CN114487887A - Battery health degree measuring method, device, equipment and storage medium - Google Patents
Battery health degree measuring method, device, equipment and storage medium 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]
- G01R31/392—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]
- G01R31/367—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]
- G01R31/385—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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Abstract
The application provides a method, a device, equipment and a storage medium for measuring the health degree of a battery. Acquiring a battery capacity increment curve of a battery to be tested, wherein the battery capacity increment curve of the battery to be tested is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be tested; determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested; and determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested. The method and the device are favorable for improving the accuracy of the health degree of the battery.
Description
Technical Field
The present disclosure relates to the field of battery technologies, and in particular, to a method, an apparatus, a device and a storage medium for measuring a health degree of a battery.
Background
Under the combined promotion of energy conservation and emission reduction and green environmental protection, electric automobiles are more and more concerned as new energy vehicles, and lithium batteries are widely used in the field of electric automobiles due to the green environmental protection performance of the lithium batteries. The power battery is one of the key parts of the electric automobile, and the performance and the cost of the electric automobile are greatly influenced by the power battery technology.
The state of health (SOH) of a lithium battery is a comprehensive indicator of the degree of aging of the battery. At present, most of the existing technologies for evaluating the health state of the lithium battery focus on an ampere-hour capacity integral method, an internal resistance method and the like, and the problems are that the data screening mechanisms of the methods are complex and the accuracy is low.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for measuring the health degree of a battery, which are used for solving the problems in the related technology, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for measuring battery health, including:
acquiring a battery capacity increment curve of a battery to be tested, wherein the battery capacity increment curve of the battery to be tested is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be tested;
determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested;
and determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
In a second aspect, an embodiment of the present application provides a battery health degree measurement apparatus, including:
the battery capacity increment curve acquiring module is used for acquiring a battery capacity increment curve of the battery to be detected, wherein the battery capacity increment curve of the battery to be detected is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be detected;
the actual charging capacity determining module is used for determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested;
and the health degree determining module is used for determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
In a third aspect, an embodiment of the present application provides a battery health degree measurement apparatus, including: a memory and a processor. Wherein the memory and the processor are in communication with each other via an internal connection path, the memory is configured to store instructions, the processor is configured to execute the instructions stored by the memory, and the processor is configured to perform the method of any of the above aspects when the processor executes the instructions stored by the memory.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the computer program runs on a computer, the method in any one of the above-mentioned aspects is executed.
The advantages or beneficial effects in the above technical solution at least include: by means of the mapping relation between the historical characteristic parameters and the historical battery capacity, the battery capacity increment curve (IC curve) of the battery to be tested is analyzed, so that the more accurate actual capacity of the battery can be obtained, and the accuracy of calculating the health degree of the battery can be improved.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a first flowchart of a battery health measurement method according to an embodiment of the present application;
FIG. 2 is an exemplary graph of a battery capacity delta curve of a battery health measurement method according to an embodiment of the present application;
FIG. 3 is a second flowchart of a method for measuring battery health according to an embodiment of the present disclosure;
FIG. 4 is a diagram illustrating an exemplary method for measuring battery health according to an embodiment of the present disclosure;
fig. 5 is a block diagram of a battery health measurement apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of a battery health measurement device according to an embodiment of the present application.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present application. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
Fig. 1 shows a flow chart of a battery health measurement method according to an embodiment of the present application. As shown in fig. 1, the battery health measuring method may include:
s101, obtaining a battery capacity increment curve of a battery to be tested, wherein the battery capacity increment curve of the battery to be tested is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be tested;
s102, determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested;
s103, determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
Wherein, the state of health (soh) of the battery is a percentage of the full charge capacity of the battery relative to the rated capacity. The initial value of the battery health may be 100%. The SOH of the lithium battery is a comprehensive index of the aging degree of the battery. Studies have shown that the main mechanisms of capacity fade in lithium batteries include: the occurrence of side reactions, precipitation of metallic lithium, anodic oxidation and cathodic reduction of the electrolyte, dissolution of the electrode active material, phase changes and structural changes.
The battery to be tested can be a lithium battery. The battery to be tested can be applied to vehicles such as electric automobiles, and can also be applied to various electronic equipment such as mobile phones, computers and the like.
According to the method and the device, the battery capacity increment curve (IC curve) of the battery to be tested is analyzed by means of the mapping relation between the historical characteristic parameters and the historical battery capacity, so that more accurate actual battery capacity can be obtained, and the accuracy of calculation of the health degree of the battery can be improved.
In one embodiment, step S101 includes:
acquiring charging data of at least one charging cycle to be tested of a battery to be tested;
and determining a battery capacity increment curve of at least one charging period to be tested according to the charging data of at least one charging period to be tested of the battery to be tested.
In the above embodiment, the number of the acquired charging cycles to be measured can be flexibly set according to the actual situation. For example, if the current health level is measured, it may be set to acquire charging data of the charging cycle to be measured in the last month. Charging data of at least one charging cycle to be tested is obtained.
Further, the charging data includes charging signals collected at preset time intervals when the battery is charged. Specifically, the charging data may include a charging time, a charging current, a cell voltage, and a battery state of charge SOC.
The state of charge (soc) of the battery represents a ratio of a remaining capacity of the battery after a period of use or after long-term leaving unused to a capacity percentage of a full state of the battery. Generally, the SOC ranges from 0 to 1, indicating that the battery is completely discharged when the SOC is 0, and indicating that the battery is completely charged when the SOC is 1.
In one embodiment, in step S101, the relationship between the battery capacity and the battery voltage during the charging process of the battery to be tested, which is represented by the battery capacity increment curve of the battery to be tested, may be specifically a relationship between a derivative dQ/dv of the battery capacity to the battery voltage and the battery voltage V. Referring to fig. 2, fig. 2 is an exemplary diagram of a battery capacity increment curve. In a coordinate system where the battery capacity increment curve is located, the vertical axis is a derivative value dQ/dv of the battery capacity to the battery voltage, and the horizontal axis is the battery voltage V.
In one embodiment, in step S102, determining the actual charge capacity of the battery under test includes determining the actual charge capacity of at least one charge cycle under test.
Step S103, comprising: and determining the health degree of the battery to be tested according to the actual charging capacity of the charging cycle to be tested, the change increment of the state of charge of the charging cycle to be tested, the rated capacity value of the battery to be tested and the number of the charging cycles to be tested.
Further, the calculation formula of the health degree of the battery to be measured is as follows:
the SOH represents the health degree of the battery to be tested, n represents the number of charging cycles to be tested, the delta SOC represents the change increment of the state of charge of the battery in the charging cycles to be tested, Q1 represents the actual charging capacity, and Q0 represents the rated capacity value of the battery to be tested.
In one embodiment, step S102 includes:
determining the value of the historical characteristic parameter of each charging cycle to be tested of the battery to be tested according to the battery capacity increment curve of each charging cycle to be tested of the battery to be tested;
and substituting the value of the historical characteristic parameter of each charging cycle to be tested into the mapping relation between the historical characteristic parameter corresponding to the model of the battery to be tested and the historical battery capacity, and solving to obtain the actual battery capacity corresponding to each charging cycle to be tested of the battery to be tested.
In one embodiment, referring to fig. 3, in step S102, the determining method of the mapping relationship between the historical characteristic parameter corresponding to the model of the battery to be tested and the historical battery capacity includes:
s301, determining a battery capacity increment curve and historical battery capacity corresponding to the historical charging period of the sample battery according to the charging data of the historical charging period of the sample battery, wherein the model of the sample battery is the same as that of the battery to be tested;
s302, determining historical characteristic parameters of the corresponding model of the battery to be tested according to a battery capacity increment curve corresponding to the historical charging period of the sample battery;
and S303, determining the mapping relation between the historical characteristic parameters of the corresponding model of the battery to be tested and the historical battery capacity.
Since the dynamic and thermodynamic characteristics of the battery change as the battery ages, the curve characteristic value of the battery capacity increment curve also changes. Therefore, in the embodiment, the actual battery capacity can be corrected by determining the mapping relationship between the battery capacity and the curve characteristics of the battery capacity increment curve (IC curve), so as to obtain more accurate battery health.
In one embodiment, in step S102, the historical battery capacity may be calculated according to a formula according to an ampere-hour integral value. Specifically, the electric quantity charged by the battery in the charging period, namely the historical battery capacity, is obtained by calculating the value of the charging current of the battery integrated with the whole time in the charging process.
In one embodiment, in step S301, determining a battery capacity increment curve corresponding to a historical charging cycle of a sample battery according to charging data of the historical charging cycle of the sample battery includes:
filtering the charging data of n historical charging cycles of the sample battery to obtain the filtered charging data of m historical charging cycles, wherein n is an integer and n is more than or equal to 1, m is an integer and belongs to [1, n ];
generating battery capacity increment curves corresponding to the m historical charging cycles respectively according to the filtered charging data of the m historical charging cycles;
wherein the filtering condition for filtering the charging data of the n historical charging cycles of the sample battery comprises at least one of:
after the ith historical charging period is finished, the time for stopping the work of the sample battery reaches the preset time, wherein i is an integer and belongs to [1, n ];
after the ith historical charging period is finished, the increment of the state of charge of the sample battery is larger than the preset increment;
and after the ith historical charging period is finished, the charging current of the sample battery is smaller than the preset current.
In the above embodiment, the history charging cycle that meets the filtering condition is filtered out by setting the corresponding filtering condition, so that the battery data of the abnormal history charging cycle is removed.
In one embodiment, step S302 includes:
determining a plurality of morphological characteristic parameters of a battery capacity increment curve corresponding to a historical charging period of a sample battery;
and performing principal component analysis on the plurality of morphological characteristic parameters to determine the historical characteristic parameters of the corresponding model of the battery to be tested.
In the principal component analysis, a plurality of variables with strong correlation can be recombined to generate a few variables which are not correlated with each other, so that the variables represent information of original variables as much as possible.
In the above embodiment, by using principal component analysis, morphological feature parameters can be generalized and dimensionality reduced, thereby avoiding the problem of multiple collinearity. If no measure of extracting the historical characteristic parameters from the principal components is taken, the stability of the mapping relationship established subsequently may be reduced, the coefficients of the morphological characteristic parameters may be too sensitive to the selection of the sample, and it is difficult to explain the individual influence of each independent variable (i.e., the morphological characteristic parameters) on the corresponding variable (i.e., the historical battery capacity). Therefore, the embodiment adopts principal component analysis, and improves the stability of the mapping relation between the historical characteristic parameters and the battery capacity which is established subsequently.
Further, the morphological characteristic parameters of the battery capacity increment curve (IC curve) include, but are not limited to: main peak height, main peak area, main peak position, main peak left slope, main peak right slope, and secondary peak height.
Wherein, the main peak refers to the highest peak value in the dQ/dv of the longitudinal axis of the IC curve, and the secondary peak refers to the second highest peak value in the dQ/dv of the longitudinal axis. The main peak area is the wedge column area enclosed by valley bottom points at two sides of the main peak and a projection point on the transverse shaft. The main peak left slope is the slope of the connecting line between the main peak top and the main peak left valley bottom point, and the main peak right slope is the slope of the connecting line between the main peak top and the main peak right valley bottom point. The height of the secondary peak is the dQ/dv value of the peak point at the second height in the curve.
In one embodiment, the method for generating the battery capacity increment curve comprises the following steps:
determining the battery voltage corresponding to each charging moment according to the battery current corresponding to each charging moment in the charging data;
determining the relation between the derivative value of the battery capacity relative to the battery voltage and the battery voltage according to the battery voltage corresponding to each charging moment and the battery voltage corresponding to each charging moment in the charging data;
and generating a battery capacity increment curve according to the relation between the derivative of the battery capacity relative to the battery voltage and the battery voltage.
Furthermore, a battery capacity increment curve (IC curve) is prepared by calculating a time interval to be an equal voltage interval according to a differential curve dQ/dv of the battery capacity and adopting a sliding average filtering with a preset fixed order for dQ/dv. The formula for dQ/dv is shown below:
where dt denotes a charging period dt, dQ denotes an amount of electric energy accumulated during the charging period dt, t0 denotes a start time of the charging period dt, i (t) denotes a charging current at time t, and dv denotes a change value of a voltage during the charging period dt.
One specific example of the embodiment of the present application is given below. This example provides a battery health measurement system. Taking a vehicle battery as an example, the flow of steps executed by the system can be exemplified by referring to fig. 4. The system builds a battery capacity estimation model according to the existing charging historical data, and estimates the health degree of the battery by using the battery capacity estimation model.
The battery health measurement system provided by this example includes: the system comprises a data collection module, a data filtering module, a feature extraction module, a Principal Component Regression (PCR) module and a health degree calculation module. The functions of the respective modules are as follows.
(1) A data collection module: firstly, taking lithium battery packs with the same model and different aging degrees as training samples of a battery capacity estimation model, and collecting charging signals of sample batteries in a preset time period as charging data; then, the charging data in the predetermined period of time is divided into each complete historical charging cycle (which may also be referred to as a charging process) and charging parameters for each historical charging cycle are determined.
The charging data may include charging time, charging current, cell voltage, and battery state of charge SOC.
Wherein the predetermined time period refers to a battery selection time window as a training sample. For example, the present example may set the predetermined period of time to within 12 months. The longer the preset time period is set, the larger the proportion of the samples with the attenuation tendency is, and the more accurate the model training effect is. In order to improve the accuracy of the model, the preset time period is recommended to be within a range of 6-36 months.
(2) A data filtering module: and filtering the charging data of the training sample according to the filtering condition to obtain the charging data meeting the filtering condition. Rejecting charging data not meeting filtering conditions
Wherein the filtering conditions include: after the charging is finished, the vehicle needs to be in a flameout state for more than 30 minutes; the SOC increment of each charging is more than or equal to 50 percent; and when the charging is finished, the charging current is less than 20A.
(3) A feature extraction module: first, the historical battery capacity (battery capacity may be referred to as charging capacity) of each historical charging cycle is calculated. Wherein the historical battery capacity can be obtained by calculating the ampere-hour integral value of the battery pack during the charging process time period. Then, a corresponding battery capacity increment curve (IC curve) is created based on the charge data of each historical charge cycle. Next, the numerical value of the morphological characteristic parameter of the battery capacity increment curve is extracted. Morphological feature parameters include, but are not limited to: main peak height, main peak area, main peak position, main peak left slope, main peak right slope, and secondary peak height.
The example of the capacitance capacity increment curve calculates a point position value dQ/dv of the battery capacity increment curve, and the calculation time interval is equal potential interval.
Wherein, the calculation formula of the point position value dQ/dv is
Wherein dt represents a charging time period dt, dQ represents an accumulated charging capacity in the charging time period dt, t0 represents a starting time of the charging time period dt, i (t) represents a charging current at time t, and dv represents a voltage change value in the charging time period dt.
And considering sampling noise, and processing the dQ/dv by adopting sliding average filtering with a preset fixed order. The window order adopting the moving average filtering is 2-4 orders, and the order adopted in the embodiment is 3 orders. The data is processed by the sliding average filtering, so that the specificity of data mutation caused by extreme values is improved and the accuracy and robustness of subsequent modeling are improved on the premise of keeping the data trend.
Further, a correlation coefficient between the charge capacity Qc and the morphological characteristic parameter is determined using a pearson correlation coefficient. And determining positive and negative influence factors and weights of the charging capacity Qc from the morphological characteristic parameters according to the correlation coefficients, and sequencing the influence factors.
(4) Principal Component Regression (PCR) module: first, principal component analysis is performed on morphological feature parameters to extract a plurality of principal component parameters. Then, a battery capacity estimation model (also referred to as a capacity regression calculation model) is constructed from the respective principal component parameters and the battery capacity parameters.
In the process of principal component analysis, the accumulated variance of the extracted principal components is required to be more than 70%. This requirement ensures that principal components can maximally represent the original data features with reduced dimensionality.
And fitting a function mapping relation between the charging capacity Qc and each curve characteristic principal component by adopting a least square method, and solving a regression equation. And, a coefficient of block (also called a deterministic coefficient) R is selected2As a test parameter for goodness-of-fit. If R is2And if the value is larger than the preset value, using the regression equation as a battery capacity estimation model. If R is2If the value is less than the preset value, the charging data of the new sample battery is uploaded to expand the training sample so as to continuously solve the regression equation until R2Meets the requirements.
The battery health degree measurement system of the present example may store the regression equation of the target battery model obtained by the solution. The battery health measurement system may update the regression equation for the target battery model once every preset time (e.g., 3 months). Specifically, the data collection module replaces the last version of the training sample with the historical charging data included in a longer time range, and then data feature extraction and regression analysis are performed through the data filtering module, the feature extraction module and the Principal Component Regression (PCR) module to obtain a new regression equation.
(5) A health degree calculation module: and determining the actual charging capacity of the charging period of the battery to be tested for n times by using the battery capacity estimation model and the charging data of the charging period of the battery to be tested for n times. Then, based on the formula
Calculating the health degree of the battery to be detected; wherein SOH represents the degree of health, n represents the number of charging cycles, Δ SOC represents the increment of change in state of charge per charging cycle, Q1 represents the actual charging capacity of the charging cycle, and Q0 represents the rated capacity value of the battery under test.
In combination with the above examples, it can be seen that the embodiments of the present application have at least the following beneficial effects:
(1) according to the embodiment of the application, the mapping relation of the curve characteristics of the battery capacity and the battery capacity increment curve (IC curve) is established through the charging data of a plurality of battery samples with the same model, then the actual charging capacity of the battery to be detected is corrected by utilizing the mapping relation, finally the health degree of the battery to be detected is calculated by utilizing the corrected actual charging electric quantity, and the accuracy of the health degree is improved. Furthermore, the method and the device are beneficial to better judging the degradation degree of the battery, analyzing the reason of battery aging and guiding and predicting the degradation track of the battery by improving the accuracy of calculation of the health degree of the battery.
(2) The embodiment of the application provides various curve change characteristics obtained by analyzing, inducing and reducing the dimensions of the principal components. And recombining a plurality of other variables with strong correlation to generate a few variables which are not correlated with each other, so that the variables represent the information of the original variables as much as possible, and the problem of multiple collinearity is avoided. If no principal component extraction measure is taken, the regression equation becomes unstable, the coefficient of the variable is too sensitive to the selection of the sample, and the independent influence of each independent variable on the variable is difficult to explain. By adopting the numerical processing method, the stability and the interpretability of a subsequent regression model are positively influenced.
Fig. 5 is a block diagram illustrating a structure of a battery health measuring apparatus according to an embodiment of the present invention. As shown in fig. 5, the apparatus may include:
a battery capacity increment curve obtaining module 501, configured to obtain a battery capacity increment curve of a battery to be tested, where the battery capacity increment curve of the battery to be tested is used to represent a relationship between a battery capacity and a battery voltage in a charging process of the battery to be tested;
an actual charging capacity determining module 502, configured to determine an actual charging capacity of the battery to be tested according to a mapping relationship between a historical characteristic parameter corresponding to the model of the battery to be tested and a historical battery capacity, and a battery capacity increment curve of the battery to be tested;
the health degree determining module 503 is configured to determine the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
In one embodiment, the battery capacity increment curve obtaining module 501 includes:
the charging data acquisition submodule is used for acquiring charging data of at least one charging cycle to be tested of the battery to be tested;
and the battery capacity increment curve determining submodule is used for determining the battery capacity increment curve of at least one charging cycle to be tested according to the charging data of at least one charging cycle to be tested of the battery to be tested.
In one embodiment, the actual charging capacity determining module 502 is specifically configured to determine an actual charging capacity of at least one charging cycle to be measured;
the health degree determination module 503 is specifically configured to determine the health degree of the battery to be tested according to the actual charging capacity of the charging cycle to be tested, the change increment of the state of charge of the charging cycle to be tested, the rated capacity value of the battery to be tested, and the number of the charging cycles to be tested.
In one embodiment, the system further comprises a mapping relation determining module, the mapping relation determining module is configured to determine a mapping relation between the historical characteristic parameter corresponding to the model of the battery to be tested and the historical battery capacity, wherein,
a mapping relationship determination module comprising:
the historical battery capacity and curve determining submodule is used for determining a battery capacity increment curve and historical battery capacity corresponding to a historical charging cycle of a sample battery according to charging data of the historical charging cycle of the sample battery, wherein the model of the sample battery is the same as that of the battery to be tested;
the historical characteristic parameter determining submodule is used for determining the historical characteristic parameter of the corresponding model of the battery to be tested according to the battery capacity increment curve corresponding to the historical charging period of the sample battery;
and the mapping relation determining submodule is used for determining the mapping relation between the historical characteristic parameters of the corresponding type of the battery to be tested and the historical battery capacity.
In one embodiment, the historical battery capacity and curve determination submodule includes:
the filtering unit is used for filtering the charging data of n historical charging cycles of the sample battery to obtain the filtered charging data of m historical charging cycles, wherein n is an integer and is more than or equal to 1, m is an integer and belongs to [1, n ];
the battery capacity increment curve determining unit is used for generating battery capacity increment curves corresponding to the m historical charging cycles respectively according to the filtered charging data of the m historical charging cycles;
wherein the filtering condition for filtering the charging data of the n historical charging cycles of the sample battery comprises at least one of the following:
after the ith historical charging period is finished, the time for stopping the work of the sample battery reaches the preset time, wherein i is an integer and belongs to [1, n ];
after the ith historical charging period is finished, the increment of the state of charge of the sample battery is larger than the preset increment;
and after the ith historical charging period is finished, the charging current of the sample battery is smaller than the preset current.
In one embodiment, the historical characteristic parameter determination sub-module includes:
the morphological characteristic parameter determination unit is used for determining a plurality of morphological characteristic parameters of a battery capacity increment curve corresponding to the historical charging period of the sample battery;
and the historical characteristic parameter determining unit is used for performing principal component analysis on the plurality of morphological characteristic parameters and determining the historical characteristic parameters of the corresponding model of the battery to be tested.
In one embodiment, the method for generating the battery capacity increment curve comprises the following steps:
determining the battery voltage corresponding to each charging moment according to the battery current corresponding to each charging moment in the charging data;
determining the relation between the derivative value of the battery capacity relative to the battery voltage and the battery voltage according to the battery voltage corresponding to each charging moment and the battery voltage corresponding to each charging moment in the charging data;
and generating a battery capacity increment curve according to the relation between the derivative of the battery capacity relative to the battery voltage and the battery voltage.
The functions of each module in each apparatus in the embodiments of the present invention may refer to the corresponding description in the above method, and are not described herein again.
Fig. 6 shows a block diagram of a battery health measurement apparatus according to an embodiment of the present invention. As shown in fig. 6, the battery health measuring apparatus includes: a memory 610 and a processor 620, the memory 610 having stored therein computer programs executable on the processor 620. The processor 620, when executing the computer program, implements the battery health measurement method in the above-described embodiments. The number of the memory 610 and the processor 620 may be one or more.
The battery health degree measuring apparatus further includes:
the communication interface 630 is used for communicating with an external device to perform data interactive transmission.
If the memory 610, the processor 620 and the communication interface 630 are implemented independently, the memory 610, the processor 620 and the communication interface 630 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Optionally, in an implementation, if the memory 610, the processor 620, and the communication interface 630 are integrated on a chip, the memory 610, the processor 620, and the communication interface 630 may complete communication with each other through an internal interface.
Embodiments of the present invention provide a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, the computer program implements the method provided in the embodiments of the present application.
The embodiment of the present application further provides a chip, where the chip includes a processor, and is configured to call and execute the instruction stored in the memory from the memory, so that the communication device in which the chip is installed executes the method provided in the embodiment of the present application.
An embodiment of the present application further provides a chip, including: the system comprises an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the application.
It should be understood that the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be an advanced reduced instruction set machine (ARM) architecture supported processor.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct memory bus RAM (DR RAM).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the present application are generated in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present application includes other implementations in which functions may be performed out of the order shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the method of the above embodiments may be implemented by hardware that is configured to be instructed to perform the relevant steps by a program, which may be stored in a computer-readable storage medium, and which, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The above-described integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (16)
1. A method for measuring a health level of a battery, comprising:
acquiring a battery capacity increment curve of a battery to be tested, wherein the battery capacity increment curve of the battery to be tested is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be tested;
determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested;
and determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
2. The method of claim 1, wherein the obtaining the battery capacity increment curve of the battery to be tested comprises:
acquiring charging data of at least one charging cycle to be tested of the battery to be tested;
and determining a battery capacity increment curve of at least one charging cycle to be tested according to the charging data of at least one charging cycle to be tested of the battery to be tested.
3. The method of claim 2, wherein said determining the actual charge capacity of the battery under test comprises determining the actual charge capacity of the at least one charge cycle under test;
the determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested comprises the following steps:
and determining the health degree of the battery to be tested according to the actual charging capacity of the charging cycle to be tested, the change increment of the state of charge of the charging cycle to be tested, the rated capacity value of the battery to be tested and the number of the charging cycles to be tested.
4. The method according to claim 1, wherein the determination of the mapping relationship between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity comprises:
according to charging data of a historical charging period of a sample battery, determining a battery capacity increment curve and historical battery capacity corresponding to the historical charging period of the sample battery, wherein the model of the sample battery is the same as that of the battery to be tested;
determining historical characteristic parameters of the corresponding model of the battery to be tested according to a battery capacity increment curve corresponding to the historical charging cycle of the sample battery;
and determining the mapping relation between the historical characteristic parameters of the corresponding model of the battery to be tested and the historical battery capacity.
5. The method of claim 4, wherein determining the incremental battery capacity curve corresponding to the historical charging cycle of the sample battery according to the charging data of the historical charging cycle of the sample battery comprises:
filtering the charging data of n historical charging cycles of the sample battery to obtain the filtered charging data of m historical charging cycles, wherein n is an integer and n is more than or equal to 1, m is an integer and belongs to [1, n ];
generating battery capacity increment curves corresponding to the m historical charging cycles respectively according to the filtered charging data of the m historical charging cycles;
wherein the filtering condition for filtering the charging data of the n historical charging cycles of the sample battery comprises at least one of:
after the ith historical charging period is finished, the time for stopping the work of the sample battery reaches preset time, wherein i is an integer and belongs to [1, n ];
after the ith historical charging period is finished, the increment of the charge state of the sample battery is larger than a preset increment;
and after the ith historical charging period is finished, the charging current of the sample battery is smaller than the preset current.
6. The method according to claim 4, wherein the determining the historical characteristic parameters of the model corresponding to the battery to be tested according to the battery capacity increment curve corresponding to the historical charging cycle of the sample battery comprises:
determining a plurality of morphological characteristic parameters of a battery capacity increment curve corresponding to the historical charging period of the sample battery;
and performing principal component analysis on the morphological characteristic parameters to determine historical characteristic parameters of the battery to be tested in the corresponding model.
7. The method according to any one of claims 1-6, wherein the battery capacity increment curve is generated in a manner that includes:
determining battery voltage corresponding to each charging moment according to the battery current corresponding to each charging moment in the charging data;
determining the relation between the derivative value of the battery capacity relative to the battery voltage and the battery voltage according to the battery voltage corresponding to each charging moment and the battery voltage corresponding to each charging moment in the charging data;
and generating the battery capacity increment curve according to the relation between the derivative of the battery capacity relative to the battery voltage and the battery voltage.
8. A battery health measurement device, comprising:
the battery capacity increment curve acquiring module is used for acquiring a battery capacity increment curve of the battery to be detected, wherein the battery capacity increment curve of the battery to be detected is used for representing the relation between the battery capacity and the battery voltage in the charging process of the battery to be detected;
the actual charging capacity determining module is used for determining the actual charging capacity of the battery to be tested according to the mapping relation between the historical characteristic parameters corresponding to the model of the battery to be tested and the historical battery capacity and the battery capacity increment curve of the battery to be tested;
and the health degree determining module is used for determining the health degree of the battery to be tested according to the actual charging capacity of the battery to be tested.
9. The apparatus of claim 8, wherein the battery capacity delta curve obtaining module comprises:
the charging data acquisition submodule is used for acquiring charging data of at least one charging cycle to be tested of the battery to be tested;
and the battery capacity increment curve determining submodule is used for determining the battery capacity increment curve of at least one charging cycle to be tested according to the charging data of at least one charging cycle to be tested of the battery to be tested.
10. The apparatus according to claim 9, wherein the actual charging capacity determining module is specifically configured to determine the actual charging capacity of the at least one charging cycle under test;
the health degree determination module is specifically used for determining the health degree of the battery to be tested according to the actual charging capacity of the charging cycle to be tested, the change increment of the state of charge of the charging cycle to be tested, the rated capacity value of the battery to be tested and the number of the charging cycles to be tested.
11. The apparatus of claim 8, further comprising a mapping relation determining module, configured to determine a mapping relation between historical characteristic parameters corresponding to the model of the battery to be tested and historical battery capacities, wherein,
the mapping relation determining module comprises:
the historical battery capacity and curve determining submodule is used for determining a battery capacity increment curve and historical battery capacity corresponding to a historical charging cycle of a sample battery according to charging data of the historical charging cycle of the sample battery, wherein the model of the sample battery is the same as that of the battery to be tested;
the historical characteristic parameter determining submodule is used for determining the historical characteristic parameter of the corresponding model of the battery to be tested according to the battery capacity increment curve corresponding to the historical charging period of the sample battery;
and the mapping relation determining submodule is used for determining the mapping relation between the historical characteristic parameters of the corresponding model of the battery to be tested and the historical battery capacity.
12. The apparatus of claim 11, wherein the historical battery capacity and curve determination submodule comprises:
the filtering unit is used for filtering the charging data of n historical charging cycles of the sample battery to obtain the filtered charging data of m historical charging cycles, wherein n is an integer and is more than or equal to 1, m is an integer and belongs to [1, n ];
a battery capacity increment curve determining unit, configured to generate battery capacity increment curves corresponding to m historical charging cycles respectively according to the filtered charging data of the m historical charging cycles;
wherein the filtering conditions for filtering the charging data of the n historical charging cycles of the sample battery include at least one of:
after the ith historical charging period is finished, the time for stopping the work of the sample battery reaches preset time, wherein i is an integer and belongs to [1, n ];
after the ith historical charging period is finished, the increment of the state of charge of the sample battery is larger than a preset increment;
and after the ith historical charging period is finished, the charging current of the sample battery is smaller than the preset current.
13. The apparatus of claim 11, wherein the historical feature parameter determination sub-module comprises:
the morphological characteristic parameter determination unit is used for determining a plurality of morphological characteristic parameters of a battery capacity increment curve corresponding to the historical charging period of the sample battery;
and the historical characteristic parameter determining unit is used for performing principal component analysis on the morphological characteristic parameters and determining the historical characteristic parameters of the corresponding model of the battery to be tested.
14. The apparatus according to any one of claims 8-13, wherein the battery capacity increment curve is generated in a manner that comprises:
determining battery voltage corresponding to each charging moment according to the battery current corresponding to each charging moment in the charging data;
determining the relation between the derivative value of the battery capacity relative to the battery voltage and the battery voltage according to the battery voltage corresponding to each charging moment and the battery voltage corresponding to each charging moment in the charging data;
and generating the battery capacity increment curve according to the relation between the derivative of the battery capacity relative to the battery voltage and the battery voltage.
15. A battery health measurement apparatus, comprising: a processor and a memory, the memory having stored therein instructions that are loaded and executed by the processor to implement the method of any of claims 1 to 7.
16. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN202011166937.8A CN114487887A (en) | 2020-10-27 | 2020-10-27 | Battery health degree measuring method, device, equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115219913A (en) * | 2022-09-19 | 2022-10-21 | 合肥原力众合能源科技有限公司 | Power battery full-life-cycle management system based on capacity increment method |
| WO2024036737A1 (en) * | 2022-08-17 | 2024-02-22 | 山东大学 | Aging state assessment and retirement screening method and system for power battery |
-
2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024036737A1 (en) * | 2022-08-17 | 2024-02-22 | 山东大学 | Aging state assessment and retirement screening method and system for power battery |
| CN115219913A (en) * | 2022-09-19 | 2022-10-21 | 合肥原力众合能源科技有限公司 | Power battery full-life-cycle management system based on capacity increment method |
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