CN112836174A

CN112836174A - AHP-based real-time charging safety evaluation method and storage medium

Info

Publication number: CN112836174A
Application number: CN202011622588.6A
Authority: CN
Inventors: 唐旭日; 李春喜; 魏高义
Original assignee: Shenzhen Jiamei Energy Technology Co ltd
Current assignee: Shenzhen Jiamei Energy Technology Co ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-05-25
Anticipated expiration: 2040-12-31
Also published as: CN112836174B

Abstract

The invention relates to the field of charging safety, in particular to an AHP-based real-time charging safety evaluation method and a storage medium. The method comprises the following steps: (1) acquiring real-time message data; (2) extracting characteristic data in the real-time message data; (3) extracting feature quantities of the feature data; (4) and fusing the characteristic quantity and the AHP matrix to evaluate the real-time charging safety. The method comprises the steps of extracting the characteristics of real-time message data of the charging pile to obtain characteristic data, judging the characteristic data to obtain corresponding characteristic quantities such as temperature characteristic quantity, monomer overvoltage characteristic quantity, characteristic quantity of SOC and charging quantity, current characteristic quantity, characteristic quantity of overlong charging time, capacity attenuation characteristic quantity and the like, fusing the characteristic quantities with AHP to judge the real-time charging safety of the battery, and carrying out active protection measures on a vehicle charged in real time according to an evaluation result.

Description

AHP-based real-time charging safety evaluation method and storage medium

Technical Field

The invention relates to the field of charging safety, in particular to an AHP-based real-time charging safety evaluation method and a storage medium.

Background

In recent years, with the continuous shortage of international energy supply, the continuous rise of crude oil price and the increasing rise of global environmental protection call, the technical research and development and the industrial development of new energy automobiles are more and more emphasized. Many developed countries around the world will ban the sale of fuel vehicles within 5 to 20 years, and many large vehicle enterprises will also stop producing fuel vehicles in 2019, and the development of new energy vehicles becomes a necessary choice in the automobile industry in the world today. The vast majority of new energy automobiles are electric automobiles, and the charging system provides guarantee for the supply of power energy of the electric automobiles. With the popularization of electric vehicles, the thermal runaway frequency of power batteries during charging frequently occurs. Therefore, the safety protection mechanism of the charging system has important practical significance.

The power supply equipment transmits the electric energy of an external power grid to the electric automobile through the cable assembly for energy supply, the energy in the whole process presents a one-way flow characteristic, and the power supply equipment relates to a plurality of systems such as a power supply network, a charging station and the electric automobile. The traditional charging safety protection mechanism is based on a Battery Management System (BMS) of the electric automobile, and the BMS is one of the most key automobile parts in the electric automobile and is used for monitoring the charging and discharging of a power battery pack in real time, estimating the SOC, managing the heat and the like. BMS plays decisive effect to the dynamic property of whole car, economic nature, continuation of the journey mileage, is the key of electric motor car safety protection mechanism.

Currently, BMS varies from brand to brand and even from vehicle to vehicle, with large differences in SOC estimation and thermal management capabilities. However, the current universal charging piles are all passively powered and are completely determined by the BMS of the electric automobile. As the power battery ages, various functions of the BMS may also degrade, particularly SOC estimation, which may lead to a sharp increase in safety concerns during charging. Therefore, the real-time charging safety evaluation method of the power battery is particularly important.

Disclosure of Invention

In order to solve the above problem, a first aspect of the present invention provides a real-time charging safety assessment method based on AHP, including:

(1) acquiring real-time message data;

(2) extracting characteristic data in the real-time message data;

(3) extracting feature quantities of the feature data;

(4) and fusing the characteristic quantity and the AHP matrix to evaluate the real-time charging safety.

As a preferred technical solution of the present invention, the characteristic data includes one or more of a maximum temperature, a minimum temperature, a maximum cell voltage, a charging current, an SOC and a charging amount, a charging duration, and historical charging data.

As a preferable aspect of the present invention, the characteristic amount includes one or more of a temperature characteristic amount, a cell overvoltage characteristic amount, a characteristic amount of SOC and a charge amount, a current characteristic amount, a characteristic amount of charge time excess, and a capacity fade characteristic amount.

In a preferred embodiment of the present invention, the temperature characteristic amount includes at least one of a maximum temperature characteristic amount and a temperature difference characteristic amount.

In a preferred aspect of the present invention, the characteristic amount of the SOC and the charged amount includes at least one of an SOC/charged amount inconsistency characteristic amount, an SOC reduction characteristic amount, and an SOC increase characteristic amount.

As a preferable aspect of the present invention, the current characteristic amount includes at least one of a current fluctuation characteristic amount and a characteristic amount of large SOC current.

As a preferred technical scheme of the invention, the AHP matrix adopts 1-9 proportion scale to assign value to the importance degree, and a judgment matrix A is constructed_n＝(a_ij)_n×n；

Wherein n is the number of relative importance comparisons between elements, a_ijIs the same layer element a_iAnd a_jScale of the importance of the elements relative to the previous layer, a_ij>0,

a_ii＝1。

As a preferred technical solution of the present invention, the AHP matrix performs consistency detection.

As a preferred technical scheme of the invention, the consistency detection formula is

A consistency test was performed where CR represents the consistency ratio, CI is the consistency index, and RI is the average random consistency index.

A second aspect of the present invention provides a computer-readable storage medium for storing a computer program for the real-time AHP-based charging safety assessment method as described above.

Compared with the prior art, the invention has the following beneficial effects: the method comprises the steps of extracting the characteristics of real-time message data of the charging pile to obtain characteristic data, judging the characteristic data to obtain corresponding characteristic quantities such as temperature characteristic quantity, monomer overvoltage characteristic quantity, characteristic quantity of SOC and charging quantity, current characteristic quantity, characteristic quantity of overlong charging time, capacity attenuation characteristic quantity and the like, fusing the characteristic quantities with AHP to judge the real-time charging safety of the battery, and carrying out active protection measures on a vehicle charged in real time according to an evaluation result.

Drawings

Fig. 1 is a flowchart of an AHP-based real-time charging safety assessment method.

FIG. 2 is an example of a SOC versus voltage curve.

Fig. 3 is an example of a two-dimensional graph obtained by clustering analysis of current fluctuation data by an SVM algorithm.

Detailed Description

The disclosure may be understood more readily by reference to the following detailed description of preferred embodiments of the invention and the examples included therein. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification, including definitions, will control.

When a parameter is expressed as a range, preferred range, or as a range defined by a list of upper preferable values and lower preferable values, this is to be understood as specifically disclosing all ranges formed from any pair of any upper range limit or preferred value and any lower range limit or preferred value, regardless of whether ranges are separately disclosed. For example, when a range of "1 to 5" is disclosed, the described range should be interpreted to include the ranges "1 to 4", "1 to 3", "1 to 2 and 4 to 5", "1 to 3 and 5", and the like. When a range of values is described herein, unless otherwise stated, the range is intended to include the endpoints thereof and all integers and fractions within the range.

The singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. "optional" or "any" means that the subsequently described event or events may or may not occur, and that the description includes instances where the event occurs and instances where it does not.

Approximating language, as used herein throughout the specification and claims, is intended to modify a quantity, such that the invention is not limited to the specific quantity, but includes portions that are literally received for modification without substantial change in the basic function to which the invention is related. Accordingly, the use of "about" to modify a numerical value means that the invention is not limited to the precise value. In some instances, the approximating language may correspond to the precision of an instrument for measuring the value. In the present description and claims, range limitations may be combined and/or interchanged, including all sub-ranges contained therein if not otherwise stated.

In addition, the indefinite articles "a" and "an" preceding an element or component of the invention are not intended to limit the number requirement (i.e., the number of occurrences) of the element or component. Thus, "a" or "an" should be read to include one or at least one, and the singular form of an element or component also includes the plural unless the stated number clearly indicates that the singular form is intended.

The present invention is illustrated by the following specific embodiments, but is not limited to the specific examples given below.

As shown in fig. 1, a first aspect of the present invention provides a real-time charging safety assessment method based on AHP, including:

(1) acquiring real-time message data;

(2) extracting characteristic data in the real-time message data;

(3) extracting feature quantities of the feature data;

Step (1)

The real-time message data can be obtained from a charging pile when charging is carried out by taking electricity as a medium of an energy source, such as an electric automobile, an electric bicycle, an electric tricycle, a mobile phone and the like, for example, an electric automobile, for example, a user of a new energy electric automobile can generate a communication message between the new energy automobile and the charging pile according with the communication protocol between a GBT 27930-charging 2015 electric automobile non-vehicle-mounted conductive charger and a battery management system when the charging pile of a company, namely, the real-time message data; the charging pile can screen part of communication messages according to an agreement signed by a company and a charging pile enterprise, the communication messages are sent to a cloud platform of the company and stored in a database, and the messages in the database are historical message data.

Step (2)

In one embodiment, the characteristic data of the present invention includes one or more of a maximum temperature, a minimum temperature, a maximum cell voltage, a charging current, a SOC and a charging amount, a charging time period, and historical charging data, and preferably, the characteristic data includes at least two of the maximum temperature, the minimum temperature, the maximum cell voltage, the charging current, the SOC and the charging amount, the charging time period, and the historical charging data; more preferably, the characteristic data includes at least four of maximum temperature, minimum temperature, maximum cell voltage, charging current, SOC and charging amount, charging time, and historical charging data; more preferably, the characteristic data includes a maximum temperature, a minimum temperature, a maximum cell voltage, a charging current, an SOC and a charging amount, a charging time period, and historical charging data. The historical charging data is historical charging message data of a target in a database, the SOC is a state of charge of the battery, also called residual electric quantity, and represents a ratio of the residual dischargeable electric quantity to the electric quantity in a full charging state of the battery after the battery is used for a period of time or is left unused for a long time, and the charging quantity is the degree of charging.

Step (3)

And (3) performing feature extraction on different feature data by adopting different methods to obtain feature quantities of different feature data, wherein feature values of the different feature quantities are all between [0,1 ]. In one embodiment, the characteristic amount of the present invention includes one or more of a temperature characteristic amount, a cell overvoltage characteristic amount, a characteristic amount of SOC and a charge amount, a current characteristic amount, a charge time excess characteristic amount, and a capacity fade characteristic amount.

Preferably, the temperature characteristic amount of the present invention includes at least one of a maximum temperature characteristic amount and a temperature difference characteristic amount.

More preferably, the characteristic value range of the highest temperature characteristic quantity in the present invention is [0,1], the corresponding temperature range is [40,55], and the characteristic value of the highest temperature characteristic quantity is the y-axis coordinate of the highest temperature fitting curve corresponding to the highest temperature. The maximum temperature fitting curve is a function fitting and normalization of an SEI film heat absorption rate-temperature curve with the temperature of 40-55 ℃ to obtain a function with the y-axis range of 0-1 and the x-axis range of 40-55.

The SEI film endothermic rate-temperature curve is a charging temperature curve and an endothermic rate curve of the SEI film during charging of the battery, and can be determined according to different batteries, for example, according to the endothermic rate curve provided in fig. 7 in "Thermal road defects of large format lithium ion battery using extended volume access measuring".

The temperature difference is the difference between the highest temperature and the lowest temperature, further preferably, the characteristic value range of the temperature difference characteristic quantity in the invention is [0,1], the corresponding temperature range of the temperature difference is [5,15], and the characteristic value of the temperature difference characteristic quantity is the y-axis coordinate of the temperature difference fitting curve corresponding to the temperature difference. The temperature difference fitting curve is a function obtained by subtracting 40 ℃ from the temperature of the x axis of the highest temperature fitting curve, wherein the range of the x axis is 5-15 ℃.

Still more preferably, the characteristic value of the cell overvoltage characteristic quantity in the present invention is 0 or 1, preferably, when the highest cell voltage is greater than the voltage corresponding to the SOC in the SOC-voltage curve, the characteristic value of the cell overvoltage characteristic quantity is 1, and when the highest cell voltage is less than the voltage corresponding to the SOC in the SOC-voltage curve, the characteristic value of the cell overvoltage characteristic quantity is 0; and the SOC in the voltage corresponding to the SOC in the SOC-voltage curve is the SOC in the characteristic data for ensuring data extraction in real time. The SOC-voltage curve is a curve of SOC and voltage during charging of the single battery, and may be determined according to different single batteries, and fig. 2 is a SOC-voltage curve of a single battery.

The unit cell generally refers to a unit secondary battery. The single storage battery is composed of electrodes and electrolyte, and forms a basic unit of the storage battery, and is called as a single storage battery.

In a preferred embodiment, the characteristic amount of SOC and the characteristic amount of charge in the present invention includes at least one of a characteristic amount of SOC not corresponding to a charged electric quantity, a characteristic amount of SOC decrease, and a characteristic amount of SOC increase.

In a more preferred embodiment, the characteristic value of the SOC according to the present invention, which is different from the characteristic value of the charge capacity, is 0 or 1; when the charging amount corresponding to the SOC is larger than the historical charging amount, the characteristic value of the characteristic amount that the SOC does not accord with the charging electric quantity is recorded as 1, otherwise, the characteristic value is recorded as 0. The charging amount corresponding to the SOC is the charging amount from the initial SOC to the current SOC in the real-time message data in the current charging, and if the charging amount is not enough, the average charging amount of the unit SOC is used for filling the vacancy.

The historical charging amount is the charging amount from the initial SOC to the current SOC in the historical message data; if the user is a new user, namely no historical charging record exists, the rated voltage V and the rated capacity C in the message are taken, and the product of the rated voltage V and the rated capacity C is the total charging quantityWherein if the initial SOC is recorded as a and the current SOC is recorded as b, the theoretical charging amount is

If the actual charge amount is larger than the historical charge amount, the characteristic value is 1; otherwise, it is 0.

In a further preferred embodiment, the characteristic value of the SOC reduction characteristic amount according to the present invention is 0 or 1, and when the SOC is reduced, the characteristic value of the SOC reduction characteristic amount is 1, and otherwise, it is 1. The SOC reduction is that the current SOC is reduced compared with the SOC at the last moment, wherein the real-time message data is generally recorded once every other moment, and the intervals of the other moments are determined according to different companies and charging pile protocols.

In a still further preferred embodiment, the characteristic value of the SOC sudden increase characteristic amount according to the present invention is [0,1 ]; when the SOC sudden increase amount is greater than 3% and not greater than 10%, the characteristic value of the SOC sudden increase characteristic amount is 0.1 × SOC sudden increase amount; when the SOC sudden increase amount is more than 10%, the characteristic value of the SOC sudden increase characteristic amount is 1; when the SOC sudden increase amount is less than 3%, the characteristic value of the SOC sudden increase characteristic amount is 0; the SOC abrupt increase is (current SOC — last time SOC)/current SOC × 100%.

In a still further preferred embodiment, the current characteristic amount according to the present invention includes at least one of a current fluctuation characteristic amount and a current large characteristic amount with a large SOC value.

In a further preferred embodiment, the characteristic values of the current fluctuation characteristic amount according to the present invention are 0, 0.25, 0.5 and 1, corresponding to normal current, slight fluctuation, moderate fluctuation and severe fluctuation, respectively, and are determined based on the mean and variance of the current fluctuation.

The characteristic value of the current fluctuation characteristic quantity is determined by performing cluster analysis on a plurality of pieces of current fluctuation data through an SVM algorithm. As an example of the current fluctuation feature quantity judgment, as shown in fig. 3, in order to manually label 200 pieces of current fluctuation data, including 4 levels of normal, mild, moderate and severe, an image obtained by clustering analysis using an SVM algorithm is obtained and is segmented, and the obtained function is:

wherein n is 0, which is normal current fluctuation, and the characteristic value of the current fluctuation characteristic quantity is 0; n is 1, the current fluctuation is slight, and the characteristic value of the current fluctuation characteristic quantity is 0.25; n is 2, the current fluctuation is moderate, and the characteristic value of the current fluctuation characteristic quantity is 0.5; and n is 3, the current fluctuation is serious, and the characteristic value of the current fluctuation characteristic quantity is 1. Wherein x is the number of current fluctuations and y is the variance of the current.

In a further preferred embodiment, the characteristic value of the characteristic quantity of the large-value current of the SOC of the present invention is 0 or 1, and when the SOC is greater than 95%, the current is large and is recorded as 1, otherwise, it is recorded as 0. The measurement standard with larger current can be determined according to an actual target battery, a charging pile and the like, and the method is not particularly limited.

In a further preferred embodiment, the characteristic value of the feature quantity of the charging time too long is 0 or 1, and when the charging time corresponding to the SOC is longer than the historical charging time, it is recorded as 1, otherwise it is recorded as 0. The charging time corresponding to the SOC is the charging time corresponding to the current SOC from the initial SOC in the real-time message data, and the historical charging time is the charging time corresponding to the current SOC from the initial SOC in the historical message data. For example, the starting SOC of the current charge is 20%, and the current charge is not fully charged after 60 minutes, but the starting SOC is also 20% in the history data, and the current charge is fully charged after only 50 minutes, and the characteristic value of this case is 1.

In a further preferred embodiment, the characteristic value of the capacity fading characteristic quantity according to the present invention is 0 or 1, and when the capacity is significantly faded, the characteristic value of the capacity fading characteristic quantity is 1, otherwise, the characteristic value is 0, or when the user is a new user, the characteristic value of the capacity fading characteristic quantity is 0. The capacity fading condition can estimate the total charge amount through historical charging data, then the total charge amount under the condition of this charging is estimated for judgment, if the capacity fades obviously, the characteristic value is 1, and the new user is 0.

Step (4)

The invention evaluates the real-time charging safety by fusing the characteristic quantity and the AHP matrix.

In the AHP matrix establishing process, the AHP matrix is obtained by carrying out comparison and evaluation on different abnormal types through experts, and the types and the number of the abnormal types are the same as the characteristic quantity extracted by the method. In one embodiment, the AHP matrix of the invention adopts 1-9 proportion scale to assign importance degree, and constructs a judgment matrix A_n＝(a_ij)_n×n；

a_ii1. n is the number of comparison of relative importance between elements, and n is the number A obtained by comparing two abnormal factors, namely characteristic quantities in the real-time charging process_n。

Preferably, the AHP matrix of the present invention performs consistency check.

More preferably, the consistency test according to the invention is formulated as

A consistency test was performed where CR represents the consistency ratio, CI is the consistency index, and RI is the average random consistency index. Further preferably, when CR < 0.1, the AHP matrix is acceptably consistent; when CR is more than or equal to 0.1, the AHP matrix needs to be corrected.

Still more preferably, in the consistency test according to the present invention, the matrix A is used_nEach row vector of the vector is geometrically averaged to obtain W_i'，

Then normalizing to obtain W_i'，

I.e. the weight vector.

To further illustrate the AHP matrix provided by the present invention, the highest temperature characteristic quantity Z is respectively calculated₁Characteristic quantity of temperature difference Z₂Characteristic quantity of overpressure of monomer Z₃Characteristic quantity Z of non-coincidence of SOC and charging electric quantity₄SOC lowering feature quantity Z₅SOC sudden increase feature quantity Z₆Characteristic quantity of current fluctuation Z₇Larger value of SOC and larger characteristic quantity Z of current₈Feature quantity Z of overlong charging time₉Characteristic quantity of capacity fade Z₁₀An example of an AHP matrix obtained by taking the feature quantities as an example and comparing the two feature quantities two by two according to a scale of 1 to 9 is shown in table 1. And the consistency judgment is carried out on the AHP matrix provided by the table 1, the CR of the AHP matrix is less than 0.1, and the consistency of the AHP matrix is acceptable.

Table 1 example of AHP matrix

The method and the device have the advantages that the real-time charging safety is evaluated by fusing the characteristic values and the AHP matrix, and the vehicle charged in real time is actively protected according to the evaluation result, so that the method and the device have the characteristics of real time, accuracy and quickness, and can avoid potential safety hazards.

A second aspect of the present invention provides a computer-readable storage medium for storing a computer program for executing the real-time AHP-based charging safety assessment method as described above.

The foregoing examples are merely illustrative and serve to explain some of the features of the method of the present invention. The appended claims are intended to claim as broad a scope as is contemplated, and the examples presented herein are merely illustrative of selected implementations in accordance with all possible combinations of examples. Accordingly, it is applicants' intention that the appended claims are not to be limited by the choice of examples illustrating features of the invention. Also, where numerical ranges are used in the claims, subranges therein are included, and variations in these ranges are also to be construed as possible being covered by the appended claims.

Claims

1. A real-time charging safety assessment method based on AHP is characterized by comprising the following steps:

(1) acquiring real-time message data;

(2) extracting characteristic data in the real-time message data;

(3) extracting feature quantities of the feature data;

2. The method of real-time safety assessment of charging of AHP of claim 1, wherein said characteristic data comprises one or more of maximum temperature, minimum temperature, maximum cell voltage, charging current, SOC and charge, length of charge, historical charging data.

3. The method of claim 2, wherein the characteristic quantities comprise one or more of a temperature characteristic quantity, a cell overvoltage characteristic quantity, a characteristic quantity of SOC and a charging quantity, a current characteristic quantity, a charging time too long characteristic quantity, and a capacity fade characteristic quantity.

4. The method for assessing the safety of the AHP during charging in real time according to claim 3, wherein the temperature characteristic value includes at least one of a maximum temperature characteristic value and a temperature difference characteristic value.

5. The method of claim 3, wherein the characteristic quantities of SOC and charging quantity comprise at least one of characteristic quantities of SOC and charging quantity not corresponding to each other, characteristic quantity of SOC reduction, and characteristic quantity of SOC sudden increase.

6. The method of claim 3, wherein the current characteristic comprises at least one of a current fluctuation characteristic and a current-to-large characteristic with a large SOC value.

7. The method for evaluating the safety of the AHP in real time for charging as claimed in any one of claims 1 to 6, wherein the AHP matrix adopts a 1-9 scale to assign importance degree, and a judgment matrix A is constructed_n＝(a_ij)_n×n；

Wherein n is the number of relative importance comparisons between elements, a_ij is a same layer element a_iAnd the scale of the importance of aj relative to the elements of the previous layer, a_ij>0,

a_ii＝1。

8. The method of claim 7, wherein the AHP matrix is configured to perform a consistency check.

9. The method for assessing the safety of the AHP in real-time charging as claimed in claim 8, wherein the consistency check is formulated as follows

10. A computer-readable storage medium for storing a computer program for executing the AHP-based real-time charging safety assessment method according to any one of claims 1 to 9.