CN110990770A - Fuzzy comprehensive fault evaluation method for power battery of electric vehicle - Google Patents

Fuzzy comprehensive fault evaluation method for power battery of electric vehicle Download PDF

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CN110990770A
CN110990770A CN201911196594.7A CN201911196594A CN110990770A CN 110990770 A CN110990770 A CN 110990770A CN 201911196594 A CN201911196594 A CN 201911196594A CN 110990770 A CN110990770 A CN 110990770A
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刘亚丽
李树鹏
胡晓辉
刘云
吕金炳
于光耀
陈培育
王天昊
崇志强
马世乾
刘瑜俊
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention relates to a fuzzy comprehensive evaluation method for faults of a power battery of an electric automobile, which is technically characterized by comprising the following steps: the method comprises the following steps: step 1, establishing a mathematical model for judging the battery fault category, and performing primary fuzzy comprehensive judgment on the mathematical model for judging the battery fault category; step 2, establishing a fuzzy mathematical model, performing second-stage comprehensive judgment on the fuzzy mathematical model, and judging whether the battery needs to be overhauled; and 3, obtaining a judgment result of whether the maintenance is carried out or not through the two-stage comprehensive judgment of the step 1 and the step 2. The evaluation result of the invention conforms to the actual field operation to the maximum extent, and the suggestion of whether the battery needs to be overhauled is directly given, so the invention has easy operation and strong practicability.

Description

Fuzzy comprehensive fault evaluation method for power battery of electric vehicle
Technical Field
The invention belongs to the technical field, relates to a fuzzy comprehensive evaluation method for power battery faults, and particularly relates to a fuzzy comprehensive evaluation method for power battery faults of an electric vehicle.
Background
At present, the service life of the power battery of the electric automobile is seriously influenced by the overcharge and the discharge of the power battery. In practical applications, selecting different charging modes according to the battery capacity limit is a necessary choice for prolonging the service life of the storage battery. The power battery pack of the electric automobile is generally formed by serially connecting a large number of monomers, and due to the difference of manufacturing processes of each monomer, inconsistency of internal resistance, voltage, capacity and temperature exists, unbalance in the charging and discharging process is easily caused, namely, a large-capacity monomer is discharged shallowly, a small-capacity monomer is discharged excessively, and serious damage is caused to the battery pack. The influence of the battery fault is manifold, and some indexes are abnormal possibly in the fault-free stage, so the fuzzy comprehensive evaluation method for the power battery is provided by the invention, and is beneficial to the comprehensive development of the electric automobile technology.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a fuzzy comprehensive evaluation method for the faults of the power battery of the electric automobile, which can comprehensively consider various influence factors, further obtain the comprehensive state level of the power battery of the electric automobile and provide a basis for operation, maintenance and overhaul of the power battery of the electric automobile.
The invention solves the practical problem by adopting the following technical scheme:
a fuzzy comprehensive fault evaluation method for a power battery of an electric vehicle comprises the following steps:
step 1, establishing a mathematical model for judging the battery fault category, and performing primary fuzzy comprehensive judgment on the mathematical model for judging the battery fault category;
step 2, establishing a fuzzy mathematical model, performing second-stage comprehensive judgment on the fuzzy mathematical model, and judging whether the battery needs to be overhauled;
and 3, obtaining a judgment result of whether the maintenance is carried out or not through the two-stage comprehensive judgment of the step 1 and the step 2.
Further, the specific steps of step 1 include:
(1) the mathematical model for judging the battery fault category is established as follows: b is A.R;
in the above formula, a is a fuzzy set, which is a factor set and represents weight distribution of each factor; b is also a fuzzy set which is a judgment set; r is a judgment matrix formed by judging all factor relations; r is an mxn order matrix called a judgment matrix;
(2) and performing primary comprehensive evaluation on the mathematical model for judging the battery fault category to obtain a comprehensive evaluation result of the battery fault degree.
Moreover, the specific steps of step 1 and step (2) include:
① fuzzy input set A is that A ═ XUL,XΔU,XnΔU,Xt,Xr),XULFuzzy input quantity, X, representing the peak value of the cell voltageΔUFuzzy input quantity, X, representing the degree of voltage deviation of the cellnΔUFuzzy input quantity, X, representing frequency of voltage deviationtFuzzy input quantity, X, representing temperature extremesrRepresenting an insulation resistance value;
②B＝(Y1,Y2,Y3,Y4)，Y1membership, Y, representing extreme differences in cell performance2Membership degree, Y, representing poor cell performance3Representing the general degree of membership, Y, of the cell performance4Representing the normal membership degree of the battery condition, wherein R is a fuzzy relation matrix of 5 multiplied by 4 orders;
③ the result of the comprehensive evaluation of the battery failure degree is obtained by the synthesis of A and R:
multiplying A by R matrix, comparing B ═ A · R, and judging Y in B1，Y2，Y3，Y4Size, if Y1The maximum, the battery performance is extremely poor, and the fault degree is high; if Y is2The maximum, the battery performance is poor, and the fault degree is higher; if Y is3The maximum is that the battery performance is general and the fault degree is low; if Y is4And if the voltage is maximum, the battery performance condition is normal and has no fault.
Further, the specific steps of step 2 include:
(1) establishing a fuzzy mathematical model C which is B S, and judging whether the battery needs to be overhauled;
in the above formula, C is a fuzzy output set, which provides a suggestion whether to repair the battery, and C ═ L, D, N; l represents the membership degree which needs to be maintained immediately, D represents the membership degree which needs to be maintained when the maintenance is suitable, and N represents the membership degree which does not need to be maintained; s is a 4 multiplied by 3 order fuzzy matrix;
(2) determining a fuzzy input set A;
(3) determining fuzzy matrixes R and S;
moreover, the step 2, the step (2), comprises the following specific steps:
① denotes the peak voltage X of the cellULAnd (3) determining fuzzy input quantity, and establishing a fuzzy membership function as follows:
Figure BDA0002294795730000031
② denotes the cell voltage offset XΔUDetermining fuzzy input quantity, and establishing a fuzzy membership function as follows:
Figure BDA0002294795730000032
③ denotes the frequency of voltage excursions XΔUFuzzy input quantity processing is carried out, and a fuzzy membership function is established as follows:
Figure BDA0002294795730000033
④ denotes extreme temperature XtFuzzy processing, establishing a fuzzy membership function as follows:
Figure BDA0002294795730000041
⑤ denotes the battery insulation value XrFuzzy processing, establishing a fuzzy membership function as follows:
Figure BDA0002294795730000042
by combining the above conditions, the fuzzy input set A is determined, and the normalization processing is performed on the input set.
Moreover, the specific steps of the step 2 and the step (3) comprise:
① according to expert experience, obtaining a fuzzy relation matrix R:
Figure BDA0002294795730000043
in the above formula, the first row of the first-level fuzzy matrix R represents the weight distribution of the cell voltage peak value on the influence of different fault degrees of the battery, and the second row of R represents the weight distribution of the cell voltage deviation degree on the influence of the battery performance. The third row of R represents the weight assignment of the voltage offset frequency effect on the insulation performance. The fourth row of R represents the assignment of weights of temperature extremes on battery performance. The fifth row of R represents the weight assignment of battery insulation to the impact of battery performance.
② according to the meaning of S matrix and expert experience, obtaining fuzzy relation matrix S:
Figure BDA0002294795730000051
the first row of the fuzzy relation matrix S represents the weight distribution of the battery performance to the overhaul recommendation when the battery performance is extremely poor, the second row represents the weight distribution of the battery performance poor to the overhaul recommendation, the third row represents the weight distribution of the battery performance general to the overhaul recommendation, and the fourth row represents the weight distribution of the battery performance normal to the overhaul recommendation.
The invention has the advantages and beneficial effects that:
according to the fuzzy comprehensive evaluation method for the faults of the power battery of the electric automobile, provided by the invention, a plurality of fault factors are comprehensively considered, the influence weight of each factor on the fault degree is obtained through a fuzzy comprehensive evaluation method, and the expert experience data and the field operation accumulated data are utilized, so that the evaluation result is in line with the field operation reality to the maximum extent, the suggestion whether the battery needs to be overhauled is directly given, and the fuzzy comprehensive evaluation method is easy to operate and high in practicability.
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FIG. 1 is a process flow diagram of the present invention.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a fuzzy comprehensive evaluation method for faults of a power battery of an electric vehicle is shown in figure 1 and comprises the following steps:
step 1, establishing a mathematical model for judging the battery fault category, and performing primary fuzzy comprehensive judgment on the mathematical model for judging the battery fault category;
the specific steps of the step 1 comprise:
(1) the mathematical model for judging the battery fault category is established as follows: b is A.R;
in the above formula, a is a fuzzy set, which is a factor set and represents weight distribution of each factor; b is also a fuzzy set which is a judgment set; r is a judgment matrix formed by judging all factor relations; r is an mxn order matrix called a judgment matrix;
(2) performing first-level comprehensive evaluation on the mathematical model for judging the battery fault category to obtain a comprehensive evaluation result of the battery fault degree;
the step 1, the step (2) comprises the following specific steps:
① fuzzy input set A is that A ═ XUL,XΔU,XnΔU,Xt,Xr),XULFuzzy input quantity, X, representing the peak value of the cell voltageΔUFuzzy input quantity, X, representing the degree of voltage deviation of the cellnΔUFuzzy input quantity, X, representing frequency of voltage deviationtFuzzy input quantity, X, representing temperature extremesrRepresenting an insulation resistance value;
②B＝(Y1,Y2,Y3,Y4)，Y1membership, Y, representing extreme differences in cell performance2Membership degree, Y, representing poor cell performance3Representing the general degree of membership, Y, of the cell performance4Representing the degree of membership of the battery condition, R is a fuzzy relation matrix of 5 multiplied by 4 orders:
③ the result of comprehensive evaluation of the degree of battery failure is obtained by the synthesis of A and R.
Multiplying A by R matrix, comparing B ═ A · R, and judging Y in B1，Y2，Y3，Y4Size, if Y1The maximum, the battery performance is extremely poor, and the fault degree is high; if Y is2The maximum, the battery performance is poor, and the fault degree is higher; if Y is3The maximum is that the battery performance is general and the fault degree is low; if Y is4And if the voltage is maximum, the battery performance condition is normal and has no fault.
Step 2, establishing a fuzzy mathematical model, performing second-stage comprehensive judgment on the fuzzy mathematical model, and judging whether the battery needs to be overhauled;
after the first-level comprehensive evaluation is carried out on each index set obtained by detecting the power battery of the electric automobile, the relevant conditions such as the battery fault degree and the like can be obtained. But only the fault degree of the battery is not enough, and a more intuitive suggestion whether to need to be overhauled or not is further obtained by adopting the comprehensive judgment of the second-stage fuzzification.
In this embodiment, the specific steps of step 2 include:
(1) establishing a fuzzy mathematical model C which is B S, and judging whether the battery needs to be overhauled;
in the above formula, C is a fuzzy output set, which provides a suggestion whether to repair the battery, and C ═ L, D, N; l represents the membership degree which needs to be maintained immediately, D represents the membership degree which needs to be maintained when the maintenance is suitable, and N represents the membership degree which does not need to be maintained; s is a 4 multiplied by 3 order fuzzy matrix;
(2) determining a set of fuzzy inputs A
The determination of the input fuzzy set A is to perform fuzzy mathematical processing on each fuzzy input quantity according to the characteristics of the fuzzy input quantity. I.e. to establish their fuzzy membership functions, respectively.
The step 2, the step (2), comprises the following specific steps:
① denotes the peak voltage X of the cellULThe fuzzy input quantity is determined, the unit voltage of the lithium iron phosphate battery for the electric automobile is generally between 2.5 and 3.7V in the normal charging and discharging process, and when the unit voltage exceeds 3.7V and even reaches more than 4.0V, the battery can have serious faults at any time. The fuzzy membership functions are established as follows:
Figure BDA0002294795730000071
② denotes the cell voltage offset XΔUThe fuzzy input quantity is determined, the monomer voltage deviation degree has a relatively obvious effect in the fault judgment of the lithium iron phosphate battery for the electric automobile, and a fuzzy membership function is established as follows:
Figure BDA0002294795730000072
③ denotes the frequency of voltage excursions XΔUThe fuzzy input quantity is processed, and the monomer voltage deviation degree X with the voltage deviation degree exceeding 100mV can be seen from the formulaΔUThe maximum voltage deviation degree, namely, the single power battery of the electric automobile with the voltage deviation degree exceeding 100mV plays a more critical role in fault judgment, and a fuzzy membership function is establishedThe numbers are as follows:
Figure BDA0002294795730000081
④ denotes extreme temperature XtFuzzy processing, as can be seen from the foregoing characteristic analysis of the lithium ion battery, the performance of the lithium ion battery is greatly affected by the working temperature of the battery, the working range of a general lithium iron phosphate battery is 0 to 55 ℃, the performance of the lithium iron phosphate battery outside the range is affected, and a fuzzy membership function is established as follows:
Figure BDA0002294795730000082
⑤ denotes the battery insulation value XrFuzzy treatment, insulation resistance value R of lithium ion batteryrGenerally, the insulation value is about several hundred megaohms, but the measured insulation value is generally influenced by external factors, and from the practical application point of view, the insulation value of the battery is generally considered to be normal when the insulation value is more than 50 megaohms, and is considered to be abnormal when the insulation value is less than 50 megaohms, and at the moment, the battery needs to be retested or repaired as required. The fuzzy membership function is established as follows:
Figure BDA0002294795730000083
by combining the above conditions, the fuzzy input set A is determined, and the normalization processing is performed on the input set.
(3) Determining blur matrices R and S
The step 2, the step (3) comprises the following specific steps:
① according to expert experience, obtaining a fuzzy relation matrix R:
Figure BDA0002294795730000091
in the above formula, the first row of the first-level fuzzy matrix R represents the weight distribution of the cell voltage peak value on the influence of different fault degrees of the battery, and the second row of R represents the weight distribution of the cell voltage deviation degree on the influence of the battery performance. The third row of R represents the weight assignment of the voltage offset frequency effect on the insulation performance. The fourth row of R represents the assignment of weights of temperature extremes on battery performance. The fifth row of R represents the weight assignment of battery insulation to the impact of battery performance.
② according to the meaning of S matrix and expert experience, obtaining fuzzy relation matrix S:
Figure BDA0002294795730000092
the first row of the fuzzy relation matrix S represents the weight distribution of the battery performance to the overhaul recommendation when the battery performance is extremely poor, the second row represents the weight distribution of the battery performance poor to the overhaul recommendation, the third row represents the weight distribution of the battery performance general to the overhaul recommendation, and the fourth row represents the weight distribution of the battery performance normal to the overhaul recommendation.
And step 3, through two-stage comprehensive judgment, the condition that B is A.R and C is B S can be directly provided with a suggestion whether to overhaul, and the realization method is simple, understandable and operable.
In the embodiment, the online comprehensive performance of the battery can be evaluated in real time by the multi-stage fuzzy comprehensive evaluation mathematical model established in the foregoing. Part of data is extracted from the battery monitoring of actual operation, and a normalized fuzzy input set is obtained according to the processing method of the fuzzy input set A.
TABLE 1 fuzzy comprehensive evaluation and diagnosis model example for power battery fault
Figure BDA0002294795730000101
Obtaining a fuzzy relation matrix according to expert experience:
Figure BDA0002294795730000102
the first row represents the weight distribution of the influence of the peak value of the cell voltage on different fault degrees of the battery, and the second row represents the weight distribution of the influence of the deviation degree of the cell voltage on the performance of the battery. The third row shows the weight assignment of the effect of the frequency of the voltage excursion on the insulation performance. The fourth row represents the assignment of weights to the temperature extremes' effect on battery performance. The fifth row represents the weight assignment of battery insulation to the battery performance impact.
The four columns in turn represent the extreme difference, the poor, the normal and the normal.
And obtaining a judgment matrix according to the condition that B is A and R:
Figure BDA0002294795730000103
according to the meaning of the S matrix and expert experience, the following results are obtained:
Figure BDA0002294795730000104
the first row represents the weight assignment to service recommendations when cell performance is very poor, the second row represents the weight assignment to service recommendations for poor cell performance, the third row represents the weight assignment to service recommendations for normal cell performance, and the fourth row represents the weight assignment to service recommendations for normal cell performance.
The four rows represent immediate maintenance in sequence, and the maintenance is required without maintenance.
And obtaining a secondary fuzzy comprehensive evaluation result according to the condition that C is B S:
Figure BDA0002294795730000111
and obtaining the extremely poor performance of the first group of batteries according to the maximum membership principle, and immediately overhauling the first group of batteries, wherein the second group of batteries have general performance and are to be overhauled, and the third group of batteries have normal performance and do not need to be overhauled.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.

Claims (6)

1. A fuzzy comprehensive fault evaluation method for a power battery of an electric vehicle is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a mathematical model for judging the battery fault category, and performing primary fuzzy comprehensive judgment on the mathematical model for judging the battery fault category;
step 2, establishing a fuzzy mathematical model, performing second-stage comprehensive judgment on the fuzzy mathematical model, and judging whether the battery needs to be overhauled;
and 3, obtaining a judgment result of whether the maintenance is carried out or not through the two-stage comprehensive judgment of the step 1 and the step 2.
2. The fuzzy comprehensive evaluation method for the faults of the power batteries of the electric vehicles according to claim 1, characterized in that: the specific steps of the step 1 comprise:
(1) the mathematical model for judging the battery fault category is established as follows: b is A.R;
in the above formula, a is a fuzzy set, which is a factor set and represents weight distribution of each factor; b is also a fuzzy set which is a judgment set; r is a judgment matrix formed by judging all factor relations; r is an mxn order matrix called a judgment matrix;
(2) and performing primary comprehensive evaluation on the mathematical model for judging the battery fault category to obtain a comprehensive evaluation result of the battery fault degree.
3. The fuzzy comprehensive evaluation method for the faults of the power batteries of the electric vehicles according to claim 2, characterized in that: the step 1, the step (2) comprises the following specific steps:
① fuzzy input set A is that A ═ XUL,XΔU,XnΔU,Xt,Xr),XULFuzzy input quantity, X, representing the peak value of the cell voltageΔUFuzzy input quantity, X, representing the degree of voltage deviation of the cellnΔUFuzzy input quantity, X, representing frequency of voltage deviationtFuzzy input quantity, X, representing temperature extremesrRepresenting an insulation resistance value;
②B＝(Y1,Y2,Y3,Y4)，Y1membership, Y, representing extreme differences in cell performance2Membership degree, Y, representing poor cell performance3Representing the general degree of membership, Y, of the cell performance4Representing the degree of membership of the battery condition, R is a fuzzy relation matrix of 5 multiplied by 4 orders:
③ the result of the comprehensive evaluation of the battery failure degree is obtained by the synthesis of A and R:
multiplying A by R matrix, comparing B ═ A · R, and judging Y in B1，Y2，Y3，Y4Size, if Y1The maximum, the battery performance is extremely poor, and the fault degree is high; if Y is2The maximum, the battery performance is poor, and the fault degree is higher; if Y is3The maximum is that the battery performance is general and the fault degree is low; if Y is4And if the voltage is maximum, the battery performance condition is normal and has no fault.
4. The fuzzy comprehensive evaluation method for the faults of the power batteries of the electric vehicles according to claim 1, characterized in that: the specific steps of the step 2 comprise:
(1) establishing a fuzzy mathematical model C which is B S, and judging whether the battery needs to be overhauled;
in the above formula, C is a fuzzy output set, which provides a suggestion whether to repair the battery, and C ═ L, D, N; l represents the membership degree which needs to be maintained immediately, D represents the membership degree which needs to be maintained when the maintenance is suitable, and N represents the membership degree which does not need to be maintained; s is a 4 multiplied by 3 order fuzzy matrix;
(2) determining a fuzzy input set A;
(3) blur matrices R and S are determined.
5. The fuzzy comprehensive evaluation method for the faults of the power batteries of the electric vehicles according to claim 4, characterized in that: the step 2, the step (2), comprises the following specific steps:
① denotes the peak voltage X of the cellULAnd (3) determining fuzzy input quantity, and establishing a fuzzy membership function as follows:
Figure FDA0002294795720000031
② denotes the cell voltage offset XΔUDetermining fuzzy input quantity, and establishing a fuzzy membership function as follows:
Figure FDA0002294795720000032
③ denotes the frequency of voltage excursions XΔUFuzzy input quantity processing is carried out, and a fuzzy membership function is established as follows:
Figure FDA0002294795720000033
④ denotes extreme temperature XtFuzzy processing, establishing a fuzzy membership function as follows:
Figure FDA0002294795720000034
⑤ denotes the battery insulation value XrFuzzy processing, establishing a fuzzy membership function as follows:
Figure FDA0002294795720000035
by combining the above conditions, the fuzzy input set A is determined, and the normalization processing is performed on the input set.
6. The fuzzy comprehensive evaluation method for the faults of the power batteries of the electric vehicles according to claim 4, characterized in that: the step 2, the step (3) comprises the following specific steps:
① according to expert experience, obtaining a fuzzy relation matrix R:
Figure FDA0002294795720000041
in the above formula, the first row of the first-level fuzzy matrix R represents the weight distribution of the influence of the cell voltage peak value on different fault degrees of the battery, and the second row of the first-level fuzzy matrix R represents the weight distribution of the influence of the cell voltage offset degree on the battery performance; the third row of R represents the weight distribution of the voltage offset frequency effect on the insulation performance; the fourth row of R represents the weight assignment of temperature extremes to the battery performance impact; the fifth row of R represents the weight distribution of the battery insulation value on the battery performance;
② according to the meaning of S matrix and expert experience, obtaining fuzzy relation matrix S:
Figure FDA0002294795720000042
the first row of the fuzzy relation matrix S represents the weight distribution of the battery performance to the overhaul recommendation when the battery performance is extremely poor, the second row represents the weight distribution of the battery performance poor to the overhaul recommendation, the third row represents the weight distribution of the battery performance general to the overhaul recommendation, and the fourth row represents the weight distribution of the battery performance normal to the overhaul recommendation.
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