CN110116625B - Automobile storage battery fault monitoring method for electric control vehicle - Google Patents

Automobile storage battery fault monitoring method for electric control vehicle Download PDF

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CN110116625B
CN110116625B CN201910409050.8A CN201910409050A CN110116625B CN 110116625 B CN110116625 B CN 110116625B CN 201910409050 A CN201910409050 A CN 201910409050A CN 110116625 B CN110116625 B CN 110116625B
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storage battery
capacity coefficient
fault
power supply
discharge capacity
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CN110116625A (en
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李光林
孙福明
李刚
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Liangshan Hongfu Traffic Equipment Co ltd
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Liaoning University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
    • B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
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  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a fault monitoring method for an automobile storage battery of an electric control vehicle, which comprises the following steps: step one, reading state data of an ith storage battery in an automobile storage battery pack in a time period t, step two, dividing the time period t into n time units, and calculating a state data mean value in each time unit; step three, calculating the discharge capacity coefficient of the ith storage battery according to the state data mean value; calculating the power supply capacity coefficient of the ith storage battery according to the state data mean value; inputting the discharge capacity coefficient and the power supply capacity coefficient into a fuzzy controller to obtain a vector group representing the fault category; and the vector group representing the fault category is output as a fault diagnosis answer, and the discharge capacity coefficient and the power supply capacity coefficient of each storage battery are calculated according to the state data mean value of the automobile storage battery and input into the fuzzy controller to obtain the fault degree of each storage battery.

Description

Automobile storage battery fault monitoring method for electric control vehicle
Technical Field
The invention relates to the field of automobile maintenance, in particular to an automobile storage battery fault monitoring method for an electric control vehicle.
Background
The storage battery is a chemical power supply, when charging, the chemical reaction in the storage battery converts external electric energy into chemical energy for storage, when using electricity, the stored chemical energy is converted into electric energy through the chemical reaction, and the electric energy is output to electric equipment.
Lead-acid batteries for automobiles are classified into general type, dry charge type, wet charge type and maintenance-free type.
Maintenance-free type battery is at the reasonable use of car process, do not need to add distilled water, electrolyte is once annotated by the manufacture factory, and sealed in the casing, consequently, electrolyte can not reveal, can not corrode terminal and organism, in use need not to add distilled water and can supply electrolyte to adjust liquid level height, need not maintenance and maintenance consequently be the battery type of universal adoption among the electric new energy automobile, but electric automobile battery contains a plurality of battery cell usually, single battery cell damages, influence the dynamic behavior of whole car, the threshold value of monoblock automobile battery is set for to conventional electric automobile, in case the system operation breaks down, can only show with single signal alarm mode, can not detail to the battery cell that breaks down.
Disclosure of Invention
The invention designs and develops an automobile storage battery fault monitoring method for an electric control vehicle, which is characterized in that the discharge capacity coefficient and the power supply capacity coefficient of each storage battery are calculated according to the state data mean value of the automobile storage battery and are input into a fuzzy controller to obtain the fault degree of each storage battery.
The technical scheme provided by the invention is as follows:
an automotive battery fault monitoring method for an electronically controlled vehicle, comprising:
step one, reading state data of an ith storage battery in an automobile storage battery pack in a time period t, wherein the state data comprises: voltage of battery Ui(t) internal resistance of cell Ri(T) cell temperature Ti(t), discharge current Ii(t) height h of electrolyte in batteryi(t) amount of stored electricity Q of batteryi(t)；
Step two, dividing the time interval t into n time units, and calculating the average value of state data in each time unit;
step three, calculating the discharge capacity coefficient of the ith storage battery according to the state data mean value;
calculating the power supply capacity coefficient of the ith storage battery according to the state data mean value;
inputting the discharge capacity coefficient and the power supply capacity coefficient into a fuzzy controller to obtain a vector group representing the fault category; and
and outputting the vector group representing the fault category as a fault diagnosis answer.
Preferably, the storage battery discharge capacity coefficient calculation formula is:
Figure BDA0002062229010000021
wherein,
Figure BDA0002062229010000022
Fiis the discharge capacity coefficient of the i-th storage battery, fi jThe discharge capacity coefficient of the storage battery in the jth period of the ith storage battery,
Figure BDA0002062229010000023
is the average value of the voltage of the cells of the ith storage battery in the jth time period,
Figure BDA0002062229010000024
the average value of the internal resistance of the i storage batteries in the jth time interval,
Figure BDA0002062229010000025
is the average value of the discharge current of the ith storage battery in the jth time period,
Figure BDA0002062229010000026
the average value of the charge capacity of the ith storage battery in the jth time period is shown; for the gamma function, K is the scaling factor.
Preferably, the battery power supply capacity coefficient calculation formula is:
Figure BDA0002062229010000027
Figure BDA0002062229010000028
wherein G isiIs as followsThe power supply capacity coefficient of the i storage batteries,
Figure BDA0002062229010000031
l n is a Laguerre polynomial, and n is the number of time units.
Preferably, the fuzzy controller works as follows:
comparing the discharge capacity coefficient with a preset point discharge capacity coefficient to obtain a discharge capacity coefficient deviation signal, and comparing the power supply capacity coefficient with a preset power supply capacity coefficient to obtain a power supply capacity coefficient deviation signal;
carrying out differential calculation on the discharge capacity coefficient deviation signal to obtain a discharge capacity coefficient change rate signal, and carrying out differential calculation on the power supply capacity coefficient deviation signal to obtain a power supply capacity coefficient change rate signal;
and amplifying the discharge capacity coefficient change rate signal and the idle power supply capacity coefficient change rate signal, inputting the amplified signals into a fuzzy controller, and outputting the amplified signals as fault levels.
Preferably, the fuzzy set of the discharge capability coefficient signal and the preset-point discharge capability coefficient signal is: { NB, NM, NS, ZR, PS, PM, PB }, NB representing negative large, NM representing negative medium, NS representing negative small, ZR representing zero, PS representing positive small, PM representing positive medium, PB representing positive large, their domains of discourse are: { -6, -5, -4, -3, -2, -1,0,1,2,3,4,5,6}.
Preferably, the fuzzy set of fault classes is { D }0，D1，D2，D3}，D0Zero order fault, indicating normal cell operation, D1Indicating continued operation for a first order fault, D2For a secondary failure, indicating the need for repair, D3And the three-level fault indicates that the emergency stop is needed and the battery unit needs to be replaced.
7. The method of claim 6, wherein the membership function of the input variable of the fuzzy controller is a triangular membership function.
8. The automobile storage battery fault monitoring method for the electric control vehicle as claimed in claim 6, wherein the state data of the automobile storage battery pack is selected for a time period of 1h ≤ t ≤ 4 h;
wherein t is the time period and h is the hour.
9. The automotive battery failure monitoring method for the electrically controlled vehicle according to claim 8, characterized in that the time cell satisfies 80 ≦ n ≦ 100.
The invention has the advantages of
The invention designs and develops an automobile storage battery fault monitoring method for an electric control vehicle, which is characterized in that the discharge capacity coefficient and the power supply capacity coefficient of each storage battery are calculated according to the state data mean value of the automobile storage battery and input into a fuzzy controller to obtain the fault degree of each storage battery, so that the fault battery can be accurately positioned in time, and the stable running of the automobile is ensured.
According to the automobile storage battery fault monitoring method for the electric control vehicle, the fault degree is divided into four fault levels through the fuzzy controller, the zero-level fault indicates that the battery runs normally, the first-level fault indicates that the second-level fault can continue to run, indicates that the battery needs to be repaired, the third-level fault indicates that the battery needs to be stopped emergently, the battery unit is replaced, and the battery maintenance mode can be determined according to the fault levels.
Drawings
FIG. 1 is a flow chart of a method for monitoring faults of an automobile storage battery of an electrically controlled vehicle according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in FIG. 1, the invention provides a method for monitoring the fault of an automobile storage battery of an electric control vehicle, which comprises the following steps:
step S110, reading state data of the ith storage battery in the automobile storage battery pack in a time period t, wherein the state data comprises: voltage of battery Ui(t) internal resistance of cell Ri(T) cell temperature Ti(t), discharge current Ii(t) electricityHeight h of electrolyte in celli(t) amount of stored electricity Q of batteryi(t)；
Step S120, dividing the t time interval into n time units, and calculating the average value of state data in each time unit;
step S130, calculating a discharge capacity coefficient of the ith storage battery according to the state data mean value;
Figure BDA0002062229010000041
wherein,
Figure BDA0002062229010000042
Fiis the discharge capacity coefficient of the i-th storage battery, fi jThe discharge capacity coefficient of the storage battery in the jth period of the ith storage battery,
Figure BDA0002062229010000043
is the average value of the voltage of the cells of the ith storage battery in the jth time period,
Figure BDA0002062229010000044
the average value of the internal resistance of the i storage batteries in the jth time interval,
Figure BDA0002062229010000045
is the average value of the discharge current of the ith storage battery in the jth time period,
Figure BDA0002062229010000046
the average value of the charge capacity of the ith storage battery in the jth time period is shown; for the gamma function, K is the scaling factor.
Calculating the power supply capacity coefficient of the ith storage battery according to the state data mean value obtained by calculation in the second step:
Figure BDA0002062229010000051
Figure BDA0002062229010000052
wherein G isiThe power supply capacity coefficient of the ith storage battery,
Figure BDA0002062229010000053
l n is a Laguerre polynomial, and n is the number of time units.
In another embodiment, the state data of the automobile storage battery pack is selected in a time period of 1h ≤ t ≤ 4h, t is the time period, h is hour, and the time unit satisfies 80 ≤ n ≤ 100
Step S140, discharge capability coefficient FiAnd power supply capacity coefficient GiInput to a fuzzy controller.
Wherein, Fi、GiRespectively [10,30 ]]，[20,60]，Fi、GiAre all { -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}
Then the scale factor k1＝6/20k2＝6/40
Defining fuzzy subsets and membership functions
Coefficient of discharge capacity FiSeven fuzzy states are divided: PB (positive large), PM (positive small), PS (positive small), 0 (zero), NS (negative small), NM (negative medium) and NB (negative large), and the discharge capacity coefficient F is obtained by combining experienceiAs shown in table 1.
TABLE 1 discharge Capacity factor FiTable of membership functions
Fi -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
PB 0 0 0 0 0 0 0 0 0 0 0 0 0
PM 0 0 0 0 0.2 0.4 0 0 0.2 0 0 0 0
PS 0 0 0 0.2 0.4 0.6 0 0 0.4 0.2 0 0 0
0 0 0 0.2 0.4 0.6 0.8 1.0 0 0.6 0.4 0.4 0 0
NB 0.2 0.4 0.4 0.8 0.8 0 0 0 0.8 0.8 0.8 0.2 0.4
NM 0.6 0.8 0.8 0 0 0 0 0 0 0 1.0 0.6 0.8
NS 0.8 1.0 1.0 0 0 0 0 0 0 0 0.1 0.8 1.0
Power supply capacity coefficient GiSeven fuzzy states are divided: PB (positive big), PM (positive middle), PS (positive small), 0 (zero), NS (negative small), NM (negative middle) and NB (negative big), and the power supply capacity coefficient G is obtained by combining experienceiAs shown in table 2.
TABLE 1 discharge Capacity coefficient GiTable of membership functions
Gi -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
PB 0 0 0 0 0 0 0 0 0 0 0 0 0
PM 0 0 0 0 0.2 0.4 0 0 0.2 0 0 0 0
PS 0 0 0 0.2 0.6 0.6 0 0 0.4 0.2 0 0 0
0 0 0 0.2 0.6 0.6 0.8 1.0 0 0.6 0.4 0.4 0 0
NB 0.2 0.4 0.4 0.8 0.8 0 0 0 0.8 0.8 0.8 0.2 0.4
NM 0.6 0.8 0.8 0 0 0 0 0 0 0 1.0 0.6 0.8
NS 0.8 1.0 1.0 0 0 0 0 0 0 0 0.1 0.8 1.0
The fuzzy inference process is acquired by executing complex matrix operation, the calculated amount is very large, the on-line inference is difficult to meet the real-time requirement of a control system, the fuzzy inference operation is carried out by adopting a table look-up method, a fuzzy inference decision adopts a two-input and single-output mode, the preliminary control rule of a fuzzy controller can be summarized through experience, the fuzzy controller carries out defuzzification on an output signal according to the obtained fuzzy value to obtain a fault level, a fuzzy control query table is solved, and as the domain of discourse is discrete, the fuzzy control rule can be expressed as a fuzzy matrix, and the fault level Z control rule is obtained by adopting single-point fuzzification and is shown in a table 3.
Table 3 is a fuzzy control rule table
Figure BDA0002062229010000061
Fuzzy set of fault classes as Z ═ D0，D1，D2，D3}，D0Zero order fault, indicating normal cell operation, D1Indicating continued operation for a first order fault, D2For a secondary failure, indicating the need for repair, D3And the three-level fault indicates that the emergency stop is needed and the battery unit needs to be replaced.
The invention designs and develops an automobile storage battery fault monitoring method for an electric control vehicle, which is characterized in that the discharge capacity coefficient and the power supply capacity coefficient of each storage battery are calculated according to the state data mean value of the automobile storage battery and are input into a fuzzy controller to obtain the fault degree of each storage battery, and the fault battery can be accurately positioned for replacement.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (6)

1. An automotive battery fault monitoring method for an electronically controlled vehicle, comprising:
step one, reading state data of an ith storage battery in an automobile storage battery pack in a time period t, wherein the state data comprises: voltage of battery Ui(t) internal resistance of cell Ri(T) cell temperature Ti(t), discharge current Ii(t) height h of electrolyte in batteryi(t) amount of stored electricity Q of batteryi(t)；
Step two, dividing the time interval t into n time units, and calculating the average value of state data in each time unit;
step three, calculating the discharge capacity coefficient of the ith storage battery according to the state data mean value;
calculating the power supply capacity coefficient of the ith storage battery according to the state data mean value;
inputting the discharge capacity coefficient and the power supply capacity coefficient into a fuzzy controller to obtain a vector group representing the fault category; and
the vector group representing the fault category is output as a fault diagnosis answer;
the discharge capacity coefficient calculation formula of the storage battery is as follows:
Figure FDA0002492772300000011
wherein,
Figure FDA0002492772300000012
Fifor the discharge capacity system of the ith batteryNumber fi jThe discharge capacity coefficient of the storage battery in the jth period of the ith storage battery,
Figure FDA0002492772300000013
is the average value of the voltage of the cells of the ith storage battery in the jth time period,
Figure FDA0002492772300000014
the average value of the internal resistance of the i storage batteries in the jth time interval,
Figure FDA0002492772300000015
is the average value of the discharge current of the ith storage battery in the jth time period,
Figure FDA0002492772300000016
the average value of the charge capacity of the ith storage battery in the jth time period is shown; is a gamma function, and K is a proportionality coefficient;
the power supply capacity coefficient calculation formula of the storage battery is as follows:
Figure FDA0002492772300000017
Figure FDA0002492772300000021
wherein G isiThe power supply capacity coefficient of the ith storage battery,
Figure FDA0002492772300000022
l n is a Laguerre polynomial, and n is the number of time units;
the working process of the fuzzy controller is as follows:
comparing the discharge capacity coefficient with a preset discharge capacity coefficient to obtain a discharge capacity coefficient deviation signal, and comparing the power supply capacity coefficient with a preset power supply capacity coefficient to obtain a power supply capacity coefficient deviation signal;
carrying out differential calculation on the discharge capacity coefficient deviation signal to obtain a discharge capacity coefficient change rate signal, and carrying out differential calculation on the power supply capacity coefficient deviation signal to obtain a power supply capacity coefficient change rate signal;
and amplifying the discharge capacity coefficient change rate signal and the power supply capacity coefficient change rate signal, inputting the amplified signals into a fuzzy controller, and outputting the amplified signals as a fault grade.
2. The vehicle battery fault monitoring method for the electronically controlled vehicle as recited in claim 1, wherein the fuzzy set of the discharging capability coefficient signal and the preset discharging capability coefficient signal is as follows: { NB, NM, NS, ZR, PS, PM, PB }, NB representing negative large, NM representing negative medium, NS representing negative small, ZR representing zero, PS representing positive small, PM representing positive medium, PB representing positive large, their domains of discourse are: { -6, -5, -4, -3, -2, -1,0,1,2,3,4,5,6}.
3. The automotive battery fault monitoring method for an electronically controlled vehicle of claim 2, wherein the fuzzy set of fault classes is { D [ ]0，D1，D2，D3}，D0Zero order fault, indicating normal cell operation, D1Indicating continued operation for a first order fault, D2For a secondary failure, indicating the need for repair, D3And the three-level fault indicates that the emergency stop is needed and the battery unit needs to be replaced.
4. The method of claim 3, wherein the membership function of the input variable of the fuzzy controller is a triangular membership function.
5. The automobile storage battery fault monitoring method for the electric control vehicle as claimed in claim 4, wherein the state data of the automobile storage battery pack is selected in a time period of 1h ≤ t ≤ 4 h;
wherein t is the time period and h is the hour.
6. The automobile battery failure monitoring method for the electrically controlled vehicle according to claim 5, characterized in that the time cell satisfies 80. ltoreq. n.ltoreq.100.
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Denomination of invention: An automobile battery fault monitoring method for electronic control vehicles

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