries, also known as batteries, power batteries, etc.) as an important component of new energy equipment (such as automobiles, aircraft, etc.) have also received widespread attention, especially the life and safety of power batteries, which have attracted widespread attention.

[0003] The battery's state of health (SOH) is usually used as a key parameter to measure battery life and safety. How to accurately calculate the battery's SOH has always been a problem that needs to be solved in the industry. Summary of the invention

[0004] The technical problem to be solved by the present application is how to provide a SOH calculation strategy that can accurately calculate the SOH of the battery so as to better manage the life and safety of the battery.

[0005] In order to solve or improve the above technical problems to a certain extent, according to the first aspect of the present application, a method for calculating the SOH of a battery is provided, the method comprising: calculating the SOH of a sample battery according to a target algorithm to obtain a calculated value, and obtaining the true value of the SOH of the sample battery; calculating the error between the calculated value and the true value, and calculating the mean and standard deviation of multiple errors; determining the state distribution of the error based on the mean and the standard deviation; determining the coverage of the state distribution within a preset accuracy range, and obtaining the confidence of the target algorithm based on the coverage; based on the confidence, calculating the target SOH of the target battery through the target algorithm.

[0006] In an optional embodiment, calculating the target SOH of the target battery by using the target algorithm based on the confidence level includes: selecting a target algorithm with the highest confidence level among a plurality of target algorithms to calculate the target SOH of the target battery.

[0007] In an optional embodiment, calculating the target SOH of the target battery by the target algorithm based on the confidence level includes: calculating a first SOH of the target battery by the target algorithm; and adjusting the first SOH based on the confidence level to obtain the target SOH.

[0008] In an optional embodiment, determining the state distribution of the error based on the mean and the standard deviation includes: drawing a state distribution curve of the error based on the mean and the standard deviation.

[0009] In an optional embodiment, the method further includes: obtaining preset rules for the attenuation of the confidence with the usage time of the target battery and / or the charge and discharge cycles experienced, and updating the confidence according to the preset rules; wherein the preset rules include at least one of the following: the mean changes with the usage time of the target battery; the standard deviation changes with the usage time of the target battery; the mean changes with the charge and discharge cycles experienced by the target battery; the standard deviation changes with the charge and discharge cycles experienced by the target battery.

[0010] In an optional embodiment, in response to the preset rules including multiple ones, the method further includes: updating the confidence levels respectively based on the multiple preset rules; selecting the confidence level with the smallest value from the updated multiple confidence levels, and calculating the target SOH of the target battery through the target algorithm based on the selected confidence level.

[0011] In an optional embodiment, the preset rule includes that the mean value increases with the increase in the usage time of the target battery, and updating the confidence level according to the preset rule includes: redetermining the state distribution of the error based on the increased mean value and the standard deviation.

[0012] In an optional embodiment, the preset rule includes that the mean value increases as the charge and discharge cycles experienced by the target battery increase, and updating the confidence level according to the preset rule includes: redetermining the state distribution of the error based on the increased mean value and the standard deviation.

[0013] According to a second aspect of the present application, a computing device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, it can implement any step of the battery SOH calculation method.

[0014] According to a third aspect of the present application, a computer-readable storage medium is provided for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of any one of the methods for calculating the SOH of a battery.

[0015] Compared with the prior art, the present application has obvious advantages and beneficial effects. By means of the above technical solution, the battery SOH calculation method, calculation device, and storage medium of the present application can achieve considerable technical advancement and practicality, and have wide industrial utilization value, and have at least the following advantages: The present application provides a battery SOH calculation method, a computing device, and a computer-readable storage medium, which can determine the corresponding confidence level according to factors such as the scenario and working conditions of the target battery and the time since the last time the SOH of the target battery was calculated by the target algorithm, so as to adjust the SOH calculated by the traditional SOH algorithm (i.e., the target algorithm) to obtain the target SOH, thereby ensuring the accuracy of the SOH and better managing the battery life and safety.

[0016] Moreover, it can expand the scope of application of the target SOH algorithm, which originally had high requirements on scenarios, working conditions and timeliness.

[0017] Furthermore, the confidence level can be updated based on time and / or charging cycles, thereby enabling a more accurate SOH calculated according to the target algorithm.

[0018] Furthermore, preset rules can be formulated based on different dimensions to calculate the confidence levels separately, and the target SOH of the target battery with the lowest confidence level can be selected. This can comprehensively consider the impact of different factors on the confidence levels of each target algorithm, making the results more accurate.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specifically cites the preferred embodiments and describes them in detail with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a flow chart of a method for calculating SOH of a battery according to an embodiment of the present application; Figure 2 A schematic diagram of a state distribution curve of an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of a battery SOH calculation device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] As mentioned in the background technology, the SOH of a battery is usually used as a key parameter to measure the battery life and safety. How to accurately calculate the SOH of a battery has always been a problem that needs to be solved in the industry.

[0022] However, the SOH algorithm in the prior art has high requirements for scenarios, working conditions and timeliness. When the scenarios and working conditions do not meet the requirements, or the time since the last calculation of SOH is long, the accuracy of SOH is not high. For example, the algorithm for calculating SOH through the dual water tank model is for slow charging conditions, and the algorithm for calculating SOH through the dual water tank model is for fast charging conditions, etc. If the current battery condition or scenario does not meet the relevant requirements, it is impossible to calculate the accurate SOH based on the corresponding SOH algorithm.

[0023] To solve this problem, the present application provides a battery SOH calculation method, a calculation device, and a computer-readable storage medium.

[0024] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific implementation methods and effects of the battery SOH calculation method, calculation device, and computer-readable storage medium proposed in the present application are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0025] It should be noted that the battery in this application is a power battery (or battery pack, power battery pack), and the battery is an energy system that provides power for new energy equipment such as electric vehicles and aircraft.

[0026] In the first aspect of this application, see Figure 1 As shown, Figure 1 This is a flow chart of the first battery SOH calculation method provided in the present application. The first battery SOH calculation method may include the following steps S101 to S105, each of which is described in detail as follows.

[0027] Step S101, calculating the SOH of a sample battery according to a target algorithm to obtain a calculated value, and acquiring a true value of the SOH of the sample battery.

[0028] Step S102, calculating the error between the calculated value and the true value, and calculating the mean and standard deviation of multiple errors.

[0029] The sample battery is the battery used to determine the confidence of the target algorithm. In order to accurately and comprehensively obtain the confidence of the target algorithm, multiple batteries of the same type can be selected as sample batteries. The target battery is one or more batteries for which SOH needs to be calculated. In order to ensure the accuracy of SOH calculation, the target battery and the sample battery should preferably be of the same type.

[0030] The same type means that if the target battery is a lithium iron phosphate battery, the sample battery should also be a lithium iron phosphate battery. Or, if the target battery is a cylindrical battery of a ternary system, the sample battery should also be a cylindrical battery of a ternary system. This ensures that the confidence level obtained from the sample battery is applicable to the target battery.

[0031] The target algorithm is one or more algorithms for calculating the SOH of the sample battery, and the SOH of the sample battery calculated according to the target algorithm is recorded as a calculated value.

[0032] In an optional embodiment, the true value of the SOH of the sample battery can be obtained by performing a constant capacity experiment on the sample battery, and the true value is used to reflect the actual SOH of the sample battery.

[0033] In another optional embodiment, an aging experiment, also called a "degradation experiment," may be performed on the sample battery, which is to perform repeated charge-discharge-charge... experiments on the sample battery in a laboratory to observe the degradation of the sample battery.

[0034] The reference SOH of the sample battery is determined every preset number of charge and discharge cycles. The preset number can be set as needed, such as determining the reference SOH every 50 or 100 cycles. The actual SOH of the sample battery is determined by disassembling the sample battery and detecting the active ingredients in the sample battery.

[0035] It should be noted that the methods for determining the actual SOH of the sample battery include but are not limited to the above examples, and other commonly used SOH determination methods are within the protection scope of this application.

[0036] The difference (i.e., error) between the calculated value and the true value is used to reflect the error of the target algorithm in calculating the SOH of the sample battery. In order to obtain a comprehensive picture of the target algorithm error, multiple sample batteries of the same type can be calculated and capacitated to obtain multiple errors. The mean and standard deviation of the multiple errors are calculated.

[0037] In an optional embodiment, the target algorithm includes at least one of the following algorithms: Incremental Capacity Analysis (ICA) algorithm; Differential Voltage Analysis (DVA); double water tank algorithm; inverse ampere-hour integration method; table lookup method.

[0038] Among them, the dual water tank algorithm constructs a dual water tank model through the mechanism analysis of capacity attenuation, uses charging data as data input for parameter identification, and uses the identified parameters to perform capacity calculation and charge curve reconstruction, thereby realizing the SOH calculation algorithm.

[0039] The inverse ampere-hour integration method is an algorithm that estimates the capacity by calculating the ratio of the change in charge to the change in the state of charge (SOC), and then calculates the SOH.

[0040] The table lookup method is an algorithm for collecting battery data and looking up the corresponding SOH from a table based on the data. The data may include battery voltage changes or internal resistance changes, etc.

[0041] Step S103: determining the state distribution of the error based on the mean and the standard deviation.

[0042] In an optional embodiment of step S103, a state distribution curve of the error is drawn based on the mean and the standard deviation. Further, the state distribution curve may be a normal distribution curve (also called a Gaussian curve). Figure 2 , Figure 2 is a schematic diagram of a normal distribution curve of an embodiment of the present application, Figure 2 Here, Mean represents mean, Std Dev represents standard deviation, the horizontal axis Error of SOH of the normal distribution curve is the error (unit: %), and the vertical axis Percentage of the normal distribution curve is the proportion corresponding to the error (unit: %).

[0043] Step S104, determining the coverage of the state distribution within a preset accuracy range, and obtaining the confidence of the target algorithm based on the coverage.

[0044] The preset accuracy range is a set interval, which is a part of the state distribution.

[0045] Optionally, the preset accuracy range stores a corresponding relationship with at least one of the following factors: the scene in which the target battery is located; the working condition of the target battery; the time since the last time the SOH of the target battery was calculated by the target algorithm. Furthermore, a corresponding relationship table can be constructed to store the relationship between one or more factors and the preset accuracy range, and then the corresponding relationship table can be queried according to the current factors of the target battery to obtain the corresponding preset accuracy range.

[0046] In a specific example, different preset accuracy ranges may be set for different scenarios where the target battery is located (eg, a scenario where the battery has high energy consumption, a scenario where the external temperature changes significantly in a short period of time, etc.).

[0047] In another specific example, different preset accuracy ranges may be set for different operating conditions of the target battery (eg, a charging condition, a battery static condition, a battery static condition after being charged, etc.).

[0048] In another specific example, different preset accuracy ranges can be set for the time difference from using the target algorithm to calculate the SOH. For example, if the time from the last time the target battery's SOH was calculated using the target algorithm is 0 to 5 days, the corresponding preset accuracy range is set to A; if the time from the last time the target battery's SOH was calculated using the target algorithm is 6 to 10 days, the corresponding preset accuracy range is set to B, and so on.

[0049] It should be noted that the correspondence between the preset accuracy range and one or more factors includes but is not limited to the above examples, which will not be repeated here.

[0050] For example, see Figure 2 , the state distribution interval corresponding to the error satisfying certain conditions in the state distribution curve is used as the preset accuracy range, for example, the interval with an error of ±2.5% is used as the preset accuracy range, corresponding to Figure 2 The area between the two dotted lines.

[0051] Correspondingly, the coverage corresponding to the preset accuracy range can be obtained according to the area of ​​the state distribution curve in the interval. For example, if the area of ​​the state distribution curve within the preset accuracy range accounts for 86% of the total area, the confidence of the target algorithm is 86% or 0.86.

[0052] Step S105 : calculating the target SOH of the target battery by using the target algorithm based on the confidence level.

[0053] pass Figure 1 The provided battery SOH calculation method can determine the corresponding confidence level according to factors such as the scenario and working condition of the target battery and the time since the last time the SOH of the target battery was calculated by the target algorithm, so as to adjust the SOH calculated by the traditional SOH algorithm (that is, the target algorithm) to obtain the target SOH. This can ensure the accuracy of the SOH and make the target SOH algorithm, which originally had high requirements for scenarios, working conditions and timeliness, more widely applicable.

[0054] Optionally, the target algorithm may include one or more algorithms, for example, the target algorithm may include at least one of the following: an algorithm for calculating SOH through a double water tank model; an algorithm for calculating SOH using a dq-dv curve; an algorithm for calculating SOH using a capacity interval method; an algorithm for calculating SOH using incremental capacity analysis (ICA); an algorithm for calculating SOH using differential voltage analysis (DVA), and the like.

[0055] In an optional embodiment of step S105, if the target algorithm is for an algorithm, the first SOH of the battery is calculated by the target algorithm; and the first SOH is adjusted based on the confidence level to obtain the target SOH.

[0056] For example, when the confidence level is lower than a first threshold, a corresponding correction amount may be obtained to correct the calculated SOH to obtain a target SOH.

[0057] In this way, the SOH calculated by the target algorithm can be corrected by combining factors such as the scene and working condition of the target battery or the time since the last time the SOH of the target battery was calculated by the target algorithm.

[0058] In another optional embodiment of step S105, it includes: selecting a target algorithm with the highest confidence among the multiple target algorithms to calculate the target SOH of the battery.

[0059] Specifically, the confidence of each target algorithm is calculated according to the above scheme, and the target algorithm with the highest current confidence is selected as the algorithm for calculating SOH this time, and the calculation result is used as the target SOH. Therefore, when multiple SOH algorithms are deployed, the algorithm with the highest current accuracy can be selected based on the confidence to calculate the target SOH of the target battery.

[0060] In an alternative embodiment, Figure 1 The battery SOH calculation method may further include: obtaining a preset rule that the confidence level decays with the usage time and / or charge and discharge cycles of the target battery, and updating the confidence level according to the preset rule.

[0061] Among them, the preset rules can be determined based on experiments.

[0062] During the use of the battery, it will go through multiple charge and discharge cycles. The process from fully charging the battery to fully discharging it is called a discharge cycle, and the process from fully discharging the battery to fully charging it is called a charge cycle. A charge and discharge cycle refers to a charge cycle and / or a discharge cycle.

[0063] In a specific example, as the usage time and / or charge and discharge cycles experienced by the target battery increase, its confidence level needs to be re-determined.

[0064] For example, as the usage time of the target battery increases by X days, the corresponding confidence decays by 10%.

[0065] For another example, as the number of charge and discharge cycles experienced by the target battery increases by Y cycles, the corresponding confidence level decays by 10%.

[0066] It should be noted that the preset rules include but are not limited to the above examples. Any solution that satisfies the requirement of updating the confidence level based on time and / or charge-discharge cycle attenuation falls within the protection scope of this application and will not be described in detail here.

[0067] In a specific embodiment, the preset rule includes at least one of the following: the mean value changes with the usage time of the target battery; the standard deviation changes with the usage time of the target battery; the mean value changes with the charge and discharge cycles experienced by the target battery; the standard deviation changes with the charge and discharge cycles experienced by the target battery. That is, the attenuation of confidence can be reflected by the change of the mean value and / or the standard deviation.

[0068] Three specific schemes for updating the confidence level according to the preset rules are provided below: Solution 1, the preset rule includes that the mean value increases with the increase in the usage time of the target battery, and updating the confidence level according to the preset rule may include: redetermining the state distribution of the error based on the increased mean value and the standard deviation.

[0069] For example, suppose that as time goes by, the standard deviation of the error remains unchanged, but the mean of the error increases. The speed at which the mean increases = the SOH attenuation value caused by the use time of the target battery.

[0070] Option 2, the preset rule includes that the mean value increases with the increase of the charge and discharge cycles experienced by the target battery, and the updating of the confidence level according to the preset rule may also include: redetermining the state distribution of the error based on the increased mean value and the standard deviation.

[0071] For example, assuming that as the number of charge and discharge cycles experienced by the target battery increases, the standard deviation of the error remains unchanged, but the mean error increases, and the rate of increase = the SOH attenuation value caused by the charge and discharge.

[0072] In scheme 3, the preset rule includes that the mean value increases with the use time of the target battery and the charge and discharge cycles it has experienced, and the updating of the confidence according to the preset rule may also include: re-determining the state distribution of the error based on the increased mean value and the standard deviation. The specific way in which the mean value increases can be determined as needed and will not be elaborated here.

[0073] In this embodiment, the confidence level can be updated based on time and / or charging cycles, so that a more accurate SOH can be calculated according to the target algorithm.

[0074] Correspondingly, if there are multiple target algorithms, the target algorithm with the highest updated confidence level may be selected to calculate the target SOH of the target battery.

[0075] In an optional embodiment, in response to the preset rules including multiple, Figure 1 The battery SOH calculation method may further include: updating the confidence levels respectively based on a plurality of the preset rules; selecting the confidence level with the smallest value from the updated multiple confidence levels, and calculating the target SOH of the target battery through the target algorithm based on the selected confidence level.

[0076] There may be multiple preset rules, for example, two preset rules may be set, one of which is a rule for updating confidence based on time, and the other is a rule for updating confidence based on charge and discharge cycles. The confidence may be updated based on these two preset rules respectively, and the one with the smaller confidence value may be selected to calculate the target SOH of the target battery.

[0077] In this embodiment, preset rules are formulated based on different dimensions to calculate the confidence respectively, and the target SOH of the target battery with the smallest confidence is selected to calculate the target SOH. This can comprehensively consider the impact of different factors on the confidence of each target algorithm, making the result more accurate.

[0078] In an optional embodiment, the battery SOH calculation method provided in the present application can be executed by the target battery, specifically by the battery management controller (Battery Management System, referred to as BMS) at the target battery end, which can also be called a battery management unit (Battery Management Unit, referred to as BMU). Furthermore, information such as the preset accuracy range and preset rules can be stored in the memory of the BMS to execute the algorithm of the present application.

[0079] In another optional embodiment, the battery SOH calculation method provided in the present application can be executed by a server, wherein the server can refer to a cloud server. The server can obtain the SOH of multiple target batteries under different charge and discharge cycles, thereby predicting the life of each target battery, thereby determining whether each target battery is about to be retired. For batteries that are about to be retired, the server can communicate with the battery or the device of the battery to prompt the user that the battery is about to be retired, and please send it to the recycling site or the original manufacturer in time for battery replacement. This is to avoid safety problems caused by the continued use of retired batteries. Therefore, the cloud can uniformly manage a large number of batteries based on the battery SOH calculation method, and based on communication with the battery end or the device end, prompt battery replacement to reduce battery safety risks.

[0080] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0081] In the second aspect of this application, based on the same application concept, see Figure 3 The present application embodiment further provides a battery SOH calculation device 30, which may include: The sample SOH determination module 301 is used to calculate the SOH of the sample battery according to the target algorithm to obtain a calculated value, and obtain the real value of the SOH of the sample battery; An error calculation module 302 is used to calculate the error between the calculated value and the true value, and calculate the mean and standard deviation of multiple errors; A state distribution determination module 303, configured to determine the state distribution of the error based on the mean and the standard deviation; A confidence acquisition module 304, used to determine the coverage of the state distribution within a preset accuracy range, and obtain the confidence of the target algorithm based on the coverage; The SOH calculation module 305 is configured to calculate the target SOH of the target battery by using the target algorithm based on the confidence level.

[0082] In an optional embodiment, the SOH calculation module 305 may also be used to: select a target algorithm with the highest confidence level from among the multiple target algorithms to calculate the target SOH of the target battery.

[0083] In an optional embodiment, the SOH calculation module 305 may also be used to: calculate a first SOH of the target battery by using the target algorithm; and adjust the first SOH based on the confidence level to obtain the target SOH.

[0084] In an optional embodiment, the state distribution determination module 303 may also be used to draw a state distribution curve of the error based on the mean and the standard deviation.

[0085] In an optional embodiment, the battery SOH calculation device 30 may also include: a first confidence update module, used to obtain preset rules for the attenuation of the confidence with the usage time of the target battery and / or the charge and discharge cycles experienced, and update the confidence according to the preset rules; wherein the preset rules include at least one of the following: the mean changes with the usage time of the target battery; the standard deviation changes with the usage time of the target battery; the mean changes with the charge and discharge cycles experienced by the target battery; the standard deviation changes with the charge and discharge cycles experienced by the target battery.

[0086] In an optional embodiment, in response to the preset rules including multiple ones, the battery SOH calculation device 30 may also include: a second confidence update module, used to update the confidence respectively based on the multiple preset rules; select the confidence with the smallest value from the updated multiple confidences, and calculate the target SOH of the target battery through the target algorithm based on the selected confidence.

[0087] In an optional embodiment, the preset rule includes that the mean value increases with the increase in the usage time of the target battery, and the first confidence update module can also be used to: redetermine the state distribution of the error based on the increased mean value and the standard deviation.

[0088] In an optional embodiment, the preset rule includes that the mean value increases as the charge and discharge cycles experienced by the target battery increase, and the first confidence update module can also be used to: redetermine the state distribution of the error based on the increased mean value and the standard deviation.

[0089] The details of the battery SOH calculation device 30 can be found in Figure 1 The relevant description of the battery SOH calculation method is not repeated here.

[0090] According to another embodiment of the present application, a computing device is also provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, the steps of the battery SOH calculation method in any of the above embodiments can be implemented. The computing device may refer to a new energy device such as a car or aircraft with a battery, or may refer to a BMS or other controller, or may refer to a server (such as a cloud server) or a charging and swapping device (such as a charging pile, a battery swapping station, etc.).

[0091] The computing device may include a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computing device is used to provide computing and control capabilities. The memory of the computing device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computing device is used to exchange information between the processor and an external device. The communication interface of the computing device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a thermal management control method for a power battery is implemented. The display unit of the computing device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computing device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computing device shell, or an external keyboard, touchpad or mouse.

[0092] According to another embodiment of the present application, a computer-readable storage medium is provided for storing a computer program, wherein the program, when executed by a computer or a processor, implements the steps of the battery SOH calculation method in any of the above-mentioned embodiments.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0094] Those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described herein may be implemented as electronic hardware, computer software, or a combination of both.

[0095] In order to show the interchangeability between hardware and software, various schematic components, blocks, modules, circuits and steps are generally described above according to their functionality. Such functionality is implemented in hardware form or software form depending on the specific application and the design restrictions imposed on the overall system. Those skilled in the art can implement the described functionality in a variable manner for specific specific applications, but such implementation decisions should not be understood as causing deviations from the scope of the present application.

[0096] Although only some of the specific embodiments of the present application are described, it should be understood by those skilled in the art that the present application can be implemented in many other forms without departing from its subject matter and scope. Therefore, the examples and embodiments shown are considered to be illustrative rather than restrictive, and the present application may include various modifications and substitutions without departing from the spirit and scope of the present application as defined in the appended claims.

[0097] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as a preferred embodiment as above, it is not intended to limit the present application. Any technician familiar with the profession can make some changes or modifications to the equivalent embodiments of the above disclosed technical contents without departing from the scope of the technical solution of the present application. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for calculating the SOH of a battery, characterized in that: The method comprises: Calculate the SOH of the sample battery according to the target algorithm to obtain a calculated value, and obtain the real value of the SOH of the sample battery; Calculating the error between the calculated value and the true value, and calculating the mean and standard deviation of a plurality of the errors; determining a state distribution of the error based on the mean and the standard deviation; Determining the coverage of the state distribution within a preset accuracy range, and obtaining the confidence of the target algorithm based on the coverage; Based on the confidence level, a target SOH of a target battery is calculated by the target algorithm.

2. The method according to claim 1, characterized in that The calculating the target SOH of the target battery by the target algorithm based on the confidence level includes: Among the multiple target algorithms, a target algorithm with the highest confidence is selected to calculate the target SOH of the target battery.

3. The method according to claim 1, characterized in that: The calculating the target SOH of the target battery by the target algorithm based on the confidence level includes: Calculate the first SOH of the target battery by using the target algorithm; The first SOH is adjusted based on the confidence level to obtain the target SOH.

4. The method according to claim 1, characterized in that: The determining the state distribution of the error based on the mean and the standard deviation comprises: A state distribution curve of the error is drawn based on the mean and the standard deviation.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining a preset rule that the confidence level decays with the usage time and / or charge-discharge cycle experience of the target battery, and updating the confidence level according to the preset rule; Among them, the preset rules include at least one of the following: the mean changes with the usage time of the target battery; the standard deviation changes with the usage time of the target battery; the mean changes with the charge and discharge cycles experienced by the target battery; the standard deviation changes with the charge and discharge cycles experienced by the target battery.

6. The method according to claim 5, characterized in that In response to the preset rules including multiple ones, the method further includes: updating the confidence levels respectively based on a plurality of the preset rules; The confidence level with the smallest value is selected from the updated multiple confidence levels, and based on the selected confidence level, the target SOH of the target battery is calculated by the target algorithm.

7. The method according to claim 5, characterized in that The preset rule includes that the mean value increases with the increase of the usage time of the target battery, and updating the confidence according to the preset rule includes: The error state distribution is re-determined based on the enlarged mean and the standard deviation.

8. The method according to claim 5 or 7, characterized in that: The preset rule includes that the mean value increases with an increase in the charge and discharge cycles experienced by the target battery, and updating the confidence level according to the preset rule includes: The error state distribution is re-determined based on the enlarged mean and the standard deviation.

9. A computing device comprising a memory and a processor, characterized in that: The memory stores a computer program, which, when executed by the processor, can implement the steps of the method for calculating the SOH of a battery according to any one of claims 1 to 8.

10. A computer-readable storage medium for storing a computer program, characterized in that: When the program is executed by a computer or a processor, the program implements the steps of the battery SOH calculation method according to any one of claims 1 to 8.

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