Method and device for evaluating residual life of battery based on measured data of battery capacity

Abstract

The invention discloses a method and device for evaluating the remaining life of a battery based on battery capacity measured data and computing equipment, and the method comprises the steps: carrying out the charging and discharging test of the battery based on an actual working environment, and determining the minimum remaining capacity of the current battery according to the capacity when the battery begins to fail; according to the test data, establishing a mathematical model of the residual capacity of the battery and the number of charge-discharge cycles, and substituting the minimum residual capacity of the battery into the pre-established mathematical model to calculate the actual number of charge-discharge cycles of the battery; and calculating the remaining life of the battery according to the actual cycle index and the cycle period. The embodiment of the invention can effectively evaluate the residual life of the solar tracking system battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, in particular, to a method and device for evaluating the remaining life of a battery based on measured battery capacity data and a computing device. BACKGROUND

[0002] In the field of new energy, batteries are widely used. For example, batteries are often used as power sources in solar tracking support tracking systems. The application scenarios and charging and discharging methods of the batteries directly affect the service life of the batteries. However, there is no good way to simulate the application scenarios of the project site to evaluate the service life of the batteries.

[0003] Sections 7.2 and 7.3 of the ICS43.10CCST47 group standard T / CSAE41-01 Guidelines for Evaluating the Remaining Life of Power Batteries for Electric Vehicles propose guidelines for evaluating the remaining life of power batteries in the electric vehicle industry. However, no specific model is established and no evaluation method is proposed. Moreover, the charging and discharging methods and application scenarios of the batteries in the solar industry and electric vehicles are different. Therefore, it is urgent to propose a method for evaluating the remaining life of a battery based on battery capacity monitoring data in an actual application scenario to provide a theoretical basis for the storage of spare parts and maintenance of the battery in the project operation stage. SUMMARY

[0004] One of the purposes of the embodiments of the present application is to address the deficiencies of the prior art. A method for evaluating the remaining life of a battery based on measured battery capacity data is proposed to establish a specific mathematical model in the case where the application scenarios and charging and discharging methods of the battery are different, and to evaluate the remaining life of the battery through the effectiveness verification of the model.

[0005] To solve the above technical problems, in a first aspect, the embodiments of the present application provide a method for evaluating the remaining life of a battery based on measured battery capacity data, which comprises:

[0006] Based on the actual working environment, the battery is subjected to charging and discharging tests, and the minimum remaining capacity of the current battery is determined according to the capacity of the battery when it starts to fail.

[0007] According to the test data, a mathematical model of the remaining capacity of the battery and the number of charging and discharging cycles is established, and the actual number of cycles of the battery is calculated by substituting the minimum remaining capacity of the battery into the pre-established mathematical model.

[0008] The remaining life of the battery is calculated according to the actual number of cycles and the cycle period.

[0009] Preferably, the mathematical model of the remaining capacity of the battery and the number of charging and discharging cycles is established according to the test data, which specifically comprises:

[0010] According to the predetermined battery working parameters, the battery is placed in an environment corresponding to the working parameters, and the battery is charged and discharged using the charge and discharge current value and time in the working parameters;

[0011] The battery cycle number and the battery remaining capacity value are collected, the battery cycle number is set as a factor X, and the battery remaining capacity value is set as an effect Y;

[0012] The battery remaining capacity values corresponding to different cycle numbers of the battery are recorded, and based on the collected battery charge and discharge cycle number and battery remaining capacity value data, a plurality of mathematical models are established;

[0013] The plurality of mathematical models established are evaluated for effectiveness.

[0014] Preferably, the mathematical models include but are not limited to one or a combination of more than two of the following models:

[0015] Exponential model Y = a * e, linear model Y = a * X + b, logarithmic model Y = a * ln(X) + b, and polynomial model Y = a * X 2 + b * X + c.

[0016] Preferably, the evaluation of the plurality of mathematical models established for effectiveness specifically includes:

[0017] The fitting coefficient and fitting error of each mathematical model are calculated;

[0018] The mathematical model is selected according to a preset fitting coefficient threshold and fitting error threshold, and the fitting coefficient threshold and fitting error threshold are set according to the working parameters.

[0019] Further, the method further includes:

[0020] The working parameters of the battery are determined according to the working mode of the battery in the actual application scenario, and the parameters include but are not limited to temperature curve, maximum battery charge / discharge current, and charge / discharge time.

[0021] In a second aspect, to solve the technical problem of the present application, an embodiment of the present application further provides a device for evaluating the remaining life of a battery based on actual battery capacity measurement data, the device comprising:

[0022] A battery capacity determination module for performing charge and discharge tests on the battery based on the actual working environment, and determining the minimum remaining capacity of the battery according to the capacity of the battery when it starts to fail;

[0023] A first calculation module for establishing a mathematical model of the remaining capacity of the battery and the charge and discharge cycle number based on the test data, and calculating the actual cycle number of the battery that can be charged and discharged by substituting the minimum remaining capacity of the battery into the previously established mathematical model;

[0024] The second calculation module calculates the remaining life of the battery according to the actual number of cycles and the cycle period.

[0025] Furthermore, the device further includes a model building module, which specifically includes:

[0026] a data acquisition unit, according to predetermined battery operating parameters, placing the battery in an environment corresponding to the operating parameters, charging and discharging the battery using the charge and discharge current values ​​and times in the operating parameters, collecting the number of battery cycles and the remaining battery capacity, and setting the number of battery cycles as a factor X and the remaining battery capacity as an effect Y;

[0027] A mathematical model building unit is used to record the battery remaining capacity values ​​corresponding to different battery cycle times, and to build several mathematical models based on the collected battery charge and discharge cycle times and battery remaining capacity value data;

[0028] The model evaluation unit is used to evaluate the effectiveness of the established mathematical models.

[0029] Preferably, the mathematical model includes but is not limited to one or a combination of two or more of the following models:

[0030] Exponential model Y = a*e, linear model Y = a*X + b, logarithmic model Y = a*ln(X) + b, polynomial model: Y = a*X 2 +b*X+c.

[0031] Preferably, the model evaluation unit specifically includes:

[0032] A subunit for calculating fitting coefficients and fitting errors for each mathematical model;

[0033] The method is used to select a subunit of the mathematical model according to a preset fitting coefficient threshold and a fitting error threshold, wherein the fitting coefficient threshold and the fitting error threshold are set according to working parameters.

[0034] In a third aspect, an embodiment of the present invention further provides a computing device comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method as described above.

[0035] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor or a calculator, the processor executes the method as described above.

[0036] Compared with the prior art, the method for estimating the remaining battery life based on measured battery capacity data provided by the embodiment of the present invention has at least the following beneficial effects:

[0037] Based on the ICS43.10CCST47 group standard T / CSAE41-01 Guidelines for Remaining Lifetime Assessment of Electric Vehicle Power Batteries, the embodiments of the present invention disclose in detail how to establish a mathematical model based on the number of battery charge and discharge test cycles and the remaining battery capacity, and disclose in detail the method and principle for judging the validity of the established mathematical model. This can effectively apply the remaining lifetime assessment method for batteries in the electric vehicle industry to the assessment of the remaining lifetime of batteries in solar tracking systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The preferred embodiments will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0039] Figure 1 This is a flow chart of a method for estimating the remaining battery life based on measured battery capacity data according to an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of working parameters in an actual working environment of a method for estimating remaining battery life based on measured battery capacity data according to an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of a fitting mode of a mathematical model in a method for estimating remaining battery life based on measured battery capacity data according to an embodiment of the present invention;

[0042] Figure 4 The figure is a schematic diagram of the structure of a computing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings and other embodiments can be obtained based on these drawings without inventive work.

[0044] To simplify the drawings, only the parts related to the invention are schematically shown in each figure. They do not represent the actual structure of the product. In addition, to simplify the drawings and facilitate understanding, in some figures, only one of the components with the same structure or function is schematically depicted or labeled. In the embodiments of the present invention, "one" not only means "only one" but also "more than one". The following mainly uses some specific embodiments as examples to describe in detail the implementation of the technical solution of the present invention.

[0045] like Figure 1 As shown, in order to achieve the purpose of the present invention, an embodiment of the present invention provides a method for estimating the remaining life of a battery based on measured battery capacity data, the method comprising:

[0046] Based on the actual working environment, the battery is charged and discharged, and the minimum remaining capacity of the current battery is determined based on the capacity when the battery begins to fail;

[0047] Based on the test data, a mathematical model is established for the remaining capacity of the battery and the number of charge and discharge cycles, and the minimum remaining capacity of the battery is substituted into the pre-established mathematical model to calculate the actual number of charge and discharge cycles that the battery can achieve;

[0048] The remaining battery life is calculated based on the actual number of cycles and the cycle period.

[0049] In this embodiment of the present invention, the calculation of the remaining battery capacity at the time of battery failure is based on the battery's capacity and the load's ability to drive it. Different trackers may have different minimum remaining battery capacity values. Assuming that the battery fails at a minimum remaining capacity of 50%, the minimum remaining capacity of 50% is substituted into a validated mathematical model to calculate the number of battery charge and discharge cycles.

[0050] In photovoltaic systems, due to the inherent characteristics of photovoltaic trackers, 24 hours of laboratory operation is equivalent to 2 days of field operation. Therefore, the remaining battery life is calculated by the number of battery charge and discharge cycles and the cycle period, providing a theoretical basis for the storage and maintenance of battery spare parts during the project operation and maintenance phase. The calculation formula is:

[0051] Remaining battery life = number of cycles * cycle period * 2 days.

[0052] Among them, the cycle period is defined according to the specific circumstances of the experiment, and can be defined as 1 day, 2 days, 3 days... The smaller the cycle period, the denser the data obtained.

[0053] Preferably, in one embodiment of the present invention, the mathematical model is established by the following steps:

[0054] According to the predetermined battery operating parameters, the battery is placed in an environment corresponding to the operating parameters, and the battery is charged and discharged using the charge and discharge current values ​​and times in the operating parameters;

[0055] Collect the battery cycle number and the battery remaining capacity value, and set the battery cycle number as factor X and the battery remaining capacity value as effect Y;

[0056] Record the battery remaining capacity values ​​corresponding to different battery cycle times, and establish several mathematical models based on the collected battery charge and discharge cycle times and battery remaining capacity value data;

[0057] The effectiveness of the established mathematical models is evaluated.

[0058] Furthermore, in one embodiment of the present invention, the method further includes:

[0059] like Figure 2 As shown, the operating parameters of the battery are determined according to the working mode of the battery in the actual application scenario, and the parameters include but are not limited to the temperature curve, the maximum value of the battery charge / discharge current, and the charge and discharge time.

[0060] The embodiment of the present invention determines the operating temperature curve of the battery and operating parameters such as the maximum value and time of the battery charge and discharge current through the application mode of the battery in actual scenarios, providing a basis for the subsequent battery charge and discharge test environment.

[0061] Place the battery in a predetermined operating temperature curve and environment, and use the specified current and time to charge and discharge the battery. Figure 2 As shown, the embodiment of the present invention places the battery in an environment corresponding to the operating parameters, uses the charge and discharge current values ​​and time in the operating parameters to charge and discharge the battery, and collects data on the number of battery cycles and the battery remaining capacity test as shown in Table 1 and Table 2 below (the greater the number of cycles, the more battery remaining capacity value data is collected, and the more accurate the established mathematical model is).

[0062] Table 1:

[0063]

[0064] Table 2:

[0065]

[0066]

[0067] Preferably, in one embodiment of the present invention, the mathematical model includes but is not limited to one or a combination of two or more of the following models:

[0068] Exponential model Y = a*e, linear model Y = a*X + b, logarithmic model Y = a*ln(X) + b, polynomial model: Y = a*X 2 +b*X+c.

[0069] Since it is impossible to determine which mathematical model is used at this time, in the embodiment of the present invention, Figure 3 As shown, typical mathematical models can be established first, and then the validity of the model can be judged by the fitting coefficient and fitting error. The possible mathematical models are as follows:

[0070] Exponential model: Y = a*e

[0071] Linear model: Y = a*X + b

[0072] Logarithmic model: Y = a*ln(X)+b

[0073] Polynomial model: Y = a * X 2 +b*X+c

[0074] Preferably, in one embodiment of the present invention, the effectiveness evaluation of the established mathematical models specifically includes:

[0075] Calculate the fitting coefficient for each mathematical model and determine whether the fitting coefficient is greater than 95%. If so, calculate the fitting error of the mathematical model. If the fitting error is less than 5%, the mathematical model is valid and meets the requirements. If the fitting error is greater than 5%, the mathematical model is invalid and does not meet the requirements. If the fitting coefficient is less than 95%, there is no need to calculate the fitting error, and the mathematical model is directly determined to be invalid and does not meet the requirements. By calculating the fitting coefficient and the fitting error in turn, the validity of the mathematical model is verified according to the preset fitting coefficient threshold and fitting error threshold, thereby ensuring the accuracy of the mathematical model. The larger the fitting coefficient, the larger the range of the model coverage of the measured data, indicating that the model is very valid; the fitting error reflects the average deviation between the predicted value and the actual value. The smaller the deviation, the higher the accuracy of the model. The fitting coefficient threshold and the fitting error threshold are set according to the working parameters.

[0076] After establishing a corresponding mathematical model using the number of battery test cycles (factor X) and the battery remaining capacity (effect Y), the embodiment of the present invention needs to judge the validity of the model through the fitting coefficient and fitting error.

[0077] Fitting coefficient R 2 Indicates the range of coverage of the model for data changes, R 2 The larger the value is, the larger the range of the model coverage of the measured data is, which means the model is very effective. For example, R 2 =95% means that the model can explain 90% of the data fluctuations.

[0078] Fitting coefficient R 2 The calculation formula is R 2 =1-SSres / SStot, where SSres represents the residual sum of squares and SStot represents the mean sum of squares.

[0079] The RMSE (Residual Value Sequence) reflects the average deviation between the predicted value and the actual value. The smaller the deviation, the higher the accuracy of the model.

[0080] The calculation formula of fitting error RMSE is: Where N represents the sample size of the number of cycles, Y1 represents the actual measured remaining capacity, and Y2 is the remaining capacity predicted by the model.

[0081] By calculating the fitting coefficient and fitting error for each mathematical model, a mathematical model is selected according to a preset fitting coefficient threshold and fitting error threshold, and the remaining battery life is evaluated based on the selected mathematical model.

[0082] Generally, according to the working parameters, the fitting coefficient threshold is set to a minimum of 95%, and the fitting error threshold is set to a maximum of 5%.

[0083] For example, for the exponential model, the charge model is Y = 1.0069e -0.002X ;

[0084] R2=0.6703=67.03%<95%, indicating that the exponential model of charging has a poor fit. In this case, there is no need to calculate the fitting error.

[0085] The discharge model is Y=1.0044e -0.003X ;

[0086] R 2 =0.981=98.1%>95%, indicating that the exponential model of discharge has a high degree of fitting. In this case, the fitting error is calculated. Specific data are shown in Table 3 below:

[0087] Table 3:

[0088]

[0089] For linear models:

[0090] The charging model is Y=-0.0015X+1.0069

[0091] R2 = 0.672 = 67.2% < 95%, indicating that the linear model fit of charging is poor, and there is no need to calculate the fitting error in this case;

[0092] The discharge model is Y=-0.0027X+1.0043

[0093] R2 =0.9819=98.19%>95%, indicating that the linear model of discharge has a high degree of fitting. See Table 4 below for specific data:

[0094] Table 4:

[0095]

[0096]

[0097] For the logarithmic model, the charging model is Y = -0.0051n(X) + 1.0056

[0098] R2 = 0.3792 = 37.92% < 95%, indicating that the logarithmic model of charging is poorly fitted, and there is no need to calculate the fitting error in this case;

[0099] Discharge model, Y = -0.011n(X) + 1.0049

[0100] R 2 =0.8259=82.59%<95%, indicating that the logarithmic model of charging is poorly fitted, and in this case, there is no need to calculate the fitting error.

[0101] For the polynomial model, the charging model is Y = -0.0004X 2 +0.0029X+0.998

[0102] R2=0.9646=96.46%>95%, indicating that the charging polynomial model has a high degree of fitting. See Table 5 below for specific data:

[0103] Table 5:

[0104]

[0105] The discharge model is Y=-0.0001X 2 -0.0012X+10013

[0106] R 2 =0.9975=99.75%>95%, indicating that the charging polynomial model has a high degree of fit. At this time, the fitting error is calculated. Specific data are shown in Table 6 below:

[0107] Table 6:

[0108]

[0109] In a second aspect, in order to solve the technical problem of the present invention, an embodiment of the present invention further provides a device for evaluating the remaining life of a battery based on measured battery capacity data, the device comprising:

[0110] The battery capacity determination module is used to perform charge and discharge tests on the battery based on the actual working environment and determine the minimum remaining capacity of the current battery based on the capacity when the battery begins to fail;

[0111] A first calculation module is used to establish a mathematical model of the remaining capacity of the battery and the number of charge and discharge cycles based on the test data, and substitute the minimum remaining capacity of the battery into the pre-established mathematical model to calculate the actual number of charge and discharge cycles that the battery can charge and discharge;

[0112] The second calculation module calculates the remaining life of the battery according to the actual number of cycles and the cycle period.

[0113] Furthermore, the device further includes a model building module, which specifically includes:

[0114] a data acquisition unit, according to predetermined battery operating parameters, placing the battery in an environment corresponding to the operating parameters, charging and discharging the battery using the charge and discharge current values ​​and times in the operating parameters, collecting the number of battery cycles and the remaining battery capacity, and setting the number of battery cycles as a factor X and the remaining battery capacity as an effect Y;

[0115] A mathematical model building unit is used to record the battery remaining capacity values ​​corresponding to different battery cycle times, and to build several mathematical models based on the collected battery charge and discharge cycle times and battery remaining capacity value data;

[0116] The model evaluation unit is used to evaluate the effectiveness of the established mathematical models.

[0117] Preferably, the mathematical model includes but is not limited to one or a combination of two or more of the following models:

[0118] Exponential model Y = a*e, linear model Y = a*X + b, logarithmic model Y = a*ln(X) + b, polynomial model: Y = a*X 2 +b*X+c.

[0119] Preferably, the model evaluation unit specifically includes:

[0120] A subunit for calculating fitting coefficients and fitting errors for each mathematical model;

[0121] The method is used to select a subunit of the mathematical model according to a preset fitting coefficient threshold and a fitting error threshold, wherein the fitting coefficient threshold and the fitting error threshold are set according to working parameters.

[0122] The embodiments of the device for evaluating the remaining life of a battery based on measured data of battery capacity correspond to the methods, and thus will not be described again.

[0123] The embodiments of the present application are based on the ICS43.10CCST47 group standard T / CSAE41-01 Electric Vehicle Power Battery Remaining Life Evaluation Guidelines, and disclose in detail how to establish a mathematical model according to the number of battery charge and discharge test cycles and the remaining capacity of the battery, and disclose in detail the judgment method and principle of the effectiveness of the established mathematical model, which can effectively adapt the battery remaining life evaluation method of the electric vehicle industry to the evaluation of the remaining life of the battery of the solar tracking system.

[0124] In a third aspect, the embodiments further provide a computing device, comprising a processor, a memory for storing a computer program, the computer program comprising program instructions, the processor or computer being configured to invoke the program instructions to execute the method as described above.

[0125] In a fourth aspect, the embodiments further provide a computer readable storage medium storing a computer program, the computer program comprising program instructions, the program instructions causing the processor or computer to execute the method as described above when executed by the processor or computer.

[0126] As shown in Figure 4 The computing device 1000 provided by the embodiments of the present application includes a processor or computer (not shown in the figure) 1001 and a memory 1002. The processor or computer 1001 and the memory 1002 can be connected to each other through a communication bus 1003. The communication bus 1003 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1003 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the memory 1002 is used to store a computer program, the computer program including program instructions, and the processor 1001 is configured to invoke the program instructions. The above program includes part or all steps for executing the method as described above.

[0127] The processor 1001 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program.

[0128] The memory 1002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor.

[0129] The computing device 1000 may further include a communication module 1004 and a display 1005. The communication module 1004 may be connected to the optical tracking device for communication. The communication module 1004 may be a wireless communication module (eg, a WiFi module, a Bluetooth module, etc.) or a wired communication module.

[0130] In addition, the computing device 1000 may also include common components such as a communication interface (eg, a USB interface, a microphone interface, etc.), an antenna, etc., which will not be described in detail here.

[0131] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0132] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0134] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0135] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0136] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0137] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0138] The above is a detailed introduction to the embodiments of the present application. Specific examples are used in the embodiments of the present invention to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0139] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the remaining life of a battery based on actual battery capacity data, characterized in that: The method comprises: Based on the actual working environment, the battery is charged and discharged, and the minimum remaining capacity of the current battery is determined based on the capacity when the battery begins to fail; Based on the test data, a mathematical model is established to determine the battery's remaining capacity and the number of charge and discharge cycles. The minimum remaining capacity of the battery is substituted into the pre-established mathematical model to calculate the actual number of charge and discharge cycles that the battery can achieve. The remaining battery life is calculated based on the actual number of cycles and the cycle period.

2. The method for estimating the remaining battery life based on measured battery capacity data according to claim 1, wherein: The mathematical model of the remaining capacity and the number of charge and discharge cycles of the battery is established based on the test data, specifically including: According to the predetermined battery operating parameters, the battery is placed in an environment corresponding to the operating parameters, and the battery is charged and discharged using the charge and discharge current values ​​and times in the operating parameters; Collect the battery cycle number and the battery remaining capacity value, and set the battery cycle number as factor X and the battery remaining capacity value as effect Y; Record the battery remaining capacity values ​​corresponding to different battery cycle times, and establish several mathematical models based on the collected battery charge and discharge cycle times and battery remaining capacity value data; The effectiveness of the established mathematical models is evaluated.

3. The method for estimating the remaining battery life based on measured battery capacity data according to claim 2, wherein: The mathematical model includes but is not limited to one or a combination of two or more of the following models: Exponential model Y = a*e, linear model Y = a*X + b, logarithmic model Y = a*ln(X) + b, polynomial model: Y = a*X 2 +b*X+c.

4. The method for estimating the remaining battery life based on measured battery capacity data according to claim 3, wherein: The effectiveness evaluation of the established mathematical models specifically includes: Calculate the fitting coefficient and fitting error for each mathematical model; The mathematical model is selected according to a preset fitting coefficient threshold and a fitting error threshold, wherein the fitting coefficient threshold and the fitting error threshold are set according to working parameters.

5. The method for estimating the remaining battery life based on measured battery capacity data according to any one of claims 1 to 4, wherein: The method further comprises: The operating parameters of the battery are determined according to the working mode of the battery in the actual application scenario. The parameters include but are not limited to the temperature curve, the maximum value of the battery charge / discharge current, and the charge / discharge time.

6. A device for evaluating the remaining life of a battery based on measured battery capacity data, characterized in that: The device comprises: The battery capacity determination module is used to perform charge and discharge tests on the battery based on the actual working environment and determine the minimum remaining capacity of the battery based on the capacity when the battery begins to fail; A first calculation module is used to establish a mathematical model of the remaining capacity of the battery and the number of charge and discharge cycles based on the test data, and substitute the minimum remaining capacity of the battery into the pre-established mathematical model to calculate the actual number of charge and discharge cycles that the battery can charge and discharge; The second calculation module calculates the remaining life of the battery according to the actual number of cycles and the cycle period.

7. The device for estimating the remaining battery life based on measured battery capacity data according to claim 6, wherein: The device further includes a model building module, which specifically includes: a data acquisition unit, according to predetermined battery operating parameters, placing the battery in an environment corresponding to the operating parameters, charging and discharging the battery using the charge and discharge current values ​​and times in the operating parameters, collecting the number of battery cycles and the remaining battery capacity, and setting the number of battery cycles as a factor X and the remaining battery capacity as an effect Y; A mathematical model building unit is used to record the battery remaining capacity values ​​corresponding to different battery cycle times, and to build several mathematical models based on the collected battery charge and discharge cycle times and battery remaining capacity value data; The model evaluation unit is used to evaluate the effectiveness of the established mathematical models.

8. The device for estimating the remaining battery life based on measured battery capacity data according to claim 7, wherein: The mathematical model includes but is not limited to one or a combination of two or more of the following models: Exponential model Y = a*e, linear model Y = a*X + b, logarithmic model Y = a*ln(X) + b, polynomial model: Y = a*X 2 +b*X+c.

9. The device for estimating the remaining battery life based on measured battery capacity data according to claim 7, wherein: The model evaluation unit specifically includes: A subunit for calculating fitting coefficients and fitting errors for each mathematical model; The method is used to select a subunit of the mathematical model according to a preset fitting coefficient threshold and a fitting error threshold, wherein the fitting coefficient threshold and the fitting error threshold are set according to working parameters.

10. A computing device, characterized in that The computing device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

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