WO2021208079A1 - 获取动力电池寿命数据的方法、装置、计算机设备及介质 - Google Patents
获取动力电池寿命数据的方法、装置、计算机设备及介质 Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
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Definitions
- This application relates to the technical field of power battery detection, and in particular to a method, device, computer equipment, and computer-readable storage medium for obtaining power battery life data.
- One of the objectives of the embodiments of the present application is to provide a method, device, computer equipment, and computer-readable storage medium for obtaining power battery life data, so as to solve the problem that the power battery life data cannot be obtained in the prior art.
- a method for obtaining power battery life data including:
- the planned number of charge and discharge times is input into the pre-built power battery life data regression model for processing, and the corresponding target power battery capacity data set and target confidence interval are output; wherein the power battery life data regression model is based on the sample power battery
- the accelerated aging data set is obtained by model construction; the accelerated aging data set includes the number of charging and discharging of the sample power battery during the accelerated aging process, and the capacity of the sample power battery corresponding to the number of charging and discharging; the target confidence interval Used to describe the uncertainty of the target power battery capacity data set.
- a device for obtaining data on the life of a power battery including:
- the first obtaining unit is used to obtain the planned number of charge and discharge times of the target power battery
- the first execution unit is configured to input the planned number of charge and discharge times into a pre-built power battery life data regression model for processing, and output a corresponding target power battery capacity data set and target confidence interval; wherein, the power battery life data returns
- the model is constructed based on the accelerated aging data set of the sample power battery; the accelerated aging data set includes the number of charge and discharge times of the sample power battery during the accelerated aging process, and the sample power battery corresponding to the number of charge and discharge times Capacity; the target confidence interval is used to describe the uncertainty of the target power battery capacity data set.
- a computer device including a memory, a processor, and a computer program that is stored in the memory and can be run on the computer device.
- the processor executes the computer program to implement the first solution. The steps of the method of obtaining power battery life data.
- a computer-readable storage medium stores a computer program that, when executed by a processor, implements each of the methods for obtaining power battery life data provided by the first solution. step.
- a computer program product is provided.
- the computer program product runs on a computer device, the computer device executes the steps of the method for obtaining power battery life data according to any one of the first aspects.
- the method, device, computer equipment, and computer-readable storage medium for obtaining power battery life data provided by the embodiments of the present application have the following beneficial effects:
- An embodiment of the application provides a method for obtaining power battery life data, by obtaining the planned charge and discharge times of the target power battery, and then input the planned charge and discharge times into a pre-built power battery life data regression model, due to the power battery life data
- the regression model is based on the accelerated aging data set of the sample power battery.
- the target power battery capacity data set can be used to describe the life data of the target power battery.
- the target confidence interval is used as the reference probability of the target power battery capacity data set.
- the uncertainty of the target power battery capacity data set is eliminated, so the power battery life data regression model can output the corresponding target power battery capacity data set and the target confidence interval according to the planned number of charge and discharge, thereby providing the target power battery to obtain its full life.
- the data solution solves the problem of not being able to obtain the full life data of the power battery.
- FIG. 1 is an implementation flowchart of a method for obtaining power battery life data provided by an embodiment of the present application
- FIG. 2 is an implementation flowchart of a method for obtaining power battery life data provided by another embodiment of the present application
- FIG. 3 is a diagram of the capacity attenuation trajectory of the target power battery in the embodiment of the present application.
- FIG. 5 is a specific implementation flow chart of step S32 in an embodiment of the present application.
- FIG. 6 is a block diagram of a structure of an apparatus for obtaining power battery life data provided by an embodiment of the present application.
- Fig. 7 is a structural block diagram of a computer device provided by another embodiment of the present application.
- the methods involved in the embodiments of the present application may be executed by a server or a terminal (hereinafter referred to as "computer equipment").
- the technical solution of the embodiment of the present application is suitable for measuring and calculating the life data of the target power battery through a computer device when the target power battery is detected or evaluated, so as to provide reference data for the detection of the power battery.
- the life data includes the number of times of charging and discharging each time of the target power battery, and the electric charging capacity corresponding to the number of times of charging and discharging each time.
- the embodiment of the present application takes a power battery, that is, a target power battery, as an example for description.
- FIG. 1 is an implementation flowchart of a method for obtaining power battery life data according to an embodiment of the present application.
- the method for obtaining power battery life data as shown in Fig. 1 includes the following steps:
- the target power battery is a target battery for obtaining power battery life data.
- the planned number of charge and discharge of the target power battery is an index used to calculate the life data of the target power battery.
- the optimal value of its various attribute data has been determined, and the maximum number of cycles that the target power battery can actually be charged and discharged has also been determined, so the value range of the planned number of charge and discharge times It should not be less than the maximum number of cycles that the target power battery can actually be charged and discharged, and should be equal to or greater than the maximum number of cycles that the target power battery can actually be charged and discharged in order to obtain all the life data of the target power battery.
- the planned number of charge and discharge times may be selected or formulated according to the needs of the power supply object of the target power battery.
- the planned number of charge and discharge times may be equal to or greater than that of the power supply object. The number of charge and discharge required.
- the mobile phone terminal Take the mobile phone terminal as the power supply object of the target power battery. If the mobile phone terminal requires 1,000 charge and discharge times for its power supply battery, then the planned number of charge and discharge times for the target power battery is the same one thousand times or more than one thousand times. .
- the planned number of charge and discharge times of the target power battery can be input into the computer equipment by the tester when the target power battery life data is acquired, or multiple scheduled charge and discharge pre-configured by the tester from the computer equipment Select from the number of times.
- the planned number of charge and discharge times is preset in the target database.
- the user selects any pre-configured power battery attribute file from the target database, and then triggers the computer device to obtain
- the preset instruction of the life data of the target power battery causes the computer device to obtain the planned number of charge and discharge times of the target power battery from the target database according to the preset instruction.
- the terminal device sends a request to the server to obtain the life data of the target power battery through the online application; if the server receives the life data acquisition request sent by the user through the client, it obtains the target power battery according to the life data acquisition request The planned number of charge and discharge.
- the terminal device is configured with a client for accessing server data, and the user sends a life data acquisition request to the server through the client.
- the computer device serving as the server obtains the target power from the local database according to the request. The planned number of charge and discharge of the battery.
- S12 Input the planned number of charge and discharge times into a pre-built power battery life data regression model for processing, and output a corresponding target power battery capacity data set and target confidence interval.
- the power battery life data regression model is obtained by constructing a model based on the accelerated aging data set of the sample power battery.
- the accelerated aging data set includes the number of charging and discharging of the sample power battery during the accelerated aging process, and the capacity of the sample power battery corresponding to the number of charging and discharging.
- the target confidence interval is used to describe the uncertainty of the target power battery capacity data set.
- the power battery life data regression model is based on the accelerated aging data set of the sample power battery.
- the target power battery capacity data set is the target power battery capacity corresponding to each charge and discharge of the target power battery in the planned number of charge and discharge times.
- the target confidence interval is used to describe the uncertainty of the target power battery capacity data set, that is, the data range or data interval obtained by quantifying the uncertainty of the target power battery capacity data set through the calculation of the power battery life data regression model.
- the input of the power battery life data regression model is the planned number of charge and discharge, and the planned number of charge and discharge corresponds to the target battery, and its output is the target power battery capacity data set and the target confidence interval.
- the power battery life data regression model is based on the accelerated aging data set of the sample power battery.
- the sample power battery and the target power battery are the same power battery, that is, they are the same in various dimensions or indicators such as model, specification, and battery capacity.
- the accelerated aging data set is obtained by the accelerated aging test of the sample power battery. Specifically, during the normal charging and discharging cycle test of the sample power battery, the test conditions that lead to the accelerated aging of the sample power battery are added. For example, at least one of overcharging, overdischarging, placing in a high temperature environment, placing in a low temperature environment, and a high current environment is performed on the sample power battery.
- the power battery life data regression model is constructed based on the accelerated aging data set of the sample power battery.
- the construction process of the power battery life data regression model is to compare the data change law during the accelerated aging process with the normal During the aging process, the data change laws are mapped to obtain the mapping relationship between the two data change laws. Therefore, in practical applications, you can first restore the accelerated aging data set to obtain the basic function used to describe the accelerated aging data set, and then linearize the basic function and eliminate the compensation for accelerated aging conditions, and finally quantify The uncertainty of the measurement result, that is, the confidence interval corresponding to the measurement result can be configured to construct the power battery life data regression model.
- step S12 specifically includes:
- C future is the target power battery capacity data set
- COV future is the variance matrix of the target power battery predicted battery capacity
- U future is the target confidence interval
- k is the number of charge and discharge times of the sample power battery
- K' is the planned number of charge and discharge of the target power battery
- GPR cov (k,k') is the covariance matrix of k and k'
- GPR cov (k,k) is the covariance of k and k Matrix
- GPR cov (k′,k′) is the covariance matrix of k′ and k′
- ⁇ n is the initial noise
- y is the sample power battery capacity corresponding to k
- GPR mean (k) is the migration of the input k Mean function
- GPR mean (k') is the transfer mean function with input k'.
- the construction process of the power battery life data regression model is actually to compare the data change law during the accelerated aging process to normal During the aging process, the data change law is mapped to the process of construction, so it is necessary to configure the initial noise part to limit the output result of the power battery life data regression model.
- the target confidence interval is used to characterize the error size of the output target power battery capacity data set.
- the power battery life data regression model outputs the target power battery capacity data set according to the planned number of charge and discharge times, since the data set cannot intuitively reflect the battery capacity degradation of the target power battery during the aging process, so To obtain more intuitive life data, data processing needs to be performed according to the power battery capacity data set and confidence interval.
- the method for obtaining power battery life data is to obtain the planned number of charge and discharge times of the target power battery, and then input the planned number of charge and discharge times into the pre-built power battery life data regression model.
- the power battery life data regression model is based on the accelerated aging data set of the sample power battery.
- the target power battery capacity data set can be used to describe the life data of the target power battery, and the target confidence interval is used as the target power battery capacity data set.
- the reference probability describes the uncertainty of the target power battery capacity data set.
- the power battery life data regression model can output the corresponding target power battery capacity data set and the target confidence interval according to the planned number of charge and discharge, thereby providing the target power battery
- the solution to obtain the life data of the battery solves the problem of not being able to obtain the life data of the power battery.
- FIG. 2 is an implementation flowchart of a method for obtaining power battery life data according to another embodiment of the present application.
- the method for obtaining power battery life data provided in this embodiment further includes S21 to S22 after step S12. The details are as follows:
- S21 Generate a capacity decay trajectory diagram of the target power battery according to the target power battery capacity data set and the target confidence interval;
- step S21 the capacity decay trajectory graph is used to characterize the life data of the target power battery, where the life data includes the number of charge and discharge times of the target power battery and the battery capacity value corresponding to each number of charge and discharge times.
- the capacity decay trajectory diagram of the target power battery is generated.
- the corresponding simulation tool can be called, and the power battery capacity data set can be drawn to characterize the target battery's planned charging.
- all power battery capacity data that is, a curve representing the correspondence between each charge and discharge in the planned number of charge and discharge times, and the power battery capacity data.
- a shadow space is drawn around the curve according to the confidence interval to represent the uncertainty of the curve.
- Fig. 3 shows the capacity decay trajectory diagram of the target power battery in this embodiment.
- the vertical axis represents the battery capacity of the target power battery in the target power battery capacity data set
- the horizontal axis represents the number of charge and discharge times of the target power battery in the target power battery capacity data set.
- the target power battery is not cycling Before charging and discharging, its battery capacity is infinitely close to 1. When the target power battery is charged and discharged for a certain number of cycles, its battery capacity will decay.
- the curve L is the trajectory of the target power battery capacity data set on the coordinate axis.
- the value range represented by the target confidence interval is accompanied by the entire curve L.
- the gray area S in Figure 3 shows the target confidence interval.
- the target inflection point is the target data point in the capacity attenuation trajectory
- the average capacity attenuation rate of n1 data points on the first adjacent trajectory of the target data point is the same as the second adjacent to the target data point.
- the difference between the average capacity attenuation rate of n2 data points on the trajectory is greater than the preset attenuation rate difference, and both n1 and n2 are integers greater than 2.
- the capacity decay trajectory graph is used to characterize the life data of the target power battery, where the life data includes the number of charge and discharge times of the target power battery and the battery capacity value corresponding to each number of charge and discharge times.
- the capacity decay trajectory graph includes multiple data points for composing the capacity decay trajectory, and each data point corresponds to a number of charge and discharge times and a target power battery capacity.
- the preset decay rate is used to describe the battery capacity decay degree when the target power battery is obviously aging. When determining the target data point, you can determine whether the data point is the target data by comparing the difference between the average capacity decay rate of the two sets of data points on the two adjacent trajectory curves of each data point is greater than the preset decay rate point.
- the curve L1 between data point P1 and data point P is the first adjacent track of data point P
- the curve L2 between P2 is the second adjacent trajectory of the data point P.
- the average capacity decay rate ⁇ W1 of n1 data points on the curve L1 and n2 data on the curve L2 are calculated
- the average capacity attenuation rate of a point ⁇ W2 when the difference between ⁇ W1 and ⁇ W2 is greater than the preset attenuation rate, then the data point P is identified as the target data point in the capacity attenuation trajectory chart, and the data point P is identified as the target data point in the capacity attenuation trajectory chart. Mark the target data point as the target inflection point. In practical applications, mark the target inflection point in the capacity attenuation trajectory diagram.
- the target inflection point can be bolded in the capacity decay trajectory diagram, or the color of the target inflection point in the capacity decay trajectory diagram can be changed, or the target inflection point can be represented by the target power in the capacity decay trajectory diagram.
- the battery capacity and the number of charge and discharge times are displayed around the target inflection point.
- the target inflection point is marked in the capacity attenuation trajectory diagram, which can intuitively reflect the charge and discharge times corresponding to the battery capacity stability interval of the target power battery, and whether it is consistent with the demand of the electric equipment Matching, can provide data reference for the selection of electrical equipment to select power supply battery.
- FIG. 4 is an implementation flowchart of a method for obtaining power battery life data according to still another embodiment of the present application.
- the method for obtaining power battery life data provided in this embodiment further includes S31 to S32 before step S11. The details are as follows:
- a normal aging data set and an accelerated aging data set are stored in the preset database, and the normal aging data set is obtained by performing a normal aging test on a sample power battery.
- the accelerated aging data set may be obtained by performing an accelerated aging test on the sample power battery.
- the accelerated aging environment may include at least one of overcharging, overdischarging, placing in a high temperature environment, placing in a low temperature environment, and a high current environment on the sample power battery.
- the accelerated aging environment component can be any one of the above-mentioned environments, or a combination of any two, or a combination of any multiple, and the accelerated aging data set can be superimposed on the part of the accelerated aging environment measured above The data can also be superimposed with all the above-mentioned data measured under accelerated aging environment.
- the power battery life data regression model is constructed based on the accelerated aging data set. Specifically, it can be the parameter identification operation of the accelerated aging data set and the preset model function, that is, the calculation shadow in the model function and the accelerated aging data set After the calculation results of the model function and the data content in the accelerated aging data set can be matched to the greatest extent, the data change law of the normal aging data set is incorporated into the model.
- step S32 specifically includes:
- S321 Generate an accelerated aging trajectory diagram according to the accelerated aging data set; wherein the accelerated aging trajectory diagram is used to describe the correspondence between the number of charge and discharge times and the capacity of the sample power battery during the accelerated aging test of the sample power battery relation.
- S323 Calculate the root mean square error corresponding to the first model based on the accelerated aging trajectory diagram and the target trajectory diagram; wherein the root mean square error is used to characterize the target trajectory diagram and the accelerated aging trajectory diagram
- the target trajectory diagram is the fitting result of the accelerated aging trajectory diagram by the first model, and the target trajectory diagram is used to describe the correspondence between the number of charge and discharge times and the estimated capacity of the power battery relation.
- S324 According to a preset conversion strategy, calculate the confidence interval of the first model with respect to the accelerated aging data set according to the root mean square error.
- S325 Construct the power battery life data regression model based on the normal aging data set, the first model, and the confidence interval.
- the preset accelerated aging model list includes a plurality of accelerated aging models, each accelerated aging model corresponds to a specific equation, and the equation corresponding to each accelerated aging model contains factors to be identified.
- parameter identification is a method that combines theoretical models and test data for prediction. Parameter identification determines the parameter values of a set of models according to the test data and the pre-established model, so that the numerical results calculated by the model can best fit the test data, so as to predict the unknown process and provide certain theoretical guidance.
- the preset accelerated aging model list may be specifically as shown in Table 1.
- the preset accelerated aging model list includes multiple types of accelerated aging models, and each type of accelerated aging model corresponds to a specific equation In each specific equation, there are factors to be identified for parameter identification, such as a n , b n and c 1.
- the specific method can be The heuristic algorithm is used to identify the parameters to be identified in the accelerated aging model.
- a heuristic algorithm is used to solve the correspondence between equations and solutions, that is, to solve the relationship between the factors to be identified in the specific equations corresponding to the accelerated aging model and the accelerated aging trajectory graph.
- the problem of correspondence fitting is not an improvement of the heuristic algorithm, so it will not be repeated here.
- Table 1 List of preset accelerated aging models (example)
- a plurality of rough accelerated aging models are pre-built in the preset accelerated aging model list, and these accelerated aging models are used to convert the data corresponding to the accelerated aging trajectory map.
- the error between the numerical result obtained by the conversion and the data corresponding to the accelerated aging trajectory graph is large, it is considered that the accelerated aging model does not match the accelerated aging trajectory graph or the gap is large, and the model is modified or the model is selected again.
- the model is considered to have high credibility, and the accelerated aging model is identified as the first model.
- the preset fitting conditions may also include: the degree of conformity between the data corresponding to the accelerated aging model and the accelerated aging trajectory diagram, or the minimum mean square error between the accelerated aging model and the accelerated aging trajectory diagram.
- the corresponding relationship between the number of charge and discharge times and the estimated capacity of the power battery can be intuitively described, and then based on the accelerated aging trajectory map and the target trajectory map, the calculation
- the root mean square error corresponding to the first model can be used to characterize the degree of difference between the target trajectory graph and the accelerated aging trajectory graph.
- step S324 specifically includes:
- the root mean square error corresponding to the first model is measured by the following formula
- MSE accelerate is the root mean square error, and MSE accelerate >0;
- m is the number of data point pairs;
- Is the estimated capacity of the power battery corresponding to the j-th second data point in the m pairs of data points;
- C j is the power battery capacity corresponding to the j-th first data point in the m pairs of data points.
- the accelerated aging trajectory diagram and the target trajectory diagram are two different trajectory diagrams, but there is a certain similarity between the two, by extracting data points with the same charge and discharge times from the two trajectory diagrams. , Compose m data point pairs, and calculate the root mean square error based on the m data point pairs, and the obtained root mean square error can accurately quantify the difference between the accelerated aging trajectory graph and the target trajectory graph.
- step S325 specifically includes:
- the confidence interval is measured by the following formula
- CI accelerate is the confidence interval
- X is the preset conversion constant, and 0 ⁇ X ⁇ 1
- MSE accelerate is the root mean square error.
- the root mean square error can accurately quantify the degree of difference between the accelerated aging trajectory graph and the target trajectory graph
- the root mean square error is used to measure or configure the confidence interval of the model output, so that the confidence interval is considered
- the error between the model and the actual data has improved the scientific degree of the confidence interval of the measurement.
- the power battery life data regression model includes: a mean value part for configuring a target transfer function, a Gaussian kernel part for configuring a covariance function, and an initial noise for describing restriction conditions; step S325 is specific include:
- the input of the first model and the output of the first model are respectively linearly transformed to obtain the migration function, and the migration function is fitted to the trajectory diagram corresponding to the normal aging data set, and the target migration function is obtained according to the fitting result, and the target
- the transfer function is configured to the mean value part; at least one target Gaussian kernel is selected as the covariance function from the preset Gaussian kernel list, and the covariance function is configured to the Gaussian kernel part; the confidence interval is identified as the initial of the power battery life data regression model noise.
- the normal aging data set is a data set obtained by performing a normal aging test on a sample power battery.
- the obtained target migration function can have the characteristics of describing the data change law of the normal aging data set.
- the preset Gaussian kernel list can be specifically as shown in Table 2, including multiple types of Gaussian kernel functions. It should be noted that when at least one target Gaussian kernel is selected as the covariance function from the preset Gaussian kernel list, and Similar to selecting the first model from the list of accelerated aging models, it is also necessary to perform parameter identification on the factors to be identified in the equation corresponding to the Gaussian function.
- ⁇ SE , l SE , ⁇ M32 , l M32 , ⁇ RQ , l RQ and ⁇ are all hyperparameters that require parameter identification in the Gaussian kernel function equation, x And x'are the input of accelerated aging data set and normal aging data set respectively.
- Table 2 List of preset Gaussian kernels (example)
- the power battery selects at least one target Gaussian kernel from the preset Gaussian kernel list as the covariance function
- the charge and discharge times in the normal aging data set are used as the input of the power battery life data regression model.
- the battery capacity data corresponding to the number of charge and discharge times in the data set is used as the output to optimize the identification parameters, and the final charge and discharge times in the normal aging data set are obtained.
- the identification method uses the Newton method to optimize the maximum likelihood probability.
- the linear transformation of the input of the first model and the output of the first model to obtain a transfer function respectively includes:
- the input of the first model and the output of the first model are respectively linearly transformed by the following formula to obtain a transfer function
- f(x) is the transfer function
- x is the input value of the transfer function
- M accelerate is the first model
- a 1 is the first output linear transformation factor
- a 2 is the second output linear transformation factor
- B 1 is the first input linear transformation factor
- b 2 is the second input linear transformation factor
- the values of a 1 , a 2 , b 1 and b 2 are obtained by fitting f(x) to the sample power battery
- the normal aging data set is determined on the corresponding trajectory graph.
- the first model M accelerate is a known quantity in the migration function.
- the trajectory graph corresponding to the normal aging data set of the sample power battery it can identify a 1 , a 2 , and the migration function.
- the values of b 1 and b 2 that is, the values of a 1 , a 2 , b 1 and b 2 are determined to obtain the objective transfer function.
- the objective transfer function framework is embedded in the Gaussian process model as the mean function, and the sample power is used
- the battery normal aging data is concentrated on parameter optimization and identification, so as to develop a regression model of the migration Gaussian process under the normal aging of the sample power battery, that is, the power battery life data regression model, in order to achieve effective power battery capacity inflection point information prediction and efficient prediction Uncertainty quantitative management.
- the method for obtaining power battery life data is to obtain the planned number of charge and discharge times of the target power battery, and then input the planned number of charge and discharge times into the pre-built power battery life data regression model.
- the power battery life data regression model is based on the accelerated aging data set of the sample power battery.
- the target power battery capacity data set can be used to describe the life data of the target power battery, and the target confidence interval is used as the target power battery capacity data set.
- the reference probability describes the uncertainty of the target power battery capacity data set.
- the power battery life data regression model can output the corresponding target power battery capacity data set and the target confidence interval according to the planned number of charge and discharge, thereby providing the target power battery
- the solution to obtain the life data of the battery solves the problem of not being able to obtain the life data of the power battery.
- the accelerated aging model is fitted with the accelerated aging trajectory graph, and then the first model is selected, so that the first model can effectively learn the full life data of the sample power battery, as well as the data change law and inflection point information.
- the subsequent establishment of the power battery life data regression model has the full life cycle aging information provided by the accelerated aging model and the efficient uncertainty quantification ability of the Gaussian process regression model, so as to achieve the effectiveness of the future battery capacity and the target inflection point of the target power battery. Forecast, and give an accurate uncertainty quantification for the forecast result.
- the power battery life data regression model provided in this embodiment does not need to solve a large number of partial differential equations when acquiring the life data of the target power battery, and has low complexity and small calculation amount, which is suitable for online applications.
- FIG. 6 is a structural block diagram of an apparatus for obtaining life data of a power battery provided by an embodiment of the present application.
- the units included in the device for obtaining power battery life data are used to execute the steps in the embodiments corresponding to FIGS. 1 to 4.
- the device 400 for acquiring power battery life data includes: a first acquiring unit 41 and a first executing unit 42. in:
- the first obtaining unit 41 is configured to obtain the planned number of charge and discharge times of the target power battery.
- the first execution unit 42 is configured to input the planned number of charge and discharge times into a pre-built power battery life data regression model for processing, and output a corresponding target power battery capacity data set and target confidence interval; wherein, the power battery life data
- the regression model is constructed based on the accelerated aging data set of the sample power battery; the accelerated aging data set includes the number of charge and discharge times of the sample power battery during the accelerated aging process, and the sample power corresponding to the number of charge and discharge times Battery capacity; the target confidence interval is used to describe the uncertainty of the target power battery capacity data set.
- the first execution unit 42 is specifically configured to input the planned number of charge and discharge times into the power battery life data regression model
- C future is the target power battery capacity data set
- COV future is the variance matrix of the target power battery predicted battery capacity
- U future is the target confidence interval
- k is the number of charge and discharge times of the sample power battery
- K' is the planned number of charge and discharge of the target power battery
- GPR cov (k,k') is the covariance matrix of k and k'
- GPR cov (k,k) is the covariance of k and k Matrix
- GPR cov (k′,k′) is the covariance matrix of k′ and k′
- ⁇ n is the initial noise
- y is the sample power battery capacity corresponding to k
- GPR mean (k) is the migration of the input k Mean function
- GPR mean (k') is the transfer mean function with input k'.
- the device 400 for obtaining power battery life data further includes: a second execution unit 43 and a marking unit 44. specifically:
- the second execution unit 43 is configured to generate a capacity decay trajectory diagram of the target power battery according to the target power battery capacity data set and the target confidence interval.
- the marking unit 44 is configured to mark the target inflection point in the capacity attenuation trajectory diagram; wherein, the target inflection point is a target data point in the capacity attenuation trajectory diagram, and the first neighbor of the target data point
- the difference between the average capacity decay rate of n1 data points on the trajectory and the average capacity decay rate of n2 data points on the second adjacent trajectory of the target data point is greater than the preset decay rate difference, and n1 and n2 are both Is an integer greater than 2.
- the device 400 for obtaining power battery life data further includes: a second obtaining unit 45 and a model building unit 46. specifically:
- the second acquiring unit 45 is configured to acquire a normal aging data set and the accelerated aging data set from a preset database.
- the model construction unit 46 is configured to construct a power battery life data regression model based on the normal aging data set and the accelerated aging data set.
- the model construction unit 46 is specifically configured to generate an accelerated aging trajectory diagram according to the accelerated aging data set; wherein, the accelerated aging trajectory diagram is used to describe the process of the accelerated aging test of the sample power battery , The corresponding relationship between the number of charge and discharge times and the capacity of the sample power battery; each accelerated aging model in the preset accelerated aging model list is used to respectively fit the accelerated aging trajectory diagram, and according to the fitting result, from the The accelerated aging model that meets the preset fitting conditions is selected from the accelerated aging model list as the first model; based on the accelerated aging trajectory diagram and the target trajectory diagram, the root mean square error corresponding to the first model is measured; wherein, the The root mean square error is used to characterize the degree of difference between the target trajectory diagram and the accelerated aging trajectory diagram; the target trajectory diagram is the fitting result of the accelerated aging trajectory diagram by the first model, and the The target trajectory diagram is used to describe the corresponding relationship between the number of charge and
- the model construction unit 46 is specifically further configured to extract m first data points from the accelerated aging trajectory graph, and extract m second data points from the target trajectory graph to obtain m Data point pairs; where m is an integer greater than 0; the number of charge and discharge times corresponding to the first data point in each pair of data points is the same as the number of charge and discharge times corresponding to the second data point;
- the root mean square error corresponding to the first model is measured by the following formula
- MSE accelerate is the root mean square error, and MSE accelerate >0;
- m is the number of data point pairs;
- Is the estimated capacity of the power battery corresponding to the j-th second data point in the m pairs of data points;
- C j is the power battery capacity corresponding to the j-th first data point in the m pairs of data points.
- the model construction unit 46 is specifically further configured to calculate the confidence interval by the following formula according to the root mean square error
- CI accelerate is the confidence interval
- X is the preset conversion constant, and 0 ⁇ X ⁇ 1
- MSE accelerate is the root mean square error.
- the power battery life data regression model includes: a mean part used to configure the target transfer function, a Gaussian kernel part used to configure the covariance function, and initial noise used to describe the limiting conditions; a model construction unit 46 is also specifically used to perform linear transformations on the input of the first model and the output of the first model to obtain a migration function, and to simulate the trajectory diagram corresponding to the normal aging data set and the migration function.
- the target transfer function is obtained according to the fitting result, and the target transfer function is configured to the mean value part; at least one target Gaussian kernel is selected as the covariance function from the preset Gaussian kernel list, and the covariance The function is configured to the Gaussian core part; the confidence interval is identified as the initial noise of the power battery life data regression model.
- the model construction unit 46 is specifically further configured to perform linear transformations on the input of the first model and the output of the first model respectively by using the following formula to obtain a transfer function;
- f(x) is the transfer function
- x is the input value of the transfer function
- M accelerate is the first model
- a 1 is the first output linear transformation factor
- a 2 is the second output linear transformation factor
- B 1 is the first input linear transformation factor
- b 2 is the second input linear transformation factor
- the values of a 1 , a 2 , b 1 and b 2 are obtained by fitting f(x) to the sample power battery
- the normal aging data set is determined on the corresponding trajectory graph.
- the solution provided by this embodiment obtains the planned number of charge and discharge of the target power battery, and then inputs the planned number of charge and discharge into the pre-built power battery life data regression model, because the power battery life data regression model is based on The accelerated aging data set of the sample power battery is model-built, and the target power battery capacity data set can be used to describe the life data of the target power battery.
- the target confidence interval is used as the reference probability of the target power battery capacity data set to describe the target power battery.
- the capacity data set is uncertain, so the power battery life data regression model can output the corresponding target power battery capacity data set and the target confidence interval according to the planned number of charge and discharge, and then provide a solution for the target power battery to obtain its life data. The problem of not being able to obtain power battery life data.
- the accelerated aging model is fitted with the accelerated aging trajectory graph, and then the first model is selected, so that the first model can effectively learn the full life data of the sample power battery, as well as the data change law and inflection point information.
- the subsequent establishment of the power battery life data regression model has the full life cycle aging information provided by the accelerated aging model and the efficient uncertainty quantification ability of the Gaussian process regression model, so as to achieve the effectiveness of the future battery capacity and the target inflection point of the target power battery. Forecast, and give an accurate uncertainty quantification for the forecast result.
- the power battery life data regression model provided in this embodiment does not need to solve a large number of partial differential equations when acquiring the life data of the target power battery, and has low complexity and small calculation amount, which is suitable for online applications.
- Fig. 7 is a structural block diagram of a computer device provided by another embodiment of the present application.
- the computer device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and running on the processor 50, such as acquiring power battery life data Method of the program.
- the processor 50 executes the computer program 52, the steps in the above embodiments of the method for obtaining power battery life data are implemented, such as S11 to S12 shown in FIG. 1.
- the processor 50 executes the computer program 52
- the functions of the units in the embodiment corresponding to FIG. 6 are implemented, for example, the functions of the units 41 to 46 shown in FIG. 6.
- FIG. 6 The related description in the example will not be repeated here.
- the computer program 52 may be divided into one or more units, and the one or more units are stored in the memory 51 and executed by the processor 50 to complete the application.
- the one or more units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the computer device 5.
- the computer program 52 may be divided into a first acquisition unit and a first execution unit, and the specific functions of each unit are as described above.
- the computer device may include, but is not limited to, a processor 50 and a memory 51.
- FIG. 7 is only an example of the computer device 6 and does not constitute a limitation on the computer device 5. It may include more or less components than those shown in the figure, or a combination of certain components, or different components.
- the computer device may also include input and output devices, network access devices, buses, and so on.
- the so-called processor 50 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
- the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
- the memory 51 may be an internal storage unit of the computer device 5, such as a hard disk or a memory of the computer device 5.
- the memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk equipped on the computer device 5, a smart memory card (Smart Media Card, SMC), or a Secure Digital (SD). Card, Flash Card, etc.
- the memory 51 may also include both an internal storage unit of the computer device 5 and an external storage device.
- the memory 51 is used to store the computer program and other programs and data required by the computer device.
- the memory 51 can also be used to temporarily store data that has been output or will be output.
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Abstract
Description
Claims (20)
- 一种获取动力电池寿命数据的方法,其特征在于,包括:获取目标动力电池的计划充放电次数;将所述计划充放电次数输入预先构建的动力电池寿命数据回归模型进行处理,输出相应的目标动力电池容量数据集与目标置信区间;其中,所述动力电池寿命数据回归模型是基于样本动力电池的加速老化数据集进行模型构建所得;所述加速老化数据集包括所述样本动力电池在加速老化过程中的充放电次数,以及与所述充放电次数对应的样本动力电池容量;所述目标置信区间用于描述所述目标动力电池容量数据集的不确定性。
- 根据权利要求1所述的获取动力电池寿命数据的方法,其特征在于,所述将所述计划充放电次数输入预先构建的动力电池寿命数据回归模型进行处理,输出相应的目标动力电池容量数据集与目标置信区间,包括:将所述计划充放电次数输入所述动力电池寿命数据回归模型;通过所述动力电池寿命数据回归模型中的下列方程组输出所述目标动力电池容量数据集与目标置信区间;其中,C future为所述目标动力电池容量数据集;COV future为所述目标动力电池预测电池容量的方差矩阵;U future为所述目标置信区间;k为所述样本动力电池的充放电次数中的最大次数;k′为所述目标动力电池的计划充放电次数;GPR cov(k,k′)为k与k′的协方差矩阵;GPR cov(k,k)为k与k的协方差矩阵;GPR cov(k′,k′)为k′与k′的协方差矩阵;σ n为初始噪音;y为与k对应的样本动力电池容量;GPR mean(k)是输入为k的迁移均值函数;GPR mean(k′)是输入为k′的迁移均值函数。
- 根据权利要求1所述的获取动力电池寿命数据的方法,其特征在于,所述将所述计划充放电次数输入预先构建的动力电池寿命数据回归模型进行处理,输出相应的目标动力电池容量数据集的步骤之后,还包括:根据所述目标动力电池容量数据集与所述目标置信区间,生成所述目标动力电池的容量衰减轨迹图;在所述容量衰减轨迹图中标记出所述目标拐点;其中,所述目标拐点为所述容量衰减轨迹图中的目标数据点,所述目标数据点的第一相邻轨迹上的n1个数据点的平均容量衰减速率,与所述目标数据点的第二相邻轨迹上的n2个数据点的平均容量衰减速率之差,大于预设衰减速率差,n1与n2均为大于2的整数。
- 根据权利要求1所述的获取动力电池寿命数据的方法,其特征在于,所述将所述计划充放电次数输入预先构建的动力电池寿命数据回归模型进行处理,输出相应的目标动力电池容量数据集与目标置信区间的步骤之前,还包括:从预设数据库中获取正常老化数据集与所述加速老化数据集;基于所述正常老化数据集与所述加速老化数据集构建动力电池寿命数据回归模型。
- 根据权利要求4所述的获取动力电池寿命数据的方法,其特征在于,所述基于所述正常老化数据集与所述加速老化数据集构建动力电池寿命数据回归模型,包括:根据所述加速老化数据集生成加速老化轨迹图;其中,所述加速老化轨迹图用于描述所述样本动力电池在加速老化测试过程中,充放电次数与样本动力电池容量之间的对应关系;利用预设的加速老化模型列表中的每个加速老化模型,分别拟合所述加速老化轨迹图,并根据拟合结果,从所述加速老化模型列表中选择符合预设拟合条件的加速老化模型作为第一模型;基于所述加速老化轨迹图与目标轨迹图,测算所述第一模型对应的均方根误差;其中,所述均方根误差用于表征所述目标轨迹图与所述加速老化轨迹图之间的差别程度;所述目标轨迹图为所述第一模型对所述加速老化轨迹图的拟合结果,所述目标轨迹图用于描述充放电次数与动力电池预估容量之间的对应关系;按照预设的转换策略,根据所述均方根误差测算所述第一模型关于所述加速老化数据集的置信区间;基于所述正常老化数据集、所述第一模型以及所述置信区间,构建所述动力电池寿命数据回归模型。
- 根据权利要求5所述的获取动力电池寿命数据的方法,其特征在于,所述基于所述加速老化轨迹图与目标轨迹图,测算所述第一模型对应的均方根误差,包括:从所述加速老化轨迹图上提取m个第一数据点,从所述目标轨迹图上提取m个第二数据点,得到m个数据点对;其中,m为大于0的整数;每个所述数据点对中的第一数据点对应的充放电次数,与第二数据点对应的充放电次数相同;基于每个所述数据点对,通过以下公式测算所述第一模型对应的均方根误差;
- 根据权利要求5所述的获取动力电池寿命数据的方法,其特征在于,所述动力电池寿命数据回归模型包括:用于配置目标迁移函数的均值部、用于配置协方差函数的高斯核部以及用于描述限制条件的初始噪音;所述基于所述正常老化数据集、所述第一模型以及所述置信区间,构建所述动力电池寿命数据回归模型,包括:对所述第一模型的输入与所述第一模型的输出,分别进行线性变换得到迁移函数,并将所述迁移函数与所述正常老化数据集对应的轨迹图进行拟合,根据拟合结果得到目标迁移函数,并将所述目标迁移函数配置到所述均值部;从预设的高斯核列表中选取至少一个目标高斯核作为协方差函数,并将所述协方差函数配置到所述高斯核部;将所述置信区间识别为所述动力电池寿命数据回归模型的初始噪音。
- 根据权利要求8所述的获取动力电池寿命数据的方法,其特征在于,所述对所述第一模型的输入与所述第一模型的输出,分别进行线性变换得到迁移函数,包括:通过以下公式,对所述第一模型的输入与所述第一模型的输出分别进行线性变换,得到迁移函数;f(x)=a 1*M accelerate*(b 1*x+b 2)+a 2;其中,f(x)为所述迁移函数;x为所述迁移函数的输入值;M accelerate为所述第一模型;a 1为第一输出线性变换因子;a 2为第二输出线性变换因子;b 1为第一输入线性变换因子;b 2为第二输入线性变换因子;a 1、a 2、b 1以及b 2的取值,通过将f(x)拟合至所述样本动力电池的正常老化数据集对应的轨迹图上确定。
- 一种获取动力电池寿命数据的装置,其特征在于,包括:第一获取单元,用于获取目标动力电池的计划充放电次数;第一执行单元,用于将所述计划充放电次数输入预先构建的动力电池寿命数据回归模型进行处理,输出相应的目标动力电池容量数据集与目标置信区间;其中,所述动力电池寿命数据回归模型是基于样本动力电池的加速老化数据集进行模型构建所得;所述加速老化数据集包括所述样本动力电池在加速老化过程中的充放电次数,以及与所述充放电次数对应的样本动力电池容量;所述目标置信区间用于描述所述目标动力电池容量数据集的不确定性。
- 根据权利要求10所述的获取动力电池寿命数据的装置,其特征在于,所述第一执行单元具体用于,将所述计划充放电次数输入所述动力电池寿命数据回归模型;通过所述动力电池寿命数据回归模型中的下列方程组输出所述目标动力电池容量数据集与目标置信区间;其中,C future为所述目标动力电池容量数据集;COV future为所述目标动力电池预测电池容量的方差矩阵;U future为所述目标置信区间;k为所述样本动力电池的充放电次数中的最大次数;k′为所述目标动力电池的计划充放电次数;GPR cov(k,k′)为k与k′的协方差矩阵;GPR cov(k,k)为k与k的协方差矩阵;GPR cov(k′,k′)为k′与k′的协方差矩阵;σ n为初始噪音;y为与k对应的样本动力电池容量;GPR mean(k)是输入为k的迁移均值函数;GPR mean(k′)是输入为k′的迁移均值函数。
- 根据权利要求10所述的获取动力电池寿命数据的装置,其特征在于,还包括:第二执行单元,用于根据所述目标动力电池容量数据集与所述目标置信区间,生成所述目标动力电池的容量衰减轨迹图;标记单元,用于在所述容量衰减轨迹图中标记出所述目标拐点;其中,所述目标拐点为所述容量衰减轨迹图中的目标数据点,所述目标数据点的第一相邻轨迹上的n1个数据点的平均容量衰减速率,与所述目标数据点的第二相邻轨迹上的n2个数据点的平均容量衰减速率之差,大于预设衰减速率差,n1与n2均为大于2的整数。
- 根据权利要求10所述的获取动力电池寿命数据的装置,其特征在于,还包括:第二获取单元,用于从预设数据库中获取正常老化数据集与所述加速老化数据集;模型构建单元,用于基于所述正常老化数据集与所述加速老化数据集构建动力电池寿命数据回归模型。
- 根据权利要求13所述的获取动力电池寿命数据的装置,其特征在于,所述模型构建单元具体用于,根据所述加速老化数据集生成加速老化轨迹图;其中,所述加速老化轨迹图用于描述所述样本动力电池在加速老化测试过程中,充放电次数与样本动力电池容量之间的对应关系;利用预设的加速老化模型列表中的每个加速老化模型,分别拟合所述加速老化轨迹图,并根据拟合 结果,从所述加速老化模型列表中选择符合预设拟合条件的加速老化模型作为第一模型;基于所述加速老化轨迹图与目标轨迹图,测算所述第一模型对应的均方根误差;其中,所述均方根误差用于表征所述目标轨迹图与所述加速老化轨迹图之间的差别程度;所述目标轨迹图为所述第一模型对所述加速老化轨迹图的拟合结果,所述目标轨迹图用于描述充放电次数与动力电池预估容量之间的对应关系;按照预设的转换策略,根据所述均方根误差测算所述第一模型关于所述加速老化数据集的置信区间;基于所述正常老化数据集、所述第一模型以及所述置信区间,构建所述动力电池寿命数据回归模型。
- 根据权利要求14所述的获取动力电池寿命数据的装置,其特征在于,所述模型构建单元具体用于,从所述加速老化轨迹图上提取m个第一数据点,从所述目标轨迹图上提取m个第二数据点,得到m个数据点对;其中,m为大于0的整数;每个所述数据点对中的第一数据点对应的充放电次数,与第二数据点对应的充放电次数相同;基于每个所述数据点对,通过以下公式测算所述第一模型对应的均方根误差;
- 根据权利要求14所述的获取动力电池寿命数据的装置,其特征在于,所述动力电池寿命数据回归模型包括:用于配置目标迁移函数的均值部、用于配置协方差函数的高斯核部以及用于描述限制条件的初始噪音;所述模型构建单元具体用于,对所述第一模型的输入与所述第一模型的输出,分别进行线性变换得到迁移函数,并将所述迁移函数与所述正常老化数据集对应的轨迹图进行拟合,根据拟合结果得到目标迁移函数,并将所述目标迁移函数配置到所述均值部;从预设的高斯核列表中选取至少一个目标高斯核作为协方差函数,并将所述协方差函数配置到所述高斯核部;将所述置信区间识别为所述动力电池寿命数据回归模型的初始噪音。
- 根据权利要求17所述的获取动力电池寿命数据的装置,其特征在于,所述模型构建单元具体用于,通过以下公式,对所述第一模型的输入与所述第一模型的输出分别进行线性变换,得到迁移函数;f(x)=a 1*M accelerate*(b 1*x+b 2)+a 2;其中,f(x)为所述迁移函数;x为所述迁移函数的输入值;M accelerate为所述第一模型;a 1为第一输出线性变换因子;a 2为第二输出线性变换因子;b 1为第一输入线性变换因子;b 2为第二输入线性变换因子;a 1、a 2、b 1以及b 2的取值,通过将f(x)拟合至所述样本动力电池的正常老化数据集对应的轨迹图 上确定。
- 一种计算机设备,其特征在于,所述计算机设备包括存储器、处理器以及存储在所述存储器中并可在所述计算机设备上运行的计算机程序,所述处理器执行所述计算机程序时实现如权利要求1至9任一项所述获取动力电池寿命数据的方法的步骤。
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,其特征在于,所述计算机程序被处理器执行时实现如权利要求1至9任一项所述获取动力电池寿命数据的方法的步骤。
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