CN116819364A - Battery health determination method, application method, electronic device and storage medium - Google Patents
Battery health determination method, application method, electronic device and storage medium Download PDFInfo
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- 238000001514 detection method Methods 0.000 claims description 31
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- 230000002159 abnormal effect Effects 0.000 description 9
- 238000012360 testing method Methods 0.000 description 5
- 238000000691 measurement method Methods 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 3
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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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
- 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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The application provides a method for determining battery health, an application method, electronic equipment and a storage medium, wherein the method for determining battery health comprises the following steps: acquiring a charging curve segment of the battery cell to be evaluated, wherein the charging curve segment comprises voltages and electric quantity of the battery cell to be evaluated at different time points; inputting the charging curve segment into a preset charging curve prediction model, and obtaining a predicted complete charging curve output by the charging curve prediction model; determining the maximum electric quantity value of the electric core to be evaluated in the prediction complete charging curve; and taking the percentage of the maximum electric quantity value relative to the preset rated electric quantity of the electric core to be evaluated as the battery health of the electric core to be evaluated. According to the application, the complete charging curve is predicted based on the charging curve segment of the battery cell to be evaluated through the charging curve prediction model, and then the battery health degree of the battery cell to be evaluated is determined based on the complete charging curve, so that compared with the current battery health degree estimation scheme, the determined battery health degree is more accurate.
Description
Technical Field
The application relates to the technical field of battery health, in particular to a method for determining battery health, an application method, electronic equipment and a storage medium.
Background
In the related art, the estimation method of the state of health (SOH) of the battery generally includes a direct measurement method, an indirect analysis method, a joint estimation method, and the like. The direct measurement method mainly comprises an electric quantity measurement method, an ohmic internal resistance measurement method, an impedance measurement method and a cycle period counting method, the indirect analysis method mainly comprises a capacity increment method and a differential voltage method, and the combined estimation method mainly uses an electrochemical model or an equivalent circuit model to identify model parameters so as to finish the calibration of the battery health.
However, the above estimation schemes have limited applicability due to the large number of factors affecting the battery health, and the difficulty in accurately estimating the battery health is large, so the current battery health estimation scheme has a problem of low accuracy.
Disclosure of Invention
The embodiment of the application provides a method for determining battery health, an application method, electronic equipment and a storage medium, which can improve the technical problem of low estimation accuracy of battery health.
In a first aspect, an embodiment of the present application provides a method for determining a battery health, including:
acquiring a charging curve segment of a battery cell to be evaluated, wherein the charging curve segment comprises voltages and electric quantity of the battery cell to be evaluated at a plurality of different time points;
inputting the charging curve segment into a preset charging curve prediction model, and obtaining a predicted complete charging curve output by the charging curve prediction model;
determining the maximum electric quantity value of the electric core to be evaluated in the predicted complete charging curve;
and taking the percentage of the maximum electric quantity value relative to the preset rated electric quantity of the electric core to be evaluated as the battery health of the electric core to be evaluated.
In an embodiment, before the step of obtaining the historical charging curve segment of the to-be-evaluated battery cell, the method further includes:
acquiring a historical complete charging curve of a preset sample cell;
extracting a historical charging curve segment from the historical complete charging curve;
generating a training sample based on the historical charging curve segment, and taking the historical complete charging curve as a prediction label of the training sample;
training a preset initial model by using the training sample and the predictive label of the training sample to obtain the charging curve predictive model.
In an embodiment, the obtaining the historical complete charging curve of the preset sample cell includes:
acquiring charging states, voltages and currents of the preset sample battery cell at a plurality of different historical time points, wherein the charging states are charging or non-charging;
determining a plurality of historical charging time points in which the charging state is charging in the plurality of different historical time points;
determining an electric quantity of each historical charging time point based on the current of each historical charging time point;
and generating the historical complete charging curve by utilizing the voltages and the electric quantity of a plurality of historical charging time points.
In an embodiment, the extracting the historical charging curve segment in the historical complete charging curve includes:
dividing the historical complete charging curve into a plurality of curve segments according to a preset voltage interval, wherein the difference value between the maximum voltage and the minimum voltage in each curve segment is the voltage interval;
each of the curve segments is taken as one of the historical charging curve segments.
In an embodiment, the generating training samples based on the historical charge curve segments includes:
acquiring each historical charging time point in the historical charging curve segment as a target time point to obtain a plurality of target time points;
acquiring the temperatures and currents of the preset sample cells at a plurality of target time points;
and taking historical charging curve segments comprising the voltage, the electric quantity, the temperature and the current of the preset sample battery cells at a plurality of target time points as the training samples.
In a second aspect, an embodiment of the present application provides a method for applying a battery health degree, where the method includes:
after determining the health degree of the battery cells to be evaluated by using the method for determining the health degree of the battery according to any one of the above, determining a battery module in which the battery cells to be evaluated are positioned so as to obtain the health degrees of the batteries of the plurality of battery cells in the battery module;
determining the maximum value and the minimum value of the health degree in the battery health degrees of a plurality of battery cells in the battery module;
and judging whether the battery module has the problem of consistency of the health degree or not based on the difference value between the maximum value and the minimum value of the health degree.
In a third aspect, an embodiment of the present application provides a method for applying a battery health degree, where the method includes:
after determining the health degree of the battery cell to be evaluated by using the method for determining the health degree of the battery, obtaining the health degrees of the battery cells to be evaluated at a plurality of different detection time points;
and determining the residual life of the battery cell to be evaluated based on the battery health of a plurality of different detection time points.
In an embodiment, the determining the remaining life of the battery cell to be evaluated based on the battery health at a plurality of the different detection time points includes at least one of the following steps:
acquiring accumulated driving mileage of a vehicle in which the battery cells to be evaluated are positioned at a plurality of different detection time points, performing straight line fitting based on the battery health degrees and the accumulated driving mileage at the plurality of different detection time points to obtain a first straight line, and determining the remaining mileage of the battery cells to be evaluated based on the first straight line, wherein the remaining life comprises the remaining mileage;
and performing straight line fitting based on the battery health degrees at a plurality of different detection time points and a plurality of different detection time points to obtain a second straight line, and determining the residual time of the battery cell to be evaluated based on the second straight line, wherein the residual life comprises the residual time.
In a fourth aspect, embodiments of the present application provide an electronic device comprising a processor and a memory, the memory having stored therein a computer program configured to be executed by the processor to implement the method of determining battery health as described in any one of the above, or to implement the method of applying battery health as described in any one of the above.
In a fifth aspect, embodiments of the present application provide a computer storage medium storing a computer program configured to be executed by a processor to implement the method of determining the battery health as set forth in any one of the above, or to implement the method of applying the battery health as set forth in any one of the above.
The embodiment of the application has the beneficial effects that:
in the embodiment of the application, the complete charging curve is predicted based on the charging curve segment of the battery cell to be evaluated through the charging curve prediction model, and then the battery health degree of the battery cell to be evaluated is determined based on the complete charging curve.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a method for determining battery health according to an embodiment of the present application;
FIG. 2 is a flow chart of one embodiment of a training process for a charge curve prediction model provided by an embodiment of the present application;
fig. 3 is a flowchart of an embodiment of a method for applying battery health according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application. Furthermore, in the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In order to improve the estimation accuracy of the battery health degree, the embodiment of the application provides a method for determining the battery health degree, an application method, electronic equipment and a storage medium. The specific scheme is described in detail below.
In a first aspect, embodiments of the present application provide a method for determining a battery health. Specifically, referring to fig. 1, fig. 1 is a flowchart illustrating an embodiment of a method for determining a battery health. In fig. 1, the method for determining the battery health may include:
101. acquiring a charging curve segment of the battery cell to be evaluated, wherein the charging curve segment comprises voltages and electric quantity of the battery cell to be evaluated at different time points;
in an embodiment of the present application, the battery cell to be evaluated may be any battery cell in a battery module of an electric vehicle. The charging curve segment of the battery cell to be evaluated is a section of charging curve of the battery cell to be evaluated in the actual charging process. The charge curve segment is relative to a predicted complete charge curve described below, and in some embodiments of the application, the charge curve segment may be a segment of, i.e., a portion of, the predicted complete charge curve.
In some embodiments of the present application, obtaining a charging curve segment of a cell to be evaluated may include: acquiring actual operation parameters of the battery cell to be evaluated in the actual use process, wherein the actual operation parameters comprise charging states, voltages and currents of the battery cell to be evaluated at a plurality of different time points, and the charging states comprise charging or non-charging; screening a plurality of charging time points with charging states being charged from a plurality of different time points included in the actual operation parameters; generating a charging current curve based on the current of each charging time point, wherein the abscissa of the coordinate points in the charging current curve is a plurality of charging time points sequenced in sequence, and the ordinate of the coordinate points in the charging current curve is the current of the charging time points; integrating the charging current curve by an ampere-hour integration method to obtain the electric quantity of each charging time point in the charging current curve, namely the electric quantity of the battery cell to be evaluated at each charging time point; and generating a charging curve segment based on the electric quantity of the battery cell to be evaluated at each charging time point and the voltage of the battery cell to be evaluated at each charging time point, wherein the abscissa of the coordinate point in the charging curve segment is the electric quantity of the charging time point, and the ordinate of the coordinate point in the charging curve segment is the voltage of the charging time point.
In a further embodiment, since the current and temperature of the battery cell to be evaluated also affect the estimation result of the battery health, the actual operation parameters may further include the current and temperature of the battery cell to be evaluated at a plurality of different time points, and similarly, the charging curve segment may also include the voltage, the electric quantity, the current and the temperature of the battery cell to be evaluated at a plurality of different time points.
In a further embodiment, before screening out a plurality of charging time points in which the charging state is charging, from a plurality of different time points included in the actual operation parameters, the method may further include: preprocessing the actual operation parameters to remove abnormal data in the actual operation parameters.
The preprocessing may include: and eliminating missing data in the actual operation parameters. Taking an example that the actual operation parameters comprise the charging states, voltages and currents of the battery cells to be evaluated at a plurality of different time points, if the voltages of the battery cells to be evaluated at a certain time point are absent, the charging states, voltages and currents of the battery cells to be evaluated at the time point are all removed as missing data.
The pretreatment may also include: and eliminating abnormal values in the actual operation parameters. Taking an example that the actual operation parameters comprise the charging states, voltages and currents of the battery cells to be evaluated at a plurality of different time points, if the voltage of the battery cells to be evaluated at a certain time point exceeds a preset normal working voltage, the charging states, voltages and currents of the battery cells to be evaluated at the time point are all removed as abnormal values. For example, taking an example that the actual operation parameters include the charging states, voltages, currents and temperatures of the battery cells to be evaluated at a plurality of different time points, the abnormal temperatures in the actual operation parameters can be identified, and the charging states, voltages, currents and the abnormal temperatures at the time points of the abnormal temperatures are all removed as abnormal values, wherein the corresponding normal operation temperature range is not usually set because the temperature of the battery cells to be evaluated is often changed greatly, for example, the temperature difference of the battery cells to be evaluated is large in summer and winter, so that the abnormal temperatures in the actual operation parameters can be identified by using a 3 sigma criterion. The 3 sigma criterion is the Laida criterion, which means that a group of detection data is firstly assumed to contain only random errors, standard deviation is obtained by calculating the detection data, a section is determined according to a certain probability, and the error exceeding the section is considered to be not random error but coarse error, and the data containing the error should be removed.
102. Inputting the charging curve segment into a preset charging curve prediction model, and obtaining a predicted complete charging curve output by the charging curve prediction model;
in the embodiment of the application, a preset charging curve prediction model is used for predicting according to an input charging curve segment to obtain a predicted complete charging curve and outputting the predicted complete charging curve. The predetermined charge curve prediction model is typically a neural network model, for example, the predetermined charge curve prediction model may be an infomer model, which is a deep learning model for time series prediction. The model training process of the preset charging curve prediction model is detailed in the steps in the embodiment shown in fig. 2.
In some embodiments of the present application, the charging curve segment may be one curve segment in a predicted complete charging curve, where the predicted complete charging curve includes predicted voltages and predicted electric quantities of a plurality of cells to be evaluated at different predicted time points, and an abscissa of a coordinate point in the predicted complete charging curve is the predicted electric quantity at the predicted time point, and an ordinate of the coordinate point in the charging curve segment is the predicted voltage at the predicted time point.
103. Determining the maximum electric quantity value of the electric core to be evaluated in the prediction complete charging curve;
in the embodiment of the application, the predicted electric quantity with the largest value is determined from the predicted electric quantities of all coordinate points in the predicted complete charging curve and is used as the maximum electric quantity value of the battery cell to be evaluated. Since the predicted electric quantity in the predicted complete charge curve gradually increases with the lapse of the charge time, the predicted electric quantity with the largest value is often located in the coordinate point of the end position of the predicted complete charge curve.
104. And taking the percentage of the maximum electric quantity value relative to the preset rated electric quantity of the electric core to be evaluated as the battery health of the electric core to be evaluated.
In the embodiment of the application, the percentage of the maximum electric quantity value relative to the preset rated electric quantity of the electric core to be evaluated is calculated and directly used as the battery health of the electric core to be evaluated.
In the embodiment of the application, the complete charging curve is predicted based on the charging curve segment of the battery cell to be evaluated through the charging curve prediction model, and then the battery health degree of the battery cell to be evaluated is determined based on the complete charging curve.
In some embodiments of the present application, a model training process of a preset charge curve prediction model is described. Specifically, as shown in fig. 2, before obtaining the historical charging curve segment of the to-be-evaluated battery cell, the method may further include:
201. acquiring a historical complete charging curve of a preset sample cell;
in an embodiment of the present application, the preset sample cell may be a cell unit belonging to the same type of battery module as the above-mentioned cell to be evaluated, for example, since the battery modules of the same type of electric vehicle are often the same, a cell unit belonging to the same type of electric vehicle as the above-mentioned cell to be evaluated may be used as the preset sample cell. In some embodiments, the predetermined sample cell may also be the cell to be evaluated as described above.
The historical complete charging curve of the preset sample cell comprises a plurality of historical voltages and historical electric quantities of the preset sample cell at different historical time points.
In some embodiments of the present application, obtaining a historical complete charging curve of a preset sample cell may include: acquiring historical actual operation parameters of a preset sample cell in a historical actual use process, wherein the historical actual operation parameters comprise charging states, voltages and currents of the preset sample cell at a plurality of different historical time points, and the charging states comprise charging or non-charging; screening a plurality of historical charging time points with charging states being charged from a plurality of different historical time points included in the historical actual operation parameters; generating a historical charging current curve based on the current of each historical charging time point, wherein the abscissa of the coordinate points in the historical charging current curve is a plurality of historical charging time points sequenced in sequence, and the ordinate of the coordinate points in the historical charging current curve is the current of the historical charging time points; integrating the historical charging current curve by an ampere-hour integration method to obtain the electric quantity of each historical charging time point in the historical charging current curve, namely presetting the electric quantity of a sample cell at each historical charging time point; generating a history complete charging curve based on the electric quantity of a preset sample cell at each history charging time point and the voltage of the preset sample cell at each history charging time point, wherein the abscissa of a coordinate point in the history complete charging curve is the electric quantity of the history charging time point, and the ordinate of the coordinate point in the history complete charging curve is the voltage of the history charging time point.
In a further embodiment, before screening the historical charging state to be the charging time points in the different historical time points included in the historical actual operation parameters, the method may further include: preprocessing the historical actual operation parameters to remove abnormal data in the historical actual operation parameters. The specific steps of the pretreatment are similar to those of the embodiment shown in fig. 1, and will not be described here.
It should be noted that, since the preset sample cell generally will not be directly charged from the zero electric quantity to the full electric quantity in the actual use process, the complete charging curve of the preset sample cell will not include all the charging data from the zero electric quantity to the full electric quantity. Thus, in an embodiment of the present application, the historical complete charging curve of the preset sample cell may be a charging curve with a span of the percentage of the charging power greater than or equal to a preset threshold, the preset threshold may take on a value of 80%, for example, the historical complete charging curve of the preset sample cell may be a charging curve with a percentage of the charging power ranging from 10% to 90%, and it may be seen that the span of the percentage of the charging power is 80%.
202. Extracting a historical charging curve segment from a historical complete charging curve;
in an embodiment of the present application, the historical charging curve segment is one of the historical full charging curves. When the historical charging curve segment is extracted from the historical complete charging curve, the voltage span of the historical charging curve segment is generally a preset voltage interval, the voltage span refers to the difference between the maximum voltage and the minimum voltage in the historical charging curve segment, and the preset voltage interval can take a value of 0.2V (volt).
In some embodiments of the present application, extracting a historical charge curve segment from a historical complete charge curve may include: dividing a historical complete charging curve into a plurality of curve segments according to a preset voltage interval, wherein the difference value between the maximum voltage and the minimum voltage in each curve segment is the voltage interval; and taking each curve segment as a historical charging curve segment, so as to obtain a plurality of different historical charging curve segments, and increasing the number of training samples for model training.
203. Generating a training sample based on the historical charging curve segment, and taking the historical complete charging curve as a prediction label of the training sample;
in the embodiment of the application, if a plurality of different historical charging curve segments are extracted from the historical complete charging curve, a training sample can be generated based on each historical charging curve segment, and the historical complete charging curve is used as a prediction label of each training sample.
In some embodiments of the application, the embodiment shown in FIG. 1 is directed to: the charging curve segment comprises a plurality of situations of voltage, electric quantity, current and temperature of the battery cell to be evaluated at different time points, and each training sample also needs to comprise corresponding current data and temperature data. Specifically, generating training samples based on historical charge curve segments may include: acquiring each historical charging time point in the historical charging curve segment as a target time point to obtain a plurality of target time points; acquiring temperatures and currents of preset sample cells at a plurality of target time points from the above-mentioned historical actual operation parameters (in this embodiment, the above-mentioned historical actual operation parameters may further include temperatures and currents of preset sample cells at a plurality of different historical time points); the temperature and the current of a preset sample cell at each target time point are added into a historical charging curve segment; and taking the historical charging curve segments comprising the voltage, the electric quantity, the temperature and the current of the preset sample battery cell at each target time point as training samples.
201. Training a preset initial model by using a training sample and a prediction label of the training sample to obtain a charging curve prediction model.
In an embodiment of the present application, the preset initial model may be an Informir model. The training process is exemplified as follows: after obtaining training samples and predictive labels of the training samples, synthesizing more training samples and predictive labels of the more training samples to generate a training data set; dividing a plurality of training samples in a data set and predictive labels according to a preset proportion to obtain a model training set and a model testing set, wherein the preset proportion is generally 8:2; training a preset initial model by using a model training set to obtain a trained model; testing the trained model by using a model test set; and after the test is qualified, taking the model which is qualified in the test as a charging curve prediction model.
In a second aspect, on the basis of the method for determining the battery health shown in fig. 1 and 2, an embodiment of the present application provides a method for applying the battery health. The application method of the battery health degree is particularly used for evaluating the uniformity of the battery health degree of the battery module, and it can be understood that the stress of each battery core is different due to the fact that the positions of the battery cores in the battery module are different, so that the attenuation degree of the battery health degree of each battery core is different, and therefore the uniformity of the battery health degree of the battery module needs to be evaluated. Specifically, the application method of the battery health degree can comprise the following steps:
after determining the health degree of the battery cells to be evaluated by using the method for determining the health degree of the battery in any one of the above steps, determining a battery module in which the battery cells to be evaluated are located to obtain the health degrees of the battery cells in the battery module, wherein the health degrees of the battery cells in the battery module include the health degrees of the battery cells to be evaluated, and the method for obtaining the health degrees of the battery cells in the battery module is similar to the battery cells to be evaluated and is not repeated herein; determining the maximum value and the minimum value of the health degree in the battery health degrees of a plurality of battery cells in the battery module; based on the difference value between the maximum value and the minimum value of the health degree, judging whether the battery module has the problem of consistency of the health degree, for example, judging that the battery module has the problem of consistency of the health degree when the difference value between the maximum value of the health degree and the minimum value of the health degree is larger than a preset difference value, and judging that the battery module does not have the problem of consistency of the health degree when the difference value between the maximum value of the health degree and the minimum value of the health degree is smaller than or equal to the preset difference value, thereby judging the overall health degree of the battery module, wherein the preset difference value can take the value of 5%.
In a third aspect, on the basis of the method for determining the battery health shown in fig. 1 and 2, an embodiment of the present application provides a method for applying the battery health. The method for applying the battery health degree is specifically used for predicting the service life of the battery core to be evaluated, and referring to fig. 3, the method for applying the battery health degree may include:
301. after determining the health degree of the battery cell to be evaluated by using the method for determining the health degree of the battery in any one of the above steps, obtaining the health degrees of the battery cells to be evaluated at a plurality of different detection time points;
in an embodiment of the present application, the plurality of different detection time points may be different time points with larger time spans, for example, in the plurality of different detection time points, the time span of the adjacent detection time points is one day, and at this time, the battery health of the to-be-evaluated battery cell at the plurality of different detection time points is the daily battery health of the to-be-evaluated battery cell.
302. The remaining life of the cell to be evaluated is determined based on the battery health at a plurality of different detection time points.
In an embodiment of the application, the remaining lifetime (Remain Useful Life, RUL) may be the remaining mileage or the remaining time of the cell to be evaluated.
Taking the remaining life as the remaining mileage of the battery cell to be evaluated as an example, determining the remaining life of the battery cell to be evaluated based on the battery health degrees at a plurality of different detection time points may include: acquiring accumulated driving mileage of an electric vehicle in which a plurality of electric cores to be evaluated are positioned at different detection time points; because the accumulated running mileage and the battery health degree of the battery cell to be evaluated generally have a linear change relation, straight line fitting can be performed based on the battery health degrees and the accumulated running mileage of a plurality of different detection time points to obtain a first straight line, wherein the abscissa of a coordinate point in the first straight line is the accumulated running mileage, and the ordinate is the battery health degree; based on the first straight line, determining the remaining mileage of the battery cell to be evaluated, for example, in the first straight line, taking the accumulated mileage of the coordinate point with the battery health degree being the first preset health degree as the expected accumulated mileage, and taking the difference between the expected accumulated mileage and the current accumulated mileage of the electric vehicle where the battery cell to be evaluated is located as the remaining mileage of the battery cell to be evaluated, wherein the first preset health degree can take a value of 80%.
Taking the remaining life as the remaining time of the battery cell to be evaluated as an example, determining the remaining life of the battery cell to be evaluated based on the battery health degrees at a plurality of different detection time points may include: performing straight line fitting based on the battery health degrees of a plurality of different detection time points and a plurality of different detection time points to obtain a second straight line, wherein the abscissa of a coordinate point in the second straight line is the detection time point, and the ordinate is the battery health degree; based on the second straight line, determining the remaining time of the battery cell to be evaluated, for example, in the second straight line, a detection time point of a coordinate point of the battery health degree being a second preset health degree may be taken as an expected time point, and a difference value between the expected time point and the current time point may be taken as the remaining time of the battery cell to be evaluated, where the second preset health degree may take a value of 80%.
It can be seen that the embodiment of the application realizes the estimation of the residual life of the battery cell to be evaluated through the battery health of the battery cell to be evaluated at a plurality of different detection time points, so as to facilitate the timely replacement of the battery cell in the electric vehicle.
In a fourth aspect, embodiments of the present application provide an electronic device comprising a processor and a memory, the memory having stored therein a computer program configured to be executed by the processor to implement a method of determining the battery health of any one of the above, or to implement an application method of the battery health of any one of the above.
In a fifth aspect, embodiments of the present application provide a computer storage medium storing a computer program configured to be executed by a processor to implement a method of determining a battery health as in any one of the above, or to implement an application method of a battery health as in any one of the above.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present application, the present description should not be construed as limiting the present application.
Claims (10)
1. A method for determining the health of a battery, the method comprising:
acquiring a charging curve segment of a battery cell to be evaluated, wherein the charging curve segment comprises voltages and electric quantity of the battery cell to be evaluated at a plurality of different time points;
inputting the charging curve segment into a preset charging curve prediction model, and obtaining a predicted complete charging curve output by the charging curve prediction model;
determining the maximum electric quantity value of the electric core to be evaluated in the predicted complete charging curve;
and taking the percentage of the maximum electric quantity value relative to the preset rated electric quantity of the electric core to be evaluated as the battery health of the electric core to be evaluated.
2. The method for determining the health of a battery according to claim 1, further comprising, before the step of obtaining the historical charge curve segment of the battery cell to be evaluated:
acquiring a historical complete charging curve of a preset sample cell;
extracting a historical charging curve segment from the historical complete charging curve;
generating a training sample based on the historical charging curve segment, and taking the historical complete charging curve as a prediction label of the training sample;
training a preset initial model by using the training sample and the predictive label of the training sample to obtain the charging curve predictive model.
3. The method for determining the health of a battery according to claim 2, wherein the obtaining a historical complete charge profile of a predetermined sample cell comprises:
acquiring charging states, voltages and currents of the preset sample battery cell at a plurality of different historical time points, wherein the charging states are charging or non-charging;
determining a plurality of historical charging time points in which the charging state is charging in the plurality of different historical time points;
determining an electric quantity of each historical charging time point based on the current of each historical charging time point;
and generating the historical complete charging curve by utilizing the voltages and the electric quantity of a plurality of historical charging time points.
4. The method of claim 2, wherein extracting historical charge curve segments from the historical complete charge curve comprises:
dividing the historical complete charging curve into a plurality of curve segments according to a preset voltage interval, wherein the difference value between the maximum voltage and the minimum voltage in each curve segment is the voltage interval;
each of the curve segments is taken as one of the historical charging curve segments.
5. The method of determining battery health of claim 2, wherein the generating training samples based on the historical charge curve segments comprises:
acquiring each historical charging time point in the historical charging curve segment as a target time point to obtain a plurality of target time points;
acquiring the temperatures and currents of the preset sample cells at a plurality of target time points;
and taking historical charging curve segments comprising the voltage, the electric quantity, the temperature and the current of the preset sample battery cells at a plurality of target time points as the training samples.
6. The application method of the battery health degree is characterized by comprising the following steps of:
after determining the health degree of the battery cells to be evaluated by using the method for determining the health degree of the battery according to any one of claims 1 to 5, determining a battery module in which the battery cells to be evaluated are positioned so as to obtain the battery health degrees of a plurality of battery cells in the battery module;
determining the maximum value and the minimum value of the health degree in the battery health degrees of a plurality of battery cells in the battery module;
and judging whether the battery module has the problem of consistency of the health degree or not based on the difference value between the maximum value and the minimum value of the health degree.
7. The application method of the battery health degree is characterized by comprising the following steps of:
after determining the health of the battery cell to be evaluated by using the method for determining the health of the battery cell according to any one of claims 1 to 5, obtaining the health of the battery cell to be evaluated at a plurality of different detection time points;
and determining the residual life of the battery cell to be evaluated based on the battery health of a plurality of different detection time points.
8. The method for applying battery health according to claim 7, wherein the determining the remaining life of the battery cell to be evaluated based on the battery health at a plurality of the different detection time points includes at least one of:
acquiring accumulated driving mileage of a vehicle in which the battery cells to be evaluated are positioned at a plurality of different detection time points, performing straight line fitting based on the battery health degrees and the accumulated driving mileage at the plurality of different detection time points to obtain a first straight line, and determining the remaining mileage of the battery cells to be evaluated based on the first straight line, wherein the remaining life comprises the remaining mileage;
and performing straight line fitting based on the battery health degrees at a plurality of different detection time points and a plurality of different detection time points to obtain a second straight line, and determining the residual time of the battery cell to be evaluated based on the second straight line, wherein the residual life comprises the residual time.
9. An electronic device, characterized in that it comprises a processor and a memory, in which a computer program is stored, which computer program is configured to be executed by the processor to implement the method of determining the battery health of any one of claims 1 to 5, or to implement the method of applying the battery health of any one of claims 6 to 8.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program configured to be executed by a processor to implement the method of determining the battery health of any one of claims 1 to 5 or to implement the method of applying the battery health of any one of claims 6 to 8.
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| CN117574788B (en) * | 2024-01-17 | 2024-05-14 | 中国第一汽车股份有限公司 | Multi-scale modeling-based battery health degree prediction method and device |
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