Method and device for calculating health degree of battery and electronic equipment

Abstract

The embodiment of the invention provides a method and device for calculating the health degree of a battery and electronic equipment. The method for calculating the health degree of the battery comprises the steps of obtaining a first characteristic curve used for representing the relationship between the state of charge and the open-circuit voltage of the battery based on battery state data in a first data set; analyzing the first characteristic curve, and establishing a first model of the first characteristic curve; adjusting the first model by using steady-state battery state data in a second data set to obtain a second model for fitting the first characteristic curve, wherein the battery state data in the second data set comprises one or more sections of data corresponding to the complete charging process of the battery; and calculating a battery health (SOH) based on the second model. The demand of data can be reduced, and the efficiency of calculating the health degree of the battery is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of battery technology, and in particular to a method, device, and electronic device for calculating battery health. Background Art

[0002] As power batteries are increasingly used in electric vehicles, power station energy storage, etc., more and more attention is paid to battery management. Among them, the calculation of battery state of health (SOH) is an important part of battery management that cannot be ignored. Battery health is used to characterize the current battery's ability to store electrical energy relative to a new battery.

[0003] In the prior art, the following method can be used to calculate the battery health.

[0004] Method 1: Determine the battery health based on the discharge cut-off voltage and charge cut-off voltage of the single cell in the battery;

[0005] Method 2: Use a constant current charging method, a constant current discharging method or other methods (for example, Patent Document 1: WO2021 / 166465A1) to calibrate the state of charge-open circuit voltage (SOC-OCV) characteristics of the battery, and calculate the battery health based on the SOC-OCV characteristics and using the battery charging data through ampere-hour integration.

[0006] Among them, the state of charge SOC can represent the current power of the battery.

[0007] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because these solutions are described in the background technology part of the present application. Summary of the invention

[0008] The inventor of the present application has found that the method for calculating battery health in the prior art has some limitations, such as:

[0009] The error between the battery health calculated by method 1 and the actual available capacity of the battery is large;

[0010] In method 2, when the SOC-OCV characteristics of the battery are calibrated using the constant current charging method or the constant current discharging method, an offline test is required for the battery to collect relevant data, which is time-consuming and affects the normal use of the battery.

[0011] In method 2, when other methods are used to calibrate the SOC-OCV characteristics of the battery, although it is not necessary to conduct offline tests on the battery, it is necessary to use the battery's usage data (for example, battery charge and discharge data, etc.) over a longer period of time (for example, more than one month). However, as battery users pay more and more attention to data security, it will become increasingly difficult to obtain such a large amount of data.

[0012] In response to at least one of the above-mentioned technical problems or other similar problems, an embodiment of the present application provides a method, device and electronic device for calculating battery health. In the method for calculating battery health, a small amount of battery status data can be used to generate a SOC-OCV characteristic curve, and then a model is obtained according to the steady-state battery status data to fit the SOC-OCV characteristic curve, and the battery health is calculated based on the model. Thus, a model can be generated by a small amount of battery status data to accurately represent the SOC-OCV characteristics, thereby reducing the data demand and improving the efficiency of calculating the battery health.

[0013] According to one aspect of an embodiment of the present application, a method for calculating a battery state of health (SOH) is provided, the method comprising:

[0014] Based on the battery state data in the first data set, a first characteristic curve for representing the relationship between the state of charge (SOC) and the open circuit voltage (OCV) of the battery is obtained;

[0015] Analyzing the first characteristic curve and establishing a first model of the first characteristic curve;

[0016] Using steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, the battery status data in the second data set comprising one or more segments of data corresponding to a complete battery charging process; and

[0017] Based on the second model, the state of health (SOH) of the battery is calculated.

[0018] According to another aspect of an embodiment of the present application, a device for calculating a battery state of health (SOH) is provided, the device comprising:

[0019] a first acquisition unit, which obtains a first characteristic curve representing a relationship between a state of charge (SOC) and an open circuit voltage (OCV) of a battery based on the battery state data in the first data set;

[0020] A first processing unit, which analyzes the first characteristic curve and establishes a first model of the first characteristic curve;

[0021] a second processing unit, which uses steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, wherein the battery status data in the second data set includes one or more segments of data corresponding to a complete charging process of the battery; and

[0022] The third processing unit calculates a battery state of health (SOH) based on the second model.

[0023] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the method for calculating the state of health (SOH) of a battery as described above.

[0024] One of the beneficial effects of the embodiments of the present application is that a SOC-OCV characteristic curve is generated using a small amount of battery status data, and then a model is obtained according to the steady-state battery status data to fit the SOC-OCV characteristic curve, and the battery health is calculated based on the model. Thus, a model can be generated by a small amount of battery status data to accurately represent the SOC-OCV characteristics, thereby reducing the data demand and improving the efficiency of calculating the battery health.

[0025] With reference to the following description and accompanying drawings, the specific implementation of the embodiment of the present application is disclosed in detail, indicating the way in which the principle of the embodiment of the present application can be adopted. It should be understood that the implementation of the present application is not limited in scope. Within the scope of the spirit and clauses of the attached claims, the implementation of the present application includes many changes, modifications and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The included drawings are used to provide a further understanding of the embodiments of the present application, which constitute a part of the specification, are used to illustrate the implementation methods of the present application, and together with the text description, explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other implementation methods can be obtained based on these drawings without creative work. In the drawings:

[0027] Figure 1 is a schematic diagram of a method for calculating battery health according to an embodiment of the present application;

[0028] Figure 2 It is a schematic diagram of the PP diagram of ocv_bacs;

[0029] Figure 3 It is a schematic diagram of the first model;

[0030] Figure 4This is a schematic diagram of the PP diagram of ocv_train;

[0031] Figure 5 is a schematic diagram of the second model;

[0032] Figure 6 is a schematic diagram of calculating battery health based on the second model;

[0033] Figure 7 is a schematic diagram of a device for calculating the state of health (SOH) of a battery according to an embodiment of the present application;

[0034] Figure 8 It is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] With reference to the accompanying drawings, the foregoing and other features of the embodiments of the present application will become apparent through the following description. In the description and the accompanying drawings, specific embodiments of the present application are specifically disclosed, which show some embodiments in which the principles of the embodiments of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the embodiments of the present application include all modifications, variations and equivalents that fall within the scope of the attached claims.

[0036] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements in terms of title, but do not indicate the spatial arrangement or temporal order of these elements, etc., and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0037] In the embodiments of the present application, the singular forms "a", "the", etc. include plural forms and should be broadly understood as "a kind" or "a type" rather than being limited to the meaning of "one"; in addition, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least in part according to...", and the term "based on" should be understood as "at least in part based on...", unless the context clearly indicates otherwise.

[0038] Features described and / or shown for one embodiment may be used in one or more other embodiments in the same or similar manner, combined with features in other embodiments, or replace features in other embodiments. The term "includes / comprising" when used herein refers to the presence of a feature, an entirety, a step, or a component, but does not exclude the presence or addition of one or more other features, entireties, steps, or components.

[0039] Embodiments of the first aspect

[0040] An embodiment of the present application provides a method for calculating the state of health (SOH) of a battery.

[0041] Figure 1 FIG. 1 is a schematic diagram of a method for calculating battery health according to an embodiment of the present application. Figure 1 As shown, the method includes:

[0042] Operation 101: obtaining a first characteristic curve for representing a relationship between a state of charge (SOC) and an open circuit voltage (OCV) of a battery based on battery state data in a first data set;

[0043] Operation 102: analyzing the first characteristic curve and establishing a first model of the first characteristic curve;

[0044] Operation 103: using steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, wherein the battery status data in the second data set includes one or more segments of data corresponding to a complete battery charging process; and

[0045] Operation 104: Calculate the battery state of health (SOH) based on the second model.

[0046] In the present application: Operation 101 can use a small amount of battery status data to generate a first characteristic curve (for example, an SOC-OCV characteristic curve). Compared with the prior art, this operation uses a small amount of data, so the first characteristic curve will suffer from some loss in accuracy; in order to reduce this loss, the method of the present application introduces operation 103. In operation 103, a small amount of steady-state data during the complete charging process of the battery is used to adjust the first model used to reflect the SOC-OCV characteristic curve, thereby obtaining a second model, which can more accurately reflect the SOC-OCV characteristics in the steady-state state during the battery charging process.

[0047] Therefore, the present application can obtain accurate SOC-OCV characteristics based on a smaller amount of data, and then calculate the battery health, thereby reducing the amount of data required and improving the efficiency of calculating the battery health.

[0048] In the present application, the battery may be a rechargeable battery (eg, a lithium-ion battery, etc.) formed by connecting a plurality of battery cells in parallel and / or in series.

[0049] In operation 101 , a first characteristic curve is obtained based on battery state data in a first data set.

[0050] The battery status data in the first data set may be battery status data within a shorter period of time, for example, battery status data within a few days or weeks.

[0051] In some embodiments of operation 101, the battery status data in the first data set may be the battery status data of the electric device when the battery is installed in the electric device and the electric device is operating normally. The electric device is, for example, an electric car or an energy storage device. The battery status data includes, for example: the voltage of the battery as a whole and each battery cell at multiple times, and / or the current of the battery as a whole and each battery cell at multiple times, and / or the temperature of the battery as a whole and each battery cell at multiple times. In addition, the battery status data may also be associated with the operating data of the electric device. For example, the operating data of the electric device may be the mileage of the electric vehicle, or the operating time of the energy storage device.

[0052] In some of these embodiments, the battery status data in the first data set can be used to obtain the SOC-OCV characteristics based on the method described in patent document 1 (WO2021 / 166465A1), thereby generating a first characteristic curve.

[0053] In other embodiments of operation 101, other methods may be used to obtain the SOC-OCV characteristic, for example, using a constant current charging method or a constant current discharging method to obtain the SOC-OCV characteristic, thereby generating a first characteristic curve. In the constant current charging method or the constant current discharging method, the battery state data in the first data set may be the voltage of the entire battery and each battery cell at multiple times, and / or the current of the entire battery and each battery cell at multiple times, and / or the temperature of the entire battery and each battery cell at multiple times, etc., obtained during the charging or discharging process of the battery.

[0054] In addition, if Figure 1 As shown, before operation 101 of the present application, there may be operation 101a:

[0055] Operation 101a: Process the battery status data in the first data set.

[0056] After the processing of operation 101a, in operation 101, the processed battery state data may be used to generate the first characteristic curve, thereby improving the accuracy of the first characteristic curve.

[0057] In operation 101a, processing the battery status data in the first data set may include: sorting the battery status data in the first data set in chronological order, and deleting time-repeated data and / or abnormal data.

[0058] In operation 101a, deleting the time-duplicate data may be: if multiple data of the same type (for example, voltage data, current data or temperature data, etc.) correspond to the same moment, then the last data of the same type at that moment is deleted, and the other data of the same type at that moment is deleted.

[0059] In operation 101a, abnormal data may include: battery status data that is inconsistent with the use of the battery, for example, the mileage of the electric vehicle corresponding to the battery status data decreases with time, etc.; and / or abnormal temperature data, for example, battery status data in which the temperature of a single cell exceeds a predetermined temperature (for example, the predetermined temperature is 100 degrees Celsius, etc.); and / or abnormal voltage data, for example, data in which the voltage of a single cell is outside a predetermined voltage range (for example, the predetermined voltage range is greater than or equal to 2V, and less than or equal to 5V).

[0060] It should be noted that, in the present application, operation 101a is optional.

[0061] In the present application, operation 102 may be performed when the battery status data of the first data set (or the battery status data processed by operation 101a) conforms to the statistical law. For example, the SOC (for example, which may be recorded as soc_bacs) and OCV (for example, which may be recorded as ocv_bacs) corresponding to the plurality of data respectively may be extracted from the battery status data processed by operation 101a, wherein the SOC and OCV may be obtained by calculating the battery status data, or the SOC may be data reported by the vehicle battery management system (BMS) in the battery status data; if the position value of ocv_bacs is greater than a predetermined value (for example, the predetermined value is 3), and the PP graph of ocv_bacs conforms to the normal distribution, it is determined that the battery status data of the first data set conforms to the statistical law, and operation 102 may be performed, wherein the position value may represent the value corresponding to the position of the data point (for example, ocv_bacs) in the data distribution graph, and the position value reflects the relative relationship between the actual distribution of the data and the theoretical distribution.

[0062] Figure 2 This is a schematic diagram of the PP diagram of ocv_bacs. Figure 2 As shown, the horizontal axis represents the cumulative probability of observation of ocv_bacs, and the vertical axis represents the deviation from the normal distribution.

[0063] In operation 102 of the present application, the first characteristic curve obtained by operation 101 may be analyzed to establish a first model for fitting the first characteristic curve. The method for establishing the first model may refer to related technologies, for example, the first model may be established based on machine learning using soc_bacs and ocv_bacs.

[0064] For example, the first model may be a cubic function. In one example, the first model may have the form of the following formula (1):

[0065] Y=-5.357X 3 +362.888X-1025.775 (1)

[0066] In formula (1), X represents the state of charge (SOC) of the battery, and Y represents the open circuit voltage (OCV) of the battery.

[0067] Figure 3 is a schematic diagram of the first model. Figure 3 As shown, the abscissa represents SOC [%], and the ordinate represents OCV [V]; the curve represented by the first model is marked as 300, Figure 3 The circle 301 in FIG. 3 represents data corresponding to soc_bacs and ocv_bacs.

[0068] In operation 103, the first model is adjusted using the steady-state battery status data in the second data set. The battery status data in the second data set includes one or more segments (e.g., 3714 segments) of data corresponding to the complete charging process of the battery. The data of the battery's completed charging process includes: battery status data before charging, battery status data during charging, and battery status data after charging is completed.

[0069] Among them, the steady-state battery status data includes: battery status data in which the current value and / or voltage value in one or more segments of data corresponding to the complete charging process of the battery meet predetermined conditions; and / or battery status data in which the fluctuation value of the current value and / or voltage value within a predetermined time period is less than a predetermined threshold in one or more segments of data corresponding to the complete charging process of the battery.

[0070] Steady-state battery status data can more accurately reflect the SOC-OCV characteristics of the battery, so operation 103 uses the steady-state battery status data in the second data set to adjust the first model to obtain a second model that more accurately reflects the SOC-OCV characteristics of the battery.

[0071] In operation 103 , the steady-state battery status data in the second data set may come from the battery status data in the first data set, or from the battery status data processed in operation 101 a , or from battery status data outside the first data set.

[0072] In the present application, operation 103 may be performed when the steady-state battery status data in the second data set conforms to the statistical law. For example, the SOC (for example, which may be recorded as soc_train) and OCV (for example, which may be recorded as ocv_train) corresponding to a plurality of data may be extracted from the steady-state battery status data in the second data set, wherein the SOC and OCV may be obtained by calculating the battery status data, or the SOC may be data reported by the vehicle battery management system (BMS) in the second data set; if the position value of ocv_train is greater than a predetermined value (for example, the predetermined value is 3), and the PP graph of ocv_train conforms to the normal distribution, it is determined that the steady-state battery status data in the second data set conforms to the statistical law, and operation 103 may be performed, wherein the position value may represent the numerical value corresponding to the position of the data point (for example, ocv_train) in the data distribution graph, and the position value reflects the relative relationship between the actual distribution of the data and the theoretical distribution.

[0073] Figure 4 This is a schematic diagram of the PP diagram of ocv_train. Figure 4 As shown, the horizontal axis represents the cumulative probability of ocv_train observations, and the vertical axis represents the deviation from the normal distribution.

[0074] In operation 103, soc_trains and ocv_train can be used to adjust the parameters in the first model based on machine learning to obtain a second model, so that the second model can fit the first characteristic curve generated in operation 101, for example, the deviation of the second model relative to the first characteristic curve is less than a threshold.

[0075] For example, the second model may be a function of the same type as the first model (for example, both are cubic functions). In one example, the second model may have the form of the following formula (2):

[0076] Y=6.648X 3 -4.976X 2 +0.006X+3.429 (2)

[0077] In formula (2), X represents the state of charge (SOC) of the battery, and Y represents the open circuit voltage (OCV) of the battery.

[0078] In this application, if Figure 1 As shown, after operation 103, there may be operation 103a:

[0079] Operation 103a: Validate the second model using the first characteristic curve.

[0080] Through operation 103a, the second model obtained in operation 103 can be verified by using the first characteristic curve generated in operation 101. When the difference between the two is less than a predetermined value, it is determined that the second model meets the requirements. In addition, if it is determined that the second model is significantly different from the first characteristic curve, operation 103 is repeated until a second model that meets the requirements is generated.

[0081] Figure 5 is a schematic diagram of the second model. Figure 5 As shown, the horizontal axis represents SOC [%], and the vertical axis represents OCV [V]; the curve represented by the second model is marked as 500, Figure 5 The circle 501 in represents the data corresponding to soc_train and ocv_train.

[0082] In operation 104 , a battery state of health (SOH) may be calculated based on the second model obtained in operation 103 .

[0083] Figure 6 This is a schematic diagram of calculating battery health based on the second model. Figure 6 As shown, the method includes:

[0084] Operation 601: Calculate the state of charge (SOC1) of the battery before charging starts and the state of charge (SOC2) of the battery after charging is completed in each complete charging process by using the second model, and obtain a change in the state of charge of each complete charging process;

[0085] Operation 602, calculating a full charge capacity (FCC) of the battery based on the change in the state of charge (delta SOC) and the charge amount of the complete charging process; and

[0086] Operation 603: Calculate the health of the battery based on the full charge capacity.

[0087] In at least one embodiment, in operation 601, for at least one complete charging process in the second data set, the open circuit voltage OCV1 of the battery before charging starts and the open circuit voltage OCV2 of the battery after charging are extracted, and OCV1 and OCV2 are respectively substituted into the second model, so that the state of charge (SOC1) of the battery before charging starts corresponding to OCV1 and the state of charge (SOC2) of the battery after charging are respectively obtained. Then, the change in the state of charge of the complete charging process is calculated, that is, delta SOC=|SOC2-SOC1|.

[0088] In operation 602, the full charge capacity (FCC) of the battery is calculated according to the change in the state of charge (delta SOC) during the complete charging process and the charge amount during the complete charging process. For example, the full charge capacity of the battery can be calculated by the following formula (3):

[0089] FCC = charge capacity of the complete charging process / delta SOC (3)

[0090] The charging amount of the complete charging process can be obtained, for example, by integrating the battery status data of the complete charging process in ampere-hours.

[0091] In operation 603, the state of health (SOH) of the battery is calculated based on the full charge capacity (FCC), for example, it can be calculated by the following formula (4):

[0092] SOH = full charge capacity / rated capacity of the battery (4)

[0093] Among them, the rated capacity of the battery is a known quantity and can be determined based on factors such as the type, model, and specifications of the battery.

[0094] The above only describes the steps or processes related to the present application, but the present application is not limited to this. The method for calculating the state of health of the battery (SOH) may also include other steps or processes. For the specific content of these steps or processes, refer to the prior art. In addition, the above only uses some operations used in the method for calculating the state of health of the battery (SOH) as an example to illustrate the embodiments of the present application, but the present application is not limited to these structures, and these operations can also be appropriately modified. The implementation methods of these modifications should all be included in the scope of the embodiments of the present application.

[0095] The above embodiments are merely exemplary of the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0096] According to the embodiment of the first aspect of the present application, accurate SOC-OCV characteristics can be obtained based on a smaller amount of data, and then the battery health can be calculated, thereby reducing the amount of data required and improving the efficiency of calculating the battery health.

[0097] Embodiments of the second aspect

[0098] The embodiment of the present application provides a device for calculating the state of health (SOH) of a battery, which corresponds to the method for calculating the state of health (SOH) of a battery in the embodiment of the first aspect. The embodiment of the second aspect is the same as the embodiment of the first aspect and will not be repeated.

[0099] Figure 7 is a schematic diagram of a device for calculating the battery health (SOH) according to an embodiment of the present application, such as Figure 7 As shown, the device 700 includes:

[0100] A first acquisition unit 701, which obtains a first characteristic curve representing the relationship between the state of charge (SOC) and the open circuit voltage (OCV) of the battery based on the battery state data in the first data set;

[0101] A first processing unit 702, which analyzes the first characteristic curve and establishes a first model of the first characteristic curve;

[0102] A second processing unit 703, which uses steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, wherein the battery status data in the second data set includes one or more segments of data corresponding to a complete charging process of the battery; and

[0103] The third processing unit 704 calculates a battery state of health (SOH) based on the second model.

[0104] In some embodiments, the steady-state battery status data includes:

[0105] The current value and / or voltage value of the battery status data meets a predetermined condition; and / or,

[0106] A fluctuation value of a current value and / or a voltage value of the battery status data within a predetermined time period is smaller than a predetermined threshold value.

[0107] In some embodiments, the second processing unit 703 further uses the first characteristic curve to verify the second model.

[0108] In some embodiments, Figure 7 As shown, the device 700 also includes:

[0109] The pre-processing unit 700a processes the battery status data in the first data set.

[0110] The first acquisition unit 701 generates the first characteristic curve based on the data processed by the preprocessing unit 700a. The processing performed by the preprocessing unit 700a includes:

[0111] The battery status data in the first data set are sorted in chronological order, and time-duplicate data and / or abnormal data are deleted.

[0112] In some embodiments, the third processing unit 704 calculates the battery state of health (SOH) based on the second model, including:

[0113] Using the second model, the state of charge (SOC1) of the battery before charging starts and the state of charge (SOC2) of the battery after charging is completed in each of the complete charging processes are calculated to obtain a change in the state of charge of each complete charging process;

[0114] Calculating the full charge capacity (FCC) of the battery based on the change in the state of charge (delta SOC) and the charge amount of the complete charging process; and

[0115] A health of the battery is calculated based on the full charge capacity.

[0116] It is worth noting that the above only describes the components or modules related to the present application, but the present application is not limited thereto. The device for calculating the battery health (SOH) may also include other components or modules, and the specific contents of these components or modules may refer to the relevant technology.

[0117] To keep it simple, Figure 7 The connection relationship or signal direction between various components or modules is only exemplified, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors and memories; the embodiments of the present application are not limited to this.

[0118] The above embodiments are merely exemplary of the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0119] Embodiments of the third aspect

[0120] The embodiment of the present application provides an electronic device, including the device 700 for calculating the battery health (SOH) as described in the embodiment of the second aspect, and the content thereof is incorporated herein. The electronic device may be, for example, a computer, a server, a workstation, a laptop computer, a smart phone, etc., but the embodiment of the present application is not limited thereto.

[0121] Figure 8 Schematic diagram of an electronic device according to an embodiment of the present application. Figure 8 As shown, the electronic device 800 may include: a processor (e.g., a central processing unit CPU) 810 and a memory 820; the memory 820 is coupled to the central processing unit 810. The memory 820 may store various data; in addition, it may store a program 821 for information processing, and execute the program 821 under the control of the processor 810.

[0122] In some embodiments, the function of the apparatus 700 for calculating the state of health (SOH) of a battery is integrated into the processor 810. The processor 810 is configured to implement the method for calculating the state of health (SOH) of a battery as described in the embodiment of the first aspect.

[0123] In some embodiments, the device 700 for calculating the battery health (SOH) is configured separately from the processor 810. For example, the device 700 for calculating the battery health (SOH) can be configured as a chip connected to the processor 810, and the function of calculating the battery health (SOH) is realized through the control of the processor 810.

[0124] For example, the processor 810 is configured to perform the following control: based on the battery status data in the first data set, obtain a first characteristic curve for representing the relationship between the state of charge (SOC) and the open circuit voltage (OCV) of the battery; analyze the first characteristic curve and establish a first model of the first characteristic curve; use the steady-state battery status data in the second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, the battery status data in the second data set includes one or more segments of data corresponding to the complete charging process of the battery; and based on the second model, calculate the battery health (SOH).

[0125] In some embodiments, the steady-state battery status data includes:

[0126] The current value and / or voltage value of the battery status data meets a predetermined condition; and / or,

[0127] A fluctuation value of a current value and / or a voltage value of the battery status data within a predetermined time period is smaller than a predetermined threshold value.

[0128] In some embodiments, the second model is verified using the first characteristic curve.

[0129] In some embodiments, the battery status data in the first data set is processed, wherein the processed data is used to generate the first characteristic curve, and the processing includes:

[0130] The battery status data in the first data set are sorted in chronological order, and time-duplicate data and / or abnormal data are deleted.

[0131] In some embodiments, based on the second model, calculating the battery state of health (SOH) includes:

[0132] Using the second model, the state of charge (SOC1) of the battery before charging starts and the state of charge (SOC2) of the battery after charging is completed in each of the complete charging processes are calculated to obtain a change in the state of charge of each complete charging process;

[0133] Calculating the full charge capacity (FCC) of the battery based on the change in the state of charge (delta SOC) and the charge amount of the complete charging process; and

[0134] A health of the battery is calculated based on the full charge capacity.

[0135] In addition, if Figure 8 As shown, the electronic device 800 may also include: an input / output (I / O) device 830 and a display 840, etc.; wherein the functions of the above components are similar to those of the prior art and are not described in detail here. It is worth noting that the electronic device 800 does not necessarily have to include Figure 8 In addition, the electronic device 800 may also include Figure 8 For components not shown in the figure, reference may be made to the related art.

[0136] An embodiment of the present application also provides a computer-readable program, wherein when the program is executed in an electronic device, the program causes a computer to execute in the electronic device the method for calculating the state of health (SOH) of a battery as described in the embodiment of the first aspect.

[0137] An embodiment of the present application also provides a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the method for calculating the battery state of health (SOH) as described in the embodiment of the first aspect in an electronic device.

[0138] The above devices and methods of the present application can be implemented by hardware, or by hardware combined with software. The present application relates to such a computer-readable program, which, when executed by a logic component, enables the logic component to implement the above-mentioned devices or components, or enables the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0139] The method / device described in conjunction with the embodiments of the present application may be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or one or more combinations of the functional block diagrams may correspond to various software modules of the computer program flow or to various hardware modules. These software modules may correspond to the various steps shown in the figure, respectively. These hardware modules may be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0140] The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in a memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if a device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0141] For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in the present application. For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0142] The present application is described above in conjunction with specific implementation methods, but it should be clear to those skilled in the art that these descriptions are exemplary and are not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on the principles of the present application, and these modifications and variations are also within the scope of the present application.

Claims

1. A method for calculating battery health, characterized in that: The method comprises: Based on the battery state data in the first data set, a first characteristic curve for representing the relationship between the state of charge and the open circuit voltage of the battery is obtained; Analyzing the first characteristic curve and establishing a first model of the first characteristic curve; Using steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, the battery status data in the second data set comprising one or more segments of data corresponding to a complete battery charging process; and Based on the second model, the battery health is calculated.

2. The method of claim 1, wherein: The steady-state battery status data includes: The current value and / or voltage value of the battery status data meets a predetermined condition; and / or, A fluctuation value of a current value and / or a voltage value of the battery status data within a predetermined time period is smaller than a predetermined threshold value.

3. The method of claim 1, wherein: The method further comprises: The second model is verified using the first characteristic curve.

4. The method of claim 1, wherein: The method further comprises: Processing the battery status data in the first data set, wherein the processed data is used to generate the first characteristic curve, The processing includes: The battery status data in the first data set are sorted in chronological order, and time-duplicate data and / or abnormal data are deleted.

5. The method of claim 1, wherein: Calculating the battery health based on the second model includes: Utilizing the second model, calculating the state of charge of the battery before charging starts and the state of charge of the battery after charging is completed in each of the complete charging processes, and obtaining a change in the state of charge in each of the complete charging processes; Calculating the full charge capacity of the battery based on the change in the state of charge and the charge amount of the complete charging process; and A health of the battery is calculated based on the full charge capacity.

6. A device for calculating battery health, characterized in that: The device comprises: A first acquisition unit, which obtains a first characteristic curve representing a relationship between a state of charge and an open circuit voltage of a battery based on the battery state data in the first data set; A first processing unit, which analyzes the first characteristic curve and establishes a first model of the first characteristic curve; a second processing unit, which uses steady-state battery status data in a second data set to adjust the first model to obtain a second model for fitting the first characteristic curve, wherein the battery status data in the second data set includes one or more segments of data corresponding to a complete charging process of the battery; and A third processing unit calculates the battery health based on the second model.

7. The device according to claim 6, wherein: The steady-state battery status data includes: The current value and / or voltage value of the battery status data meets a predetermined condition; and / or, A fluctuation value of a current value and / or a voltage value of the battery status data within a predetermined time period is smaller than a predetermined threshold value.

8. The device of claim 6, wherein: The second processing unit further verifies the second model using the first characteristic curve.

9. The device according to claim 6, wherein: The device also includes: a preprocessing unit, which processes the battery status data in the first data set, Wherein, the first acquisition unit generates the first characteristic curve based on the processed data, The processing includes: The battery status data in the first data set are sorted in chronological order, and time-duplicate data and / or abnormal data are deleted.

10. The device according to claim 6, wherein: The third processing unit calculates the battery health based on the second model, including: Utilizing the second model, calculating the state of charge of the battery before charging starts and the state of charge of the battery after charging is completed in each of the complete charging processes, and obtaining a change in the state of charge in each of the complete charging processes; Calculating the full charge capacity of the battery based on the change in the state of charge and the charge amount of the complete charging process; and A health of the battery is calculated based on the full charge capacity.

11. An electronic device, characterized in that: The electronic device comprises the device for calculating the battery health as claimed in any one of claims 6 to 10.

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