CN115236540A - Health status quantification method, health status quantification device, health status storage medium and computer program product - Google Patents
Health status quantification method, health status quantification device, health status storage medium and computer program product Download PDFInfo
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- CN115236540A CN115236540A CN202110437216.4A CN202110437216A CN115236540A CN 115236540 A CN115236540 A CN 115236540A CN 202110437216 A CN202110437216 A CN 202110437216A CN 115236540 A CN115236540 A CN 115236540A
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- 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 embodiment of the disclosure relates to a health state quantification method, a health state quantification device, a storage medium and a computer program product. The method comprises the steps of obtaining state data of a plurality of battery packs, carrying out feature extraction on the state data of each battery pack to obtain a plurality of feature parameters of each battery pack, and quantifying the health state of each battery pack according to the plurality of feature parameters of each battery pack to obtain a quantified value of the health state of each battery pack, wherein the state data comprise different types of state data of the battery packs in different working modes. In the quantification method, because the state data comprises the multi-dimensional state data of the battery pack, the state information of the battery pack in each working mode can be comprehensively reflected, the characteristic parameters are extracted based on the state data, the health state of the battery pack is evaluated according to the characteristic parameters, and the effect of comprehensively evaluating the health state of the battery pack can be achieved.
Description
Technical Field
The present disclosure relates to the field of new energy power battery charging technologies, and in particular, to a method and an apparatus for quantifying a health status, a storage medium, and a computer program product.
Background
With the development of new energy power technology, various types of battery charging systems are established in China, places and enterprises to provide electric energy for new energy power equipment in time. In order to realize efficient energy supply, how to quickly, comprehensively and accurately evaluate the health state of each battery pack in the battery system becomes a technical problem to be solved urgently.
At present, each battery pack in a battery charging system reports battery data to a battery monitoring platform in real time in a normal working process, then the battery monitoring platform screens out available capacity data of a battery and nominal capacity data of the battery pack at an initial stage from the battery data of each battery pack, and then the Health State of each battery pack is evaluated by further calculating a ratio of the available capacity of each battery pack to the nominal capacity at the corresponding initial stage, namely a State of Health (SOH) parameter.
However, the above-described evaluation method has a problem that the state of health of the battery pack cannot be comprehensively and accurately evaluated.
Disclosure of Invention
The disclosed embodiments provide a health status quantification method, apparatus, storage medium, and computer program product, which can be used for comprehensively and accurately evaluating the health status of a battery pack.
In a first aspect, an embodiment of the present disclosure provides a method for quantifying a health status, where the method includes:
acquiring state data of a plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack;
and quantizing the health state of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain quantized values of the health states of the battery packs.
In a second aspect, an embodiment of the present disclosure provides an apparatus for quantifying a health status, the apparatus including:
the acquisition module is used for acquiring the state data of the plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
the extraction module is used for performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack;
and the quantization module is used for quantizing the health state of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the health state of each battery pack.
In a third aspect, the disclosed embodiments provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of the first aspect described above.
In a fourth aspect, the present disclosure provides a computer program product comprising a computer program, which when executed by a processor implements the method of the first aspect.
According to the method, the device, the storage medium and the computer program product for quantifying the health state provided by the embodiment of the disclosure, the state data of the plurality of battery packs are acquired, the characteristic extraction is performed on the state data of each battery pack to obtain a plurality of characteristic parameters of each battery pack, and the health state of each battery pack is quantified according to the plurality of characteristic parameters of each battery pack to obtain a quantified value of the health state of each battery pack, wherein the state data comprises different types of state data of the battery packs in different working modes. In the quantification method, because the state data comprises the multi-dimensional state data of the battery pack, the state information of the battery pack in each working mode can be comprehensively reflected, the characteristic parameters are extracted based on the state data, the health state of the battery pack is evaluated according to the characteristic parameters, and the effect of comprehensively evaluating the health state of the battery pack can be achieved. In addition, the quantization method provided by the application also realizes the quantization of the characteristic parameters of multiple dimensions, so that the health state of the battery pack can be evaluated according to specific quantization values, and the accuracy of the health state evaluation of the battery pack is improved.
Drawings
FIG. 1 is an internal block diagram of a battery monitoring cloud platform in one embodiment;
FIG. 2 is a flow chart illustrating a method for quantifying a health status according to one embodiment;
FIG. 3 is a flowchart illustrating an implementation manner of S103 in the embodiment of FIG. 2;
FIG. 4 is a flowchart illustrating an implementation manner of S202 in the embodiment of FIG. 3;
FIG. 5 shows a block diagram of S301 in the embodiment of FIG. 4 a flow diagram of a method of implementation;
FIG. 6 is a flow diagram illustrating a method for quantifying a health status according to one embodiment;
FIG. 7 is a flowchart illustrating an implementation manner of S102 in the embodiment of FIG. 2;
FIG. 8 is a flow chart illustrating a method for quantifying a health status according to one embodiment;
FIG. 9 is a flow chart illustrating a method for quantifying a health status according to one embodiment;
FIG. 10 is a block diagram of an apparatus for health status quantification in one embodiment;
FIG. 11 is a block diagram of an apparatus for health status quantification in one embodiment;
FIG. 12 is a block diagram showing the structure of a health status quantifying means in one embodiment;
FIG. 13 is a block diagram showing the structure of a health status quantifying means in one embodiment;
FIG. 14 is a block diagram showing the structure of a health status quantifying means in one embodiment;
FIG. 15 is a block diagram showing the structure of a health status quantifying means in one embodiment;
FIG. 16 is another internal block diagram of the battery monitoring cloud platform in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clearly understood, the embodiments of the present disclosure are described in further detail below with reference to the accompanying drawings and the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the embodiments of the disclosure and that no limitation to the embodiments of the disclosure is intended.
First, before specifically describing the technical solution of the embodiment of the present disclosure, a technical background or a technical evolution context on which the embodiment of the present disclosure is based is described. Generally, in the technical field of charging new energy power batteries, the current technical background is as follows: with the development of new energy power technology, various types of battery charging systems are established in China, places and enterprises to provide electric energy for new energy power equipment in time. The safety problem of the battery pack in the battery system during the use process is increasingly highlighted. As the service time of each battery pack in a battery system increases, the battery packs have the phenomena of capacity reduction, internal resistance increase, inconsistency and the like. Therefore, when various shared electric vehicles or shared electric bicycles are charged urgently, how to ensure that the battery pack is in a healthy state all the time when the battery pack is obtained from the battery system for use, so as to realize efficient energy supply becomes a necessary condition for the current application of the battery system. At present, a battery system reports battery data of each battery pack managed in real time to a battery monitoring platform, the monitoring platform screens available capacity data of the battery and nominal capacity data of the battery pack at an initial stage from the battery data of each battery pack, and then, the Health State (State of Health, SOH) parameter is further calculated by calculating a ratio of the available capacity of each battery pack to the nominal capacity at the corresponding initial stage, so as to realize the evaluation of the Health State of each battery pack. However, since SOH cannot describe the state of the battery pack completely and accurately, the method for estimating the state of health of the battery pack using SOH is relatively simple and not accurate enough. Based on this background, the applicant finds that the state data of the battery pack in different working modes can comprehensively reflect the health state of the battery pack through long-term model simulation research and development and experimental data collection, demonstration and verification, and how to comprehensively and accurately describe the health state of the battery pack through analyzing the state data of the battery pack in different working modes becomes a problem to be solved urgently at present. In addition, it should be noted that, through the method for comprehensively and accurately describing the state of health of the battery pack by analyzing the state data of the battery pack in different operating modes and the technical solutions introduced in the following embodiments, the applicant has paid a lot of creative efforts.
The following describes technical solutions related to the embodiments of the present disclosure with reference to a scenario in which the embodiments of the present disclosure are applied.
The health status quantification method provided by the application can be applied to a battery monitoring cloud platform shown in fig. 1, the battery monitoring cloud platform can be a server, the battery monitoring cloud platform can also be a terminal, and the internal structure diagram of the battery monitoring cloud platform can be shown in fig. 1. The battery monitoring cloud platform comprises a processor, a memory and a network interface which are connected through a system bus. Wherein the processor of the battery monitoring cloud platform is configured to provide computing and control capabilities. The storage of the battery monitoring cloud platform comprises a nonvolatile storage medium and an internal storage. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the battery monitoring cloud platform is used for storing state data of the battery pack. The network interface of the battery monitoring cloud platform is used for being connected and communicated with an external terminal through a network. The computer program is executed by a processor to implement a method of quantifying a health status.
In one embodiment, as shown in fig. 2, a method for quantifying a health status is provided, which is described by taking the method as an example for being applied to the battery monitoring cloud platform in fig. 1, and includes the following steps:
s101, state data of a plurality of battery packs are acquired.
The State data includes different types of State data of the battery pack in different operation modes, for example, the State data may include at least two types of data of resistance of the battery pack, cell voltage of the battery pack, total voltage of the battery pack, temperature of the battery pack, state of Charge (State of Charge SOC) of the battery pack, number of times of cycle charging of the battery pack, number of times of cycle discharging of the battery pack, accumulated charging energy of the battery pack, accumulated discharging energy of the battery pack, accumulated standing energy of the battery pack, temperature of the battery pack, capacity of the battery pack, and the like. The operation mode includes any one of a rest operation mode, a discharge operation mode, and a charge operation mode.
In this embodiment, the battery systems distributed in each area upload different types of state data of the battery packs managed by each battery system in each working mode to the battery monitoring cloud platform at a certain frequency in each period. The battery monitoring cloud platform can receive state data of different types of battery packs at each time period and each working mode, and can also receive state data of a large batch of battery packs of the same type at each time period and each working mode. It should be noted that the battery monitoring cloud platform may obtain state data of the plurality of battery packs in a preset time, and the preset time may be determined by the battery monitoring cloud platform in advance according to an actual monitoring requirement, for example, the preset time may be different time periods such as one week, one month, one quarter, and the like.
And S102, performing feature extraction on the state data of each battery pack to obtain a plurality of feature parameters of each battery pack.
The characteristic parameters are used for describing the state data and can reflect the health state of the battery pack in different working modes. In this embodiment, when the battery monitoring cloud platform acquires the state data of the batch of battery packs, the characteristic parameters of each battery pack in different working modes can be further calculated or estimated by analyzing the state data of each battery pack in different working modes. For example, the characteristic parameters may include the magnitude of self-discharge (leakage current) of the battery pack calculated from the stationary state data for a preset time or more, the magnitude of internal resistance of the battery pack estimated when there is a step current in the outside of the battery pack after the battery pack has been stationary for a certain period of time, the capacity of the battery pack estimated from the effective battery charge and discharge data, and the like. Optionally, the battery monitoring cloud platform may also calculate or estimate characteristic parameters of each battery pack in different time periods by analyzing state data of each battery pack in different time periods, for example, the characteristic parameters may include a temperature variation trend, a resistance variation trend, a capacity variation trend, and the like of the battery pack in one day. Optionally, the battery monitoring cloud platform may also calculate or estimate different characteristic parameters of each battery pack by analyzing different types of state data of each battery pack, for example, the characteristic parameters may include a cell voltage of the battery pack, an SOC of the battery pack, and a temperature of the battery pack. It should be noted that each battery pack has a plurality of characteristic parameters, and the types of the characteristic parameters corresponding to different battery packs are the same, for example, if battery pack #1 corresponds to characteristic parameter a, characteristic parameter B, and characteristic parameter C, and battery pack #2 corresponds to characteristic parameter D, characteristic parameter E, and characteristic parameter F, then the types of characteristic parameter a and characteristic parameter D are the same (for example, the characteristic parameter is the voltage of the battery pack), the types of characteristic parameter B and characteristic parameter E are the same (for example, the characteristic parameter is the current of the battery pack), and the types of characteristic parameter C and characteristic parameter F are the same (for example, the characteristic parameter is the capacity of the battery pack).
And S103, quantizing the health state of each battery pack according to the plurality of characteristic parameters of each battery pack to obtain a quantized value of the health state of each battery pack.
In this embodiment, when the battery monitoring cloud platform obtains the plurality of characteristic parameters of each battery pack, the battery monitoring cloud platform may further perform quantitative analysis on each characteristic parameter of the battery pack to obtain a quantitative value corresponding to each characteristic parameter of the battery pack, and then evaluate the health state of the battery pack according to the quantitative values corresponding to the plurality of characteristic parameters of the battery pack and the degree of correlation between each characteristic parameter and the health state of the battery pack, so as to obtain the quantitative value of the health state of the battery pack. For example, assuming the characteristic parameter a and the characteristic parameter B of the battery pack #1, the characteristic parameter a and the characteristic parameter B of the battery pack #1 are respectively subjected to quantization analysis to obtain a quantized value A1 of the characteristic parameter a and a quantized value B1 of the characteristic parameter B, and then the quantized value of the state of health of the battery pack #1 is determined according to the quantized value A1 and the quantized value B1, for example, since the characteristic parameter a and the characteristic parameter B have the same degree of correlation with the state of health of the battery pack #1, the quantized value A1 and the quantized value B1 may be averaged in a later period, and then the average value of the quantized value A1 and the quantized value B1 is used as the quantized value of the state of health of the battery pack # 1.
Optionally, when the same type of characteristic parameters exist in the multiple characteristic parameters of the battery pack, the battery monitoring cloud platform may perform quantitative analysis on the characteristic parameters of each type of the battery pack to obtain a quantitative value corresponding to the characteristic parameters of each type of the battery pack, and then evaluate the health state of the battery pack according to the quantitative value corresponding to the characteristic parameters of each type of the battery pack and the correlation degree between the characteristic parameters of each type and the health state of the battery pack, so as to obtain the quantitative value of the health state of the battery pack.
The method for quantifying the health state obtains a plurality of characteristic parameters of each battery pack by acquiring the state data of the plurality of battery packs and performing characteristic extraction on the state data of each battery pack, and quantifies the health state of each battery pack according to the plurality of characteristic parameters of each battery pack to obtain a quantified value of the health state of each battery pack, wherein the state data comprise different types of state data of the battery packs in different working modes. In the quantification method, because the state data comprises the multi-dimensional state data of the battery pack, the state information of the battery pack in each working mode can be comprehensively reflected, the characteristic parameters are extracted based on the state data, the health state of the battery pack is evaluated according to the characteristic parameters, and the effect of comprehensively evaluating the health state of the battery pack can be achieved. In addition, the quantization method provided by the application also realizes the quantization of the characteristic parameters of multiple dimensions, so that the health state of the battery pack can be evaluated according to specific quantization values, and the accuracy of the health state evaluation of the battery pack is improved.
In one embodiment, as shown in fig. 3, the step S103 of "quantizing the state of health of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the state of health of each battery pack" includes:
s201, determining the weight coefficient of each type of characteristic parameter of each battery pack.
The weight coefficient represents the correlation degree between the characteristic parameter and the state of health, the larger the weight coefficient is, the larger the correlation between the corresponding characteristic parameter and the state of health of the battery pack is, and the smaller the weight coefficient is, the smaller the correlation between the corresponding characteristic parameter and the state of health of the battery pack is.
In this embodiment, when the battery monitoring cloud platform acquires different types of characteristic parameters of each battery pack, the weight coefficient of each type of characteristic parameter of each battery pack may be further determined according to the type of the battery pack; optionally, the weight coefficient of each type of characteristic parameter of each battery pack may also be determined according to the application requirement; optionally, the weighting coefficients of the characteristic parameters of each type of each battery pack may also be determined by combining the type of the battery and the application requirements. For example, when the battery pack is a power type battery pack, a larger value may be set for the weight coefficient corresponding to the characteristic parameter belonging to the internal resistance type; when the battery pack is an energy type battery pack, the weight coefficient corresponding to the characteristic parameter belonging to the capacity type can be set to a larger value; when the user pays attention to the thermal current characteristic of the battery pack according to the application requirement, the weight coefficient corresponding to the characteristic parameter belonging to the thermal current is set to be a larger value.
And S202, quantizing the health state of each battery pack according to the characteristic parameters of each type of each battery pack and the weight coefficients of the characteristic parameters of each type of each battery pack to obtain quantized values of the health state of each battery pack.
In this embodiment, when the battery monitoring cloud platform obtains the feature parameters of each type of each battery pack and the weight coefficients of the feature parameters of each type of each battery pack, the feature parameters of each type of each battery pack may be further subjected to quantization analysis to obtain quantization values corresponding to the feature parameters of each type of each battery pack, and then the health state of each battery pack is evaluated according to the quantization values corresponding to the feature parameters of each type of each battery pack and the weight coefficients of the feature parameters of each type, so as to obtain the quantization value of the health state of each battery pack. For example, assuming that the characteristic parameter a and the characteristic parameter B of the battery pack #1, the weight coefficient of the characteristic parameter a is A1, and the weight coefficient of the characteristic parameter B is a2, the characteristic parameter a and the characteristic parameter B of the battery pack #1 are respectively subjected to quantization analysis to obtain a quantized value A1 of the characteristic parameter a and a quantized value B1 of the characteristic parameter B, and then the quantized value C1= A1+ B1 a2 of the health state of the battery pack #1 is determined according to the quantized value A1, the weight coefficient A1, the quantized value B1, and the weight coefficient a2.
In the method for quantizing the health state implemented in the above embodiment, since the weighting coefficients of the different types of feature parameters may reflect the degree of correlation between the different types of feature parameters and the health state, the health state of each battery pack is quantized later based on the different types of feature parameters of the battery pack and the corresponding weighting coefficients, and an accurate quantization value may be obtained.
In an embodiment, an implementation manner of the foregoing S202 is further provided, as shown in fig. 4, where in the foregoing S202 "quantize the health status of each battery pack according to the characteristic parameters of each type of each battery pack and the weight coefficients of the characteristic parameters of each type of each battery pack, to obtain a quantized value of the health status of each battery pack" includes:
and S301, determining the dispersion of the characteristic parameters of each type of each battery pack.
The dispersion of the characteristic parameters of each type of each battery pack is used for representing the difference degree between the characteristic parameters of the same type of each battery pack and other battery packs. When the dispersion of the characteristic parameters is large, the characteristic parameters are described as abnormal, and the battery pack with the characteristic parameters is indicated as possible to be in failure; when the dispersion of the characteristic parameter is small, it indicates that the characteristic parameter may be a normal characteristic parameter, indicating that the battery pack having the characteristic parameter is healthy.
In this embodiment, when the battery monitoring cloud platform acquires different types of characteristic parameters of each battery pack, the dispersion of each type of characteristic parameter of each battery pack in the batch of battery packs may be further obtained according to a dispersion calculation method, so as to evaluate the health state of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack.
Optionally, as shown in fig. 5, when the battery monitoring cloud platform executes the step S301, the following steps may be specifically performed:
s3011, an average value of the characteristic parameters of each type of each battery pack is determined.
In this embodiment, since there may be a plurality of characteristic parameters of one type for each battery pack, for example, the characteristic parameters of the temperature type corresponding to battery pack #1 include: three different characteristic parameters of 35 ℃, 34 ℃ and 36 ℃. Therefore, when the battery monitoring cloud platform acquires the characteristic parameters of each type of each battery pack, further calculation is needed to obtain an average value of the characteristic parameters of each type of each battery pack.
S3012, determining an arithmetic average of the characteristic parameters of each type based on the average of the characteristic parameters of each type of each battery pack.
In this embodiment, after the average value of each type of characteristic parameter of each battery pack is obtained through calculation by the battery monitoring cloud platform, the arithmetic average value of each type of characteristic parameter in each type of characteristic parameter of all battery packs may be further obtained through calculation by a corresponding arithmetic average value calculation method.
Optionally, the battery monitoring cloud platform may obtain an arithmetic average of the characteristic parameter of each type through the following relation (1):
in the above formula, i represents the type number of the characteristic parameter; j represents the serial number of the battery pack; m represents the number of battery packs; m j_i An average value of characteristic parameters of the ith type representing the jth battery pack;
represents the arithmetic mean of the characteristic parameters of the i-th type.
S3013, the dispersion of the characteristic parameters of each type for each battery group is obtained based on the average of the characteristic parameters of each type for each battery group and the arithmetic average of the characteristic parameters of each type.
Specifically, after the battery monitoring cloud platform calculates and obtains the average value of each type of characteristic parameter of each battery pack and the arithmetic average value of each type of characteristic parameter, the dispersion of each type of characteristic parameter of each battery pack can be further calculated and obtained through a corresponding dispersion calculation method.
Optionally, the battery monitoring cloud platform may obtain the dispersion of each type of characteristic parameter of each battery pack through the following relation (2):
in the above formula, i represents the type number of the characteristic parameter; j represents the serial number of the battery pack; m is a group of j_i An average value of characteristic parameters of the ith type representing the jth battery pack;
an arithmetic mean value representing the characteristic parameter of the i-th type; mu.s j_i The dispersion of the characteristic parameters of the ith type of the jth battery pack is expressed.
And S302, obtaining a quantized value of the health state of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack and the weight coefficient of each type of characteristic parameter of each battery pack.
In this embodiment, when the battery monitoring cloud platform acquires the dispersion of each type of characteristic parameter and the weight coefficient of each type of characteristic parameter of each battery pack, a quantization value of the health state of each battery pack may be further calculated according to the dispersion of each type of characteristic parameter and the weight coefficient of each type of characteristic parameter of each battery pack by a corresponding quantization calculation method.
Optionally, the battery monitoring cloud platform may obtain a quantized value of the state of health of the battery pack through the following relation (3):
in the above formula, i represents the type number of the characteristic parameter of each battery pack; n represents the number of types of characteristic parameters of each battery pack; k is a radical of formula j_i A weight coefficient indicating an i-th type of characteristic parameter of a j-th battery pack; mu.s j_i A dispersion representing the i-th type of characteristic parameter of the j-th battery pack; z j A quantized value representing the state of health of the jth battery pack.
In the method for quantifying the state of health implemented by the embodiment, since the dispersion of the different types of characteristic parameters of each battery pack can intuitively and accurately reflect the state of health of each battery pack, the state of health of each battery pack is quantified by using the dispersion of the different types of characteristic parameters of the battery pack, and an accurate quantified value of the state of health of the battery pack can be obtained.
In practical applications, after the battery monitoring cloud platform determines the dispersion of the characteristic parameters of each type of each battery pack, it may also determine whether each battery pack is a faulty battery pack according to the dispersion of the characteristic parameters of each type of each battery pack, and based on this, as shown in fig. 6, after the battery monitoring cloud platform performs the above step S301, the following steps are further performed:
and S401, comparing the dispersion of each type of characteristic parameter of each battery pack with a preset dispersion threshold corresponding to each type of characteristic parameter.
The preset dispersion threshold is an index parameter for measuring whether the characteristic parameter of the battery pack is abnormal, and if the characteristic parameter is abnormal, the battery pack with the characteristic parameter is a failed battery pack.
S402, if the dispersion degree of at least one type of characteristic parameter in the battery pack is larger than the corresponding preset dispersion degree threshold value, the battery pack is determined to be a fault battery pack.
In this embodiment, if the dispersion of at least one type of characteristic parameter in the battery pack is greater than the corresponding preset dispersion threshold, it is determined that the battery pack is a faulty battery pack; if the dispersion of the characteristic parameters which is greater than the preset dispersion threshold value does not exist in the battery pack, the battery pack is a healthy battery pack. For example, assuming that the battery pack #1 has the characteristic parameter a, the characteristic parameter B, and the characteristic parameter C, if the dispersion of the characteristic parameter a of the battery pack #1 is greater than the corresponding preset dispersion threshold, the dispersion of the characteristic parameter B of the battery pack #1 is smaller than the corresponding preset dispersion threshold, and the dispersion of the characteristic parameter C of the battery pack #1 is smaller than the corresponding preset dispersion threshold, it is determined that the battery pack #1 is a faulty battery pack; if the dispersion of the characteristic parameter a of the battery pack #1 is smaller than the corresponding preset dispersion threshold, the dispersion of the characteristic parameter B of the battery pack #1 is smaller than the corresponding preset dispersion threshold, and the dispersion of the characteristic parameter C of the battery pack #1 is smaller than the corresponding preset dispersion threshold, it is determined that the battery pack #1 is a healthy battery pack. In this embodiment, after the battery monitoring cloud platform determines that the battery pack is the faulty battery pack in the monitoring process, the battery monitoring cloud platform can also send out a warning signal to discover bad batteries in time, improve the safety of the battery system during battery use, and ensure that the battery system can efficiently provide electric energy for new energy equipment.
In an embodiment, an implementation manner of the foregoing S102 is further provided, and as shown in fig. 7, the foregoing S102 "performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack" includes:
s501, segmenting the state data of each battery pack according to a preset segmentation rule to obtain a plurality of data segments of each battery pack; the division rule comprises division according to a preset time period and division according to different working modes.
In this embodiment, when the battery monitoring cloud platform acquires the state data of each battery pack, since the state data includes different types of state data of the battery packs in different working modes, the state data is multidimensional state data, and the state data may be data acquired by the battery monitoring cloud platform within a long time period, after the battery monitoring cloud platform acquires the state data, the state data may be divided into fragment data with a small data size according to a preset time period and different working modes in advance, so as to be processed later. Specifically, the battery monitoring cloud platform may first divide the state data according to a preset time period, for example, divide the state data in a week into state data of each day; the state data divided according to time is divided according to different working modes, for example, the state data is divided into state data in a standing working mode, state data in a charging working mode and state data in a discharging working mode. Optionally, the battery monitoring cloud platform may also divide the state data according to different working modes, and then divide the state data divided according to the working modes according to different preset time periods. Optionally, after the battery monitoring cloud platform performs fragment division on the state data of each battery pack according to a preset division rule, the divided state data can be further divided according to the size of the preset data fragment, so that the finally obtained data fragment meets the data processing requirement, and the efficiency of post-processing data is improved. After the battery monitoring cloud platform segments the state data of each battery pack, a plurality of data segments corresponding to each battery pack can be obtained, and the plurality of data segments may include data segments of the same time period and different types, data segments of the same time period and the same type, data segments of different time periods and the same type, and data segments of different time periods and different types.
And S502, respectively extracting the characteristics of each data segment of each battery pack to obtain a plurality of characteristic parameters of each battery pack.
In this embodiment, after the battery monitoring cloud platform acquires the plurality of data segments of each battery pack based on the step S501, feature extraction may be performed on each data segment, so as to obtain at least one feature parameter corresponding to each data segment, and finally, a plurality of feature parameters of each battery pack may be obtained, where the plurality of feature parameters include feature parameters of the same type and feature parameters of different types. For example, the characteristic parameter of the data segment #1 may be the internal resistance of the battery pack, or may include both the internal resistance of the battery pack and the capacity of the battery pack.
Furthermore, when the battery monitoring cloud platform acquires the state data of each battery pack, abnormal data processing can be performed on the state data firstly, so that data which obviously belong to abnormality in the state data are eliminated, and the influence of the abnormal data on the health state of the battery pack in the later period is avoided. Based on this, the method described in the embodiment of fig. 7, as shown in fig. 8, further includes the steps of:
s503, performing abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack.
In this embodiment, the battery monitoring cloud platform can screen abnormal data in the state data according to a preset abnormal data judgment condition. The abnormal data determination conditions may include conditions that the voltage does not meet a preset voltage threshold, the current does not meet a preset current threshold, the internal resistance does not meet a preset internal resistance threshold, the temperature does not meet a preset temperature threshold, the number of charging cycles does not meet a preset number of thresholds, the capacity does not meet a preset capacity threshold, the SOC does not meet a preset charge threshold, and the like in the state data.
Optionally, a specific abnormal data processing method is provided, where the method includes: screening temperature data from the state data of each battery pack, and removing the temperature data within a preset temperature range to obtain data to be processed of each battery pack; and/or screening out voltage data from the data to be processed of each battery pack, and removing the voltage data within a preset voltage range to obtain the processed state data of each battery pack.
The preset temperature range and the preset voltage range may be determined by abnormal data determination conditions in actual applications. The above-described embodiment removes abnormal temperature data and abnormal voltage data. It should be noted that the removal of the abnormal temperature data and the abnormal voltage data is merely an example, and in practical applications, the abnormal data processing may include various types of abnormal data processing, such as abnormal current data, abnormal internal resistance data, abnormal SOC data, and the like.
Correspondingly, when the battery monitoring cloud platform executes the step S502, the following steps are specifically executed: and carrying out fragment division on the processed state data of each battery pack at different time periods to obtain a plurality of data fragments of each battery pack.
After the battery monitoring cloud platform processes the abnormal data of the state data, the processed state data can be segmented, and the subsequent steps are executed. In this embodiment, the abnormal data processing achieves the effect of performing effective data cleaning on the acquired original state data in advance, and can improve the accuracy of quantifying the health state of the battery pack based on the processed state data in the later period.
In combination with all the above embodiments, there is provided a method for quantifying the state of health of a battery pack, as shown in fig. 9, the method comprising:
s601, acquiring state data of a plurality of battery packs.
S602, performs abnormal data processing on the state data of each battery pack, and obtains processed state data of each battery pack.
S603, segmenting the processed state data of each battery pack according to a preset segmentation rule to obtain a plurality of data segments of each battery pack; the division rule comprises division according to a preset time period and division according to different working modes.
And S604, respectively extracting the features of each data segment of each battery pack to obtain a plurality of feature parameters of each battery pack.
S605, an average value of the characteristic parameters of each type of each battery pack is determined.
S606, determining an arithmetic average of the characteristic parameters of each type according to the average of the characteristic parameters of each type of each battery pack.
S607, obtaining the dispersion of each type of characteristic parameter of each battery pack according to the average value of each type of characteristic parameter of each battery pack and the arithmetic average value of each type of characteristic parameter.
And S608, determining the weight coefficient of each type of characteristic parameter of each battery pack.
And S609, obtaining a quantized value of the health state of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack and the weight coefficient of each type of characteristic parameter of each battery pack.
For the detailed description of the above steps, reference is made to the foregoing contents, which are not repeated herein.
It should be understood that although the various steps in the flow charts of fig. 2-9 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-9 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 10, there is provided an apparatus for quantifying a health status, including:
an obtaining module 11, configured to obtain state data of a plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
an extraction module 12, configured to perform feature extraction on the state data of each battery pack to obtain a plurality of feature parameters of each battery pack;
a quantizing module 13, configured to quantize the state of health of each battery pack according to a plurality of characteristic parameters of each battery pack, so as to obtain a quantized value of the state of health of each battery pack.
In one embodiment, as shown in fig. 11, the quantization module 13 includes:
a determining unit 131, configured to determine a weighting factor of each type of characteristic parameter of each battery pack.
A quantizing unit 132, configured to quantize the state of health of each battery pack according to the characteristic parameter of each type of each battery pack and the weight coefficient of the characteristic parameter of each type of each battery pack, so as to obtain a quantized value of the state of health of each battery pack.
In one embodiment, as shown in fig. 12, the quantization unit 132 includes:
a first determining subunit 1321 configured to determine the dispersion of the characteristic parameters of the respective types of each of the battery packs.
A quantizing subunit 1322, configured to obtain a quantized value of the state of health of each of the battery packs according to the dispersion of the respective types of characteristic parameters of each of the battery packs and the weighting coefficients of the respective types of characteristic parameters of each of the battery packs.
In one embodiment, the first determining subunit 1321 is specifically configured to determine an average value of each type of characteristic parameter of each battery pack; determining an arithmetic mean value of the characteristic parameters of each type according to the mean value of the characteristic parameters of each type of each battery pack; and obtaining the dispersion of the characteristic parameters of each type of each battery pack according to the average value of the characteristic parameters of each type of each battery pack and the arithmetic average value of the characteristic parameters of each type.
In an embodiment, after the first determining subunit 1321, as shown in fig. 13, the method further includes:
a comparing subunit 1323, configured to compare the dispersion of each type of characteristic parameter of each battery pack with a preset dispersion threshold corresponding to each type of characteristic parameter;
a second determining subunit 1324, configured to determine that the battery pack is a faulty battery pack if there is at least one type of dispersion of the characteristic parameter in the battery pack that is greater than a corresponding preset dispersion threshold.
In one embodiment, the extracting module 12, as shown in fig. 14, includes:
the dividing unit 121 is configured to segment the state data of each battery pack according to a preset dividing rule to obtain a plurality of data segments of each battery pack; the division rules comprise division according to a preset time period and division according to different working modes;
an extracting unit 122, configured to perform feature extraction on each data segment of each battery pack to obtain a plurality of feature parameters of each battery pack.
In an embodiment, as shown in fig. 15, before the dividing unit 121, the method further includes:
a processing unit 123, configured to perform abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack;
correspondingly, the dividing unit 121 is specifically configured to perform segment division on the processed state data of each battery pack for different time periods to obtain a plurality of data segments of each battery pack.
In an embodiment, the processing unit 123 is specifically configured to screen temperature data from the state data of each battery pack, and remove the temperature data within a preset temperature range to obtain data to be processed of each battery pack; and/or screening out voltage data from the data to be processed of each battery pack, and removing the voltage data within a preset voltage range to obtain the processed state data of each battery pack.
In one embodiment, the operating modes include: any one of a rest operation mode, a discharge operation mode, and a charge operation mode.
In one embodiment, the state data of the battery pack includes at least two data of resistance of the battery pack, cell voltage of the battery pack, total voltage of the battery pack, temperature of the battery pack, state of charge of the battery pack, number of times of cyclic discharge of the battery pack, accumulated charge energy of the battery pack, accumulated discharge energy of the battery pack, accumulated rest energy of the battery pack, temperature of the battery pack, and capacity of the battery pack.
For the specific definition of the health status quantifying device, reference may be made to the above definition of the health status quantifying method, and details are not repeated here. The modules in the health status quantifying device can be wholly or partially implemented by software, hardware and a combination thereof. The modules may be embedded in hardware or independent of a processor in the server, or may be stored in a memory in the electronic device in software, so that the processor calls and executes operations corresponding to the modules.
Fig. 16 is a block diagram illustrating a battery monitoring cloud platform 1400, according to an example embodiment. Referring to fig. 16, battery monitoring cloud platform 1400 includes processing components 1420, which further include one or more processors, and memory resources, represented by memory 1422, for storing instructions or computer programs, such as applications, that are executable by processing components 1420. The application programs stored in memory 1422 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1420 is configured to execute instructions to perform the above-described method of charging a battery pack.
The battery monitoring cloud platform 1400 may also include a power component 1424 configured to perform power management of the device 1400, a wired or wireless network interface 1426 configured to connect the device 1400 to a network, and an input output (I/O) interface 1428. The battery monitoring cloud platform 1400 may operate based on an operating system stored in memory 1422, such as Window14 14ervm, mac O14 XTM, unixTM, linuxTM, freeB14DTM, or the like.
In an exemplary embodiment, a storage medium is also provided that includes instructions, such as the memory 1422 that includes instructions, that are executable by the processor of the server 1400 to perform the above-described methods. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program product is also provided, which, when executed by a processor, may carry out the above-mentioned method. The computer program product includes one or more computer instructions. When loaded and executed on a computer, may implement some or all of the above-described methods, in whole or in part, according to the procedures or functions described in the embodiments of the disclosure.
Illustratively, the embodiment of the application discloses TS1, a method for quantifying a health state, which is characterized by comprising the following steps:
acquiring state data of a plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
performing feature extraction on the state data of each battery pack to obtain a plurality of feature parameters of each battery pack;
and quantizing the state of health of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the state of health of each battery pack.
TS2, the method of TS1, wherein the quantifying the state of health of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantified value of the state of health of each battery pack includes:
determining the weight coefficient of each type of characteristic parameter of each battery pack;
and quantizing the health state of each battery pack according to the characteristic parameters of each type of each battery pack and the weight coefficients of the characteristic parameters of each type of each battery pack to obtain a quantized value of the health state of each battery pack.
TS3, the method according to TS2, wherein the quantizing the state of health of each battery pack according to the characteristic parameter of each type of each battery pack and the weighting coefficient of the characteristic parameter of each type of each battery pack to obtain the quantized value of the state of health of each battery pack includes:
determining dispersion of each type of characteristic parameter of each battery pack;
and obtaining a quantized value of the state of health of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack and the weight coefficient of each type of characteristic parameter of each battery pack.
TS4, the method as set forth in TS3, wherein the determining the dispersion of the characteristic parameters of the respective types of each of the battery packs includes:
determining an average value of each type of characteristic parameter of each battery pack;
determining an arithmetic mean value of the characteristic parameters of each type according to the mean value of the characteristic parameters of each type of each battery pack;
and obtaining the dispersion of the characteristic parameters of each type of each battery pack according to the average value of the characteristic parameters of each type of each battery pack and the arithmetic average value of the characteristic parameters of each type.
TS5 the method of TS3, wherein after determining the dispersion of the characteristic parameters of each type for each battery pack, the method further comprises:
comparing the dispersion of each type of characteristic parameter of each battery pack with a preset dispersion threshold corresponding to each type of characteristic parameter;
and if the dispersion of at least one type of characteristic parameter in the battery pack is greater than a corresponding preset dispersion threshold value, determining that the battery pack is a fault battery pack.
TS6, the method according to any one of TS1 to TS5, wherein the performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack includes:
segmenting the state data of each battery pack according to a preset segmentation rule to obtain a plurality of data segments of each battery pack; the division rules comprise division according to a preset time period and division according to different working modes;
and respectively extracting the characteristics of each data segment of each battery pack to obtain a plurality of characteristic parameters of each battery pack.
TS7, the method of TS6, wherein the method further comprises:
performing abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack;
the segmenting of the state data of each battery pack for different time periods to obtain a plurality of data segments of each battery pack comprises:
and carrying out fragment division on the processed state data of each battery pack at different time periods to obtain a plurality of data fragments of each battery pack.
TS8, the method according to TS7, wherein the performing abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack includes:
screening temperature data from the state data of each battery pack, and removing the temperature data within a preset temperature range to obtain data to be processed of each battery pack; and/or the presence of a gas in the atmosphere,
and screening out voltage data from the data to be processed of each battery pack, and removing the voltage data in a preset voltage range to obtain the processed state data of each battery pack.
TS9, the method according to any of TS1 to TS5, wherein the operating mode comprises: any one of a standing operation mode, a discharging operation mode, and a charging operation mode.
TS10 the method of TS1, wherein the state data of the battery pack includes at least two data of resistance of the battery pack, cell voltage of the battery pack, total voltage of the battery pack, temperature of the battery pack, state of charge of the battery pack, number of times of cyclic discharge of the battery pack, accumulated charge energy of the battery pack, accumulated discharge energy of the battery pack, accumulated rest energy of the battery pack, temperature of the battery pack, and capacity of the battery pack.
TS11, an apparatus for quantifying a health status, the apparatus comprising:
the acquisition module is used for acquiring the state data of the plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
the extraction module is used for performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack;
and the quantization module is used for quantizing the health state of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the health state of each battery pack.
TS12, the apparatus as in TS11, wherein the quantization module comprises:
and the determining unit is used for determining the weight coefficient of each type of characteristic parameter of each battery pack.
And the quantization unit is used for quantizing the health state of each battery pack according to the characteristic parameters of each type of each battery pack and the weight coefficients of the characteristic parameters of each type of each battery pack to obtain the quantized value of the health state of each battery pack.
TS13, the apparatus as set forth in TS12, wherein the quantization unit includes:
and a first determining subunit, configured to determine dispersion of the characteristic parameters of each type of each battery pack.
And the quantization subunit is used for obtaining a quantized value of the health state of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack and the weight coefficient of each type of characteristic parameter of each battery pack.
TS14, the apparatus as in TS13, wherein the first determining subunit is specifically configured to determine an average value of each type of characteristic parameter of each battery pack; determining an arithmetic mean value of the characteristic parameters of each type according to the mean value of the characteristic parameters of each type of each battery pack; and obtaining the dispersion of the characteristic parameters of each type of each battery pack according to the average value of the characteristic parameters of each type of each battery pack and the arithmetic average value of the characteristic parameters of each type.
TS15, the apparatus as set forth in TS13, wherein after the first determining subunit, the apparatus further comprises:
the comparison subunit is used for comparing the dispersion of each type of characteristic parameter of each battery pack with a preset dispersion threshold corresponding to each type of characteristic parameter;
and the second determining subunit is used for determining that the battery pack is a fault battery pack under the condition that the dispersion of at least one type of characteristic parameter in the battery pack is greater than a corresponding preset dispersion threshold value.
TS16, the apparatus according to any one of TS11 to TS15, wherein the extracting module comprises:
the dividing unit is used for carrying out fragment division on the state data of the battery packs according to a preset dividing rule to obtain a plurality of data fragments of each battery pack; the division rules comprise division according to a preset time period and division according to different working modes;
and the extraction unit is used for respectively extracting the characteristics of each data segment of each battery pack to obtain a plurality of characteristic parameters of each battery pack.
TS17, the apparatus of TS16, wherein the apparatus further comprises:
the processing unit is used for performing abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack;
correspondingly, the dividing unit is specifically configured to perform segment division on the processed state data of each battery pack at different time periods to obtain a plurality of data segments of each battery pack.
TS18, the device according to TS17, wherein the processing unit is specifically configured to screen temperature data from status data of each battery pack, and remove temperature data within a preset temperature range to obtain to-be-processed data of each battery pack; and/or screening out voltage data from the data to be processed of each battery pack, and removing the voltage data in a preset voltage range to obtain the processed state data of each battery pack.
TS19, the apparatus according to any one of TS11 to TS15, wherein the operation mode comprises: any one of a standing operation mode, a discharging operation mode, and a charging operation mode.
TS20, the apparatus as recited in TS11, wherein the state data of the battery pack includes at least two data of resistance of the battery pack, cell voltage of the battery pack, total voltage of the battery pack, temperature of the battery pack, state of charge of the battery pack, number of times of cyclic charging of the battery pack, number of times of cyclic discharging of the battery pack, accumulated charging energy of the battery pack, accumulated discharging energy of the battery pack, accumulated rest energy of the battery pack, temperature of the battery pack, and capacity of the battery pack.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases or other media used in the embodiments provided in the disclosure may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above embodiments only express several implementation manners of the embodiments of the present disclosure, and the descriptions thereof are specific and detailed, but should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, variations and modifications can be made without departing from the concept of the embodiments of the present disclosure, and these are all within the scope of the embodiments of the present disclosure. Therefore, the protection scope of the patent of the embodiment of the present disclosure should be subject to the appended claims.
Claims (10)
1. A method for quantifying a health status, the method comprising:
acquiring state data of a plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
performing feature extraction on the state data of each battery pack to obtain a plurality of feature parameters of each battery pack;
and quantizing the state of health of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the state of health of each battery pack.
2. The method of claim 1, wherein said quantifying the state of health of each of the battery packs according to a plurality of characteristic parameters of each of the battery packs to obtain a quantified value of the state of health of each of the battery packs comprises:
determining the weight coefficient of each type of characteristic parameter of each battery pack;
and quantizing the health state of each battery pack according to the characteristic parameters of each type of each battery pack and the weight coefficients of the characteristic parameters of each type of each battery pack to obtain quantized values of the health state of each battery pack.
3. The method of claim 2, wherein the quantifying the state of health of each battery pack according to the type of characteristic parameter of each battery pack and the weight coefficient of the type of characteristic parameter of each battery pack to obtain the quantified value of the state of health of each battery pack comprises:
determining dispersion of each type of characteristic parameter of each battery pack;
and obtaining a quantized value of the state of health of each battery pack according to the dispersion of each type of characteristic parameter of each battery pack and the weight coefficient of each type of characteristic parameter of each battery pack.
4. The method of claim 3, wherein said determining the dispersion of the respective types of characteristic parameters of each of the battery packs comprises:
determining an average value of each type of characteristic parameter of each battery pack;
determining an arithmetic mean value of the characteristic parameters of each type according to the mean value of the characteristic parameters of each type of each battery pack;
and obtaining the dispersion of the characteristic parameters of each type of each battery pack according to the average value of the characteristic parameters of each type of each battery pack and the arithmetic average value of the characteristic parameters of each type.
5. The method according to claim 3, wherein after the determining the dispersion of the respective types of characteristic parameters of each of the battery packs, the method further comprises:
comparing the dispersion of each type of characteristic parameter of each battery pack with a preset dispersion threshold corresponding to each type of characteristic parameter;
and if the dispersion of the characteristic parameters of at least one type in the battery pack is greater than the corresponding preset dispersion threshold, determining that the battery pack is a fault battery pack.
6. The method according to any one of claims 1 to 5, wherein the performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack comprises:
segmenting the state data of each battery pack according to a preset segmentation rule to obtain a plurality of data segments of each battery pack; the division rules comprise division according to a preset time period and division according to different working modes;
and respectively extracting the characteristics of each data segment of each battery pack to obtain a plurality of characteristic parameters of each battery pack.
7. The method of claim 6, further comprising:
performing abnormal data processing on the state data of each battery pack to obtain processed state data of each battery pack;
the segmenting of the state data of each battery pack for different time periods to obtain a plurality of data segments of each battery pack comprises:
and carrying out fragment division on the processed state data of each battery pack at different time periods to obtain a plurality of data fragments of each battery pack.
8. An apparatus for quantifying a health status, the apparatus comprising:
the acquisition module is used for acquiring the state data of the plurality of battery packs; the state data comprises different types of state data of the battery pack in different working modes;
the extraction module is used for performing feature extraction on the state data of the battery packs to obtain a plurality of feature parameters of each battery pack;
and the quantization module is used for quantizing the health state of each battery pack according to a plurality of characteristic parameters of each battery pack to obtain a quantized value of the health state of each battery pack.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 7.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 7 when executed by a processor.
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