CN112526378B - Battery inconsistency fault early warning method and device - Google Patents
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Abstract
The application discloses a battery inconsistency fault early warning method and equipment, which are characterized in that a battery charge state interval is obtained; dividing the system into a plurality of segments, and solving health factors in each segment; then, health factors under a plurality of discharging working conditions are obtained, a time sequence is generated, accumulated processing is carried out on the time sequence to generate accumulated data and an accumulated sequence, and prediction data is generated according to the accumulated sequence; and setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data. By applying the technical scheme of the application, the variation trend of the battery inconsistency in the battery use (discharge) dimension is fully reflected; based on the accumulated sequence, the fluctuation of the data change is improved, and a basis is provided for the reliable prediction of the data trend; and carrying out predictive analysis based on the accumulated result, and realizing high-precision early warning of inconsistent faults in the next section.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery inconsistency fault early warning method and device.
Background
A Battery (Battery) refers to a device that converts chemical energy into electrical energy in a cup, tank, or other container or portion of a space of a composite container that contains an electrolyte solution and metal electrodes to generate an electrical current. New energy automobiles are used as an emerging automobile type, the main source of energy is a power battery, and the inconsistency of the power battery affects the service life of the battery and the running safety of the vehicle. The performance of the battery is often determined by the worst cell, and the cell with poor consistency can more easily reach the charge-discharge cut-off voltage, resulting in the severe limitation of the use of the entire battery. The inconsistency of the batteries can trigger the protection of the batteries by the vehicle-mounted control equipment such as a battery management system and a whole vehicle controller, so that the use of the vehicle is affected; the severe case battery power "breaks" causing the vehicle braking system to weaken or the drive system to fail, with an uncontrolled personnel safety risk.
Disclosure of Invention
Therefore, the invention aims to provide a battery inconsistency fault early warning method and device, which are used for realizing early warning of battery inconsistency, so that a user has enough time to repair or replace the battery.
Based on the above object, in one aspect, the present invention provides a battery inconsistency fault early warning method, including:
Acquiring a charge state interval under a battery discharging working condition;
Dividing the charge state interval into a plurality of segments, acquiring effective data of the battery in the segments, and determining the number of the effective data of the battery; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; generating health factors according to the number of the effective data of the battery and the number of the screening data;
Acquiring the health factors under a plurality of discharging working conditions, generating a time sequence of the health factors, performing accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generating prediction data according to the accumulated sequence;
and setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
In some embodiments, the dividing the state of charge interval into a plurality of segments, in particular:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
In some embodiments, the screening the battery effective data according to a preset safety setting value, obtaining screening data, and determining the number of the screening data specifically includes:
the effective data of the battery at least comprises single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
In some embodiments, the generating a health factor according to the number of the battery effective data and the number of the screening data specifically includes:
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0.
In some embodiments, the performing the accumulating process on the time sequence to generate accumulated data and an accumulated sequence specifically includes:
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; The total number of state of charge segments over a plurality of discharge conditions.
In some embodiments, the generating the prediction data according to the accumulated sequence specifically includes:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
In some embodiments, the performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data specifically includes:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
In some embodiments, the obtaining the predicted health factor according to the predicted data is specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
On the other hand, the invention also provides a battery inconsistency fault early warning device, which comprises:
The acquisition module is used for acquiring a charge state interval under a battery discharging working condition;
The generation module is used for dividing the charge state interval into a plurality of segments, acquiring the effective data of the battery in the segments and determining the number of the effective data of the battery; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; generating health factors according to the number of the effective data of the battery and the number of the screening data;
The accumulation module is used for acquiring the health factors under a plurality of discharge working conditions, generating a time sequence of the health factors, carrying out accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generating prediction data according to the accumulated sequence;
And the early warning module is used for setting a safety threshold and carrying out fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
In some embodiments, the generating module divides the state of charge interval into a plurality of segments, in particular:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
In some embodiments, the generating module screens the battery effective data according to a preset safety setting value to obtain screening data, and determines the number of the screening data, specifically:
the effective data of the battery at least comprises single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
In some embodiments, the generating module generates the health factor according to the number of the battery effective data and the number of the screening data, specifically:
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0.
In some embodiments, the accumulating module performs accumulating processing on the time sequence to generate accumulated data and an accumulated sequence, and specifically includes:
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; The total number of state of charge segments over a plurality of discharge conditions.
In some embodiments, the accumulating module generates the prediction data according to the accumulated sequence, specifically including:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
In some embodiments, the early warning module performs fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data, and specifically includes:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
In some embodiments, the early warning module obtains the predicted health factor according to the predicted data, specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
From the above, the method and the device for early warning the fault of the inconsistency of the battery provided by the application are characterized in that the battery charge state interval is obtained; dividing the system into a plurality of segments, and solving health factors in each segment; then, health factors under a plurality of discharging working conditions are obtained, a time sequence is generated, accumulated processing is carried out on the time sequence to generate accumulated data and an accumulated sequence, and prediction data is generated according to the accumulated sequence; and setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data. By applying the technical scheme of the application, the variation trend of the battery inconsistency in the battery use (discharge) dimension is fully reflected; based on the accumulated sequence, the fluctuation of the data change is improved, and a basis is provided for the reliable prediction of the data trend; and carrying out predictive analysis based on the accumulated result, and realizing high-precision early warning of inconsistent faults in the next section.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a battery inconsistency fault early warning method according to an embodiment of the present invention;
FIG. 2 is a graph showing the comparison between the degree of cell voltage inconsistency and the distribution of health factors according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an accumulated sequence after accumulation according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a battery inconsistency fault early warning device according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present disclosure pertains. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements, articles, or method steps preceding the word are included in the listed elements, articles, or method steps following the word, and equivalents thereof, without precluding other elements, articles, or method steps.
As described in the background art, the new energy automobile refers to an automobile with advanced technical principles, new technology and new structure, which is formed by adopting unconventional automobile fuel as a power source (or adopting conventional automobile fuel and adopting a novel vehicle-mounted power device) and integrating the advanced technology in the aspects of power control and driving of the automobile. The power battery as an energy source mainly comprises a lithium ion battery, a nickel-hydrogen battery, a fuel cell, a lead-acid battery and a super capacitor, wherein the super capacitor is mostly in the form of an auxiliary power source. The use of the power battery is related to the vehicle operating state. When in charging, the vehicle is positioned in a fixed place such as a parking space, a charging station and the like; during discharging, the vehicle is in a plurality of states such as lane movement or roadside temporary stop. The battery inconsistency in the discharging process is easier to influence the vehicle and passengers, the better mode is to predict the expansion trend of the inconsistency in a reasonable mode before triggering the fault protection of the battery inconsistency, carry out fault alarm in advance, and ensure that the vehicle has enough time to reach a maintenance place for fault treatment.
Aiming at the problems, the application constructs the health factor for reflecting the inconsistency of the battery based on the real-time data of the state of charge, and performs fault early warning based on the time sequence of the health factor.
The following describes in detail the technical solutions provided in the embodiments of the present specification with reference to the accompanying drawings.
As shown in fig. 1, a flow chart of a battery inconsistency fault early warning method according to the present embodiment is shown, and the method specifically includes the following steps:
and step 101, acquiring a charge state interval under a battery discharging working condition.
This step aims at acquiring the state of charge interval of the battery. The state of charge (SOC) is the ratio of the remaining capacity of the storage battery after the storage battery is used for a period of time or is left unused for a long time to the capacity of the full charge state of the storage battery, and is usually expressed as a percentage. The state of charge is an important indicator for measuring the use of the battery and is an indirect representation of the use time of the battery. The SOC of the power battery is mainly distributed within the range of 30% -100%, and the charge and discharge working conditions and the first and last positions can be accurately distinguished through the monotone relation and the magnitude of the SOC value. The state of charge is not directly available, and the magnitude of the SOC can be inferred through detection of parameters such as battery voltage, battery current, battery internal resistance, battery temperature, and the like. And battery non-uniformity is related to battery capacity, voltage. In a specific embodiment of the application, a power battery of a new energy automobile obtains sensor data such as single voltage, probe temperature, total voltage and total current and further obtains statistical data such as SOC through a CAN bus module of a BMS (battery management system).
In the use process of the battery, the application ranges of the SOC under different working conditions are different, and for a section [ SOC min,SOCmax ] with stable battery voltage characteristics of the power battery, the common setting is that SOC min＝40％,SOCmax =90%. The interval setting range can be adjusted according to different specific application scenarios, for example: SOC min was set to 35%, 40%, 45%, etc., and SOC max was set to 85%, 90%, 95%, etc.
Therefore, as long as the corresponding purposes can be achieved by different charge state acquisition methods and charge state interval setting methods, the protection scope of the invention is not affected by different methods.
102, Dividing the charge state interval into a plurality of segments, acquiring effective data of a battery in the segments, and determining the number of the effective data of the battery; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; and generating health factors according to the number of the battery effective data and the number of the screening data.
This step aims at dividing the state of charge interval into a plurality of segments and finding the health factor in each segment. There are many types of divisions of the state of charge interval, for example: the division into a plurality of segments at equal proportions, division according to the frequency of use (small interval of division of areas that are often used, large interval of division of areas that are not often used, etc.), division according to a specific setting, and the like.
The health factor is used for reflecting the inconsistency of the batteries, the effective data of the battery performance is obtained in each section, the number of the effective data is counted, the effective data is compared with a preset safety value, screening data is screened, the number of the screening data is counted, and then the health factor is obtained. Wherein the valid data is data that is effective in reflecting cell inconsistencies, such as: battery voltage, battery capacity, etc.
Meanwhile, the method for screening the effective data of the battery according to the preset safety setting value can be as follows: the difference value between the obtained voltage (or capacity) and the average value of all the voltages (or capacities) is compared with a safety set value, and when the difference value is larger than the safety set value, a piece of screening data is generated; the difference value between the obtained voltage (or capacity) and the average value of all the voltages (or capacities) is compared with a safety set value, and when the difference value is smaller than the safety set value, a piece of screening data is generated; the difference between the acquired voltage (or capacity) and the overall voltage (or capacity) variance is compared to a safety setting, and when the difference is greater than the safety setting, a piece of screening data is generated, and so on.
In addition, there are various ways to generate health factors based on the number of battery effective data and the number of screening data: obtaining the number of the effective data of the battery, screening the ratio of the number of the data, taking the logarithm of the comparison value to obtain the health factor, taking the thermal logarithm of the comparison value to obtain the health factor, obtaining the health factor by multiplying the logarithm of the ratio and the ratio, and the like, wherein the different obtaining methods can not influence the protection scope of the invention as long as the health factor has a specific mathematical relationship with the inconsistency degree of the battery.
Step 103, obtaining the health factors under a plurality of discharging working conditions, generating a time sequence of the health factors, performing accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generating prediction data according to the accumulated sequence.
The step aims at calculating a health factor formation sequence in a long time, so that the health factor and the time hook can further predict the next health factor. The time sequence is a set of health factors, and according to the previous steps, the SOC section of each discharging working condition of a plurality of discharging working conditions (such as historic N c discharging working conditions and the like) can be divided into a plurality of segments, and the health factors in each segment are calculated to form the time sequence. The accumulating process of the time sequence can be based on summation, product and the like, and the summation or the set of the products can be the elements in the whole time sequence or the elements with specific intervals in the time sequence. The accumulated data is the result of the accumulated processing of the ith element alignment in the time sequence, and the accumulated sequence is a set of the accumulated data.
In a specific embodiment, the time sequence { HI } is accumulated to obtain an accumulated sequence { HI '}, and accumulated data in { HI' }, such as the ith accumulated dataWhere HI j is the j-th element in HI.
In addition, prediction data is generated from the accumulated sequence. The prediction data may be generated from the whole accumulated sequence or may be generated from a part of the accumulated sequence. The generation process can be to predict by using regression analysis (such as linear regression, logistic regression, gaussian regression, etc.) or by using time series analysis (such as AR autoregressive model, MA moving average model, ARMA autoregressive moving average model, ARMA differential integration moving average autoregressive model, etc.), so as to obtain prediction data.
And 104, setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
The step aims to perform fault early warning of battery inconsistency by utilizing the parameters generated in the steps. Wherein the safety threshold is a parameter which is manually set and can characterize the inconsistency prediction standard. The problem of inconsistency of the battery can be reflected by comparing the safety threshold with the current health factor, for example: when the health factor is larger than the safety threshold, indicating that the current monomer has inconsistent faults, and performing fault early warning; when the health factor is smaller than the safety threshold, the current monomer has inconsistent faults, and fault early warning is carried out. Further, comparing the accumulated data with the predicted data can predict whether the next stage of the battery will have inconsistency, for example: when the accumulated data reflects the inconsistency of the battery, and when the accumulated data is larger than the predicted data, the fact that the inconsistency degree of the current monomer is not continuously deteriorated and fault early warning is not carried out is indicated; when the accumulated data reflects the consistency of the battery, when the accumulated data is smaller than the predicted data, the fact that the degree of the next inconsistency of the current monomer is not continuously deteriorated is indicated, and fault early warning is not carried out.
By applying the technical scheme of the application, the battery charge state interval is obtained; dividing the system into a plurality of segments, and solving health factors in each segment; then, health factors under a plurality of discharging working conditions are obtained, a time sequence is generated, accumulated processing is carried out on the time sequence to generate accumulated data and an accumulated sequence, and prediction data is generated according to the accumulated sequence; and setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data. By applying the technical scheme of the application, the variation trend of the battery inconsistency in the battery use (discharge) dimension is fully reflected; based on the accumulated sequence, the fluctuation of the data change is improved, and a basis is provided for the reliable prediction of the data trend; and carrying out predictive analysis based on the accumulated result, and realizing high-precision early warning of inconsistent faults in the next section.
In an alternative embodiment of the application, in order to make the state of charge segment more representative of each stage of the battery discharge condition, and to ensure that the discharge condition of the battery within the segment can meet the needs of user repair or replacement. The dividing the charge state interval into a plurality of segments specifically comprises:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
In an alternative embodiment of the application, the screening data is calculated for accuracy. Screening the effective data of the battery according to a preset safety setting value to obtain screening data, and determining the number of the screening data, wherein the method specifically comprises the following steps of:
the effective data of the battery at least comprises single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
In an alternative embodiment of the application, the health factor is made more sensitive to cell inconsistencies in order to accurately calculate the health factor. The health factor is generated according to the number of the effective data of the battery and the number of the screening data, and specifically comprises the following steps:
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0.
In an alternative embodiment of the application, the calculation method of the accumulated data is determined in order to more accurately represent the accumulated data. The step of performing accumulation processing on the time sequence to generate accumulated data and an accumulated sequence specifically includes:
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; The total number of state of charge segments over a plurality of discharge conditions.
In an alternative embodiment of the application, to make the predicted data more representative, meaningless parts of the accumulated sequence are removed and a model is determined to obtain the predicted data. The generating prediction data according to the accumulated sequence specifically includes:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
In an alternative embodiment of the application, in order to clarify the comparison mode of fault early warning, the method can complete fault early warning of battery inconsistency by utilizing simple comparison after obtaining each parameter. The fault early warning is performed according to the safety threshold, the health factor, the accumulated data and the predicted data, and specifically includes:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
In an alternative embodiment of the application, the way in which the health factor is obtained is explicitly predicted. The early warning module obtains a predicted health factor according to the predicted data, specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
In the application scene of the power battery of a specific new energy automobile, under different discharging working conditions, the using ranges of the battery SOC are different; and the battery SOC continuously changes, and the battery SOC needs to be divided at equal intervals to be quantized, and the specific steps are as follows:
1) Selecting a section with stable voltage characteristics of the battery as a maximum SOC use range [ SOC min,SOCmax ], wherein common SOC min＝40％,SOCmax =90%;
2) Defining the number N soc of equal interval segments in the maximum range of the SOC, wherein the number N soc +1 of interval points are respectively: For space width Ensuring that the vehicle can travel enough mileage to reach a maintenance site within the interval;
3) The maximum SOC usage range may be expressed as
4) The SOC usage range under actual discharge conditions may be mapped to a partial or full segment of the maximum usage range.
Under the current discharging working condition, the number of the effective data (including all the single voltage data) of the battery is received in the nth equal interval segment [ SOC n,SOCn+1 ] of the maximum use range of the assumed SOCThe value is the total number of the received data of the monomer i; for monomer number i, hereIn the bar data, the number of bars with the difference value between the voltage of the single body and the average single body voltage being larger than the safety setting value epsilon isThe health factor within the state of charge segment is:
Wherein the method comprises the steps of HI i =0; the value of epsilon is related to the battery performance parameter.
Fig. 2 is a schematic diagram showing the distribution of the degree of voltage inconsistency and the health factor of a certain type of battery # 1.
In the running process of the vehicle, parameters such as battery voltage, current and the like are changed severely, and the health factor calculated by the formula can reflect the inconsistent change trend, but the fluctuation of the value distribution is large, and the direct use of the health factor can cause large errors of the prediction result.
The influence of short data fluctuation on the whole change trend can be eliminated based on the health factor accumulation of the historical time, the change trend of inconsistency can be qualitatively judged, and the early warning target can be realized.
Selecting N c discharging working conditions, and supposing that the number of SOC segment intervals formed in the discharging working conditions isThe health factor time sequence { HI } can be constructed based on the equal interval SOC segments, and the sequence length isPerforming accumulation processing on { HI } to obtain an accumulation sequence { HI' }, wherein the accumulation processing process is as follows: ith to { HI' }Individual elementsHI i is { HI } jThe elements. The accumulated accumulation sequence is shown in fig. 3.
The fault early warning method based on the accumulated sequence after accumulation comprises the following steps:
1) Triggering a fault early warning method every time one HI is newly added;
2) After HI is added, a health factor time sequence { HI } and an accumulated sequence { HI' } are constructed based on N c discharge working conditions which have occurred recently, and N c takes values of [10,30]; the newly added health factor is recorded as
3) The closer the { HI' } is to the latest data, the more even the distribution is due to the cumulative effect; the data at the last 1/3-1/2 position of { HI '} can be extracted, and regression analysis (such as linear regression, logistic regression, gaussian regression, etc.), time series analysis (such as AR, MA, ARMA, ARIMA, etc.) and the like can be performed to predict the data under { HI' }Or directly at { HI' } last dataPerforming first-order derivation at the position, and predicting through a slope;
4) Fault early warning logic for the following HI data:
Determining a health factor safety threshold HI safe capable of reflecting inconsistent faults according to battery characteristics or battery fault data;
If it is Indicating that the current monomer has failed in inconsistency, whetherPredicting how the result is, and performing fault early warning;
If it is And is also provided withIndicating that the current monomer has no inconsistent fault and the inconsistent degree is not continuously deteriorated, and not carrying out fault early warning;
If it is And is also provided withIndicating that the current monomer has no inconsistent fault but the inconsistent degree is continuously deteriorated, and carrying out fault early warning according to a deterioration program; at this time, the liquid crystal display device,According toAnd carrying out early warning on the relation between the detected value and the HI safe.
By applying the scheme, a health factor definition form based on the SOC equidistant segmented interval is constructed, and the variation trend of the single voltage inconsistency in the battery use dimension is fully reflected; the historical data accumulation based on the health factor time sequence improves the fluctuation of the data change and provides a basis for the reliable prediction of the data trend; and finally, carrying out predictive analysis such as regression based on the accumulated health factor time sequence stable segment data, and realizing high-precision early warning of inconsistent faults in the next SOC segment interval.
Based on the same inventive concept, the embodiment of the invention also provides a battery inconsistency fault early warning device, as shown in fig. 4, comprising:
the acquiring module 401 acquires a state of charge interval under a battery discharging condition;
A generating module 402, configured to divide the state of charge interval into a plurality of segments, obtain effective battery data in the segments, and determine the number of effective battery data; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; generating health factors according to the number of the effective data of the battery and the number of the screening data;
The accumulation module 403 obtains the health factors under a plurality of discharging working conditions, generates a time sequence of the health factors, performs accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generates prediction data according to the accumulated sequence;
and the early warning module 404 is used for setting a safety threshold and carrying out fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
In an alternative embodiment, the generating module 402 divides the state of charge interval into a plurality of segments, specifically:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
In an alternative embodiment, the generating module 402 screens the battery valid data according to a preset safety setting value to obtain screening data, and determines the number of the screening data, specifically:
the effective data of the battery at least comprises single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
In an alternative embodiment, the generating module 402 generates the health factor according to the number of the battery valid data and the number of the screening data, specifically:
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0.
In an alternative embodiment, the accumulating module 403 performs accumulating processing on the time sequence to generate accumulated data and an accumulated sequence, which specifically includes:
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; The total number of state of charge segments over a plurality of discharge conditions.
In an alternative embodiment, the accumulating module 403 generates the prediction data according to the accumulated sequence, specifically includes:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
In an alternative embodiment, the early warning module 404 performs the fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data, and specifically includes:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
In an alternative embodiment, the early warning module 404 obtains the predicted health factor according to the predicted data, specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
The device of the foregoing embodiment is configured to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the invention. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (12)
1. The battery inconsistency fault early-warning method is characterized by comprising the following steps of:
Acquiring a charge state interval under a battery discharging working condition;
Dividing the charge state interval into a plurality of segments, acquiring effective data of the battery in the segments, and determining the number of the effective data of the battery; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; generating health factors according to the number of the effective data of the battery and the number of the screening data;
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0;
Acquiring the health factors under a plurality of discharging working conditions, generating a time sequence of the health factors, performing accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generating prediction data according to the accumulated sequence;
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; the total number of state of charge segments in the plurality of discharge conditions;
and setting a safety threshold, and performing fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
2. The method according to claim 1, characterized in that the state of charge interval is divided into a plurality of segments, in particular:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
3. The method according to claim 1, wherein the screening of the battery effective data according to a preset safety setting value, obtaining screening data, and determining the number of the screening data, specifically:
The battery effective data comprise single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
4. The method according to claim 1, wherein said generating prediction data from said accumulated sequence comprises:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
5. The method according to claim 1, wherein the performing fault pre-warning according to the safety threshold, the health factor, the accumulated data and the predicted data specifically comprises:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
6. The method according to claim 5, wherein the obtaining the predicted health factor from the predicted data is specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
7. A battery inconsistency fault early warning apparatus, comprising:
The acquisition module is used for acquiring a charge state interval under a battery discharging working condition;
The generation module is used for dividing the charge state interval into a plurality of segments, acquiring the effective data of the battery in the segments and determining the number of the effective data of the battery; screening the effective data of the battery according to a preset safety setting value, obtaining screening data, and determining the number of the screening data; generating health factors according to the number of the effective data of the battery and the number of the screening data;
The health factor is
Wherein HI i is the ith health factor; The number of the effective data of the battery; epsilon is a safety setting value, and the value is related to the battery performance parameter; To screen the number of data, when HI i =0;
The accumulation module is used for acquiring the health factors under a plurality of discharge working conditions, generating a time sequence of the health factors, carrying out accumulation processing on the time sequence to generate accumulated data and an accumulated sequence, and generating prediction data according to the accumulated sequence;
The accumulated data is
Wherein HI 'i is the ith element in the accumulated sequence { HI' }, which is the set of accumulated data; HI j is the jth element of the time series { HI } which is a set of health factors; the total number of state of charge segments in the plurality of discharge conditions;
And the early warning module is used for setting a safety threshold and carrying out fault early warning according to the safety threshold, the health factor, the accumulated data and the predicted data.
8. The device according to claim 7, wherein the generation module divides the state of charge interval into a plurality of segments, in particular:
setting the number of segments, and equally-spaced segmentation is carried out on the charge state interval according to the number of segments, so that the battery in the segments can be continuously discharged for a certain time.
9. The apparatus of claim 7, wherein the generating module screens the battery effective data according to a preset safety setting value to obtain screening data, and determines the number of the screening data, specifically:
The battery effective data comprise single voltage data, a difference value between the single voltage data and an average value of all the single voltage data is calculated, and the single voltage data is recorded as one piece of screening data when the difference value is larger than the safety setting value.
10. The apparatus of claim 7, wherein the accumulation module generates prediction data from the accumulation sequence, and specifically comprises:
The accumulated sequence is a set of the accumulated data, all the accumulated data in a specific section at the tail end of the accumulated sequence are obtained, and regression analysis and/or time sequence analysis are carried out on the accumulated data to obtain prediction data.
11. The device of claim 7, wherein the early warning module performs fault early warning according to the safety threshold, the health factor, the accumulated data, and the predicted data, specifically comprising:
when the health factor is not smaller than the safety threshold, performing fault early warning;
when the health factor is smaller than the safety threshold, comparing the accumulated data with the predicted data, and if the accumulated data is not smaller than the predicted data, not performing fault early warning; and if the accumulated data is smaller than the predicted data, obtaining a predicted health factor according to the predicted data, and if the predicted health factor is not smaller than the safety threshold, performing fault early warning.
12. The device according to claim 7, wherein the early warning module obtains a predicted health factor according to the predicted data, specifically:
The predicted health factor is
Wherein,Is a predictive health factor; is predictive data; Is accumulated data; The total number of state of charge segments over a plurality of discharge conditions.
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