CN112986839A - Confidence interval-based fault diagnosis method and system for lithium ion power battery pack - Google Patents
Confidence interval-based fault diagnosis method and system for lithium ion power battery pack Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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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
- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/4207—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells for several batteries or cells simultaneously or sequentially
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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- 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
Abstract
The invention relates to a method and a system for diagnosing faults of a lithium ion power battery pack based on a confidence interval. The fault diagnosis method distinguishes the use states of the batteries by dividing the different use states of the batteries, and further judges whether the voltage of the batteries is within the safety threshold range of the stage. And with the continuous use of the battery, the aging condition of the battery is considered, and a continuously updated reasonable threshold range of the battery voltage is provided. The reasonable confidence interval of the battery voltage is calculated according to historical voltage data of the vehicle type battery after the abnormal point is removed, so that whether the voltage of the battery is in a normal range or not is judged, the fault of the battery can be reasonably and effectively diagnosed and an alarm can be given, and the fault rate of the battery is reduced.
Description
Technical Field
The invention relates to the field of battery monomer fault diagnosis, in particular to a lithium ion power battery pack fault diagnosis method and system based on a confidence interval.
Background
With the continuous popularization of new energy automobiles, the development of battery technology becomes the focus of attention of students, the energy density of the lithium ion power battery is also continuously improved along with the improvement of the requirement of people on the driving range of the electric automobile, and the potential safety hazard is more and more prominent. Before the thermal runaway of the battery occurs, the voltage of the battery can be greatly reduced, fault diagnosis can be carried out by means of the change of the voltage value, but because the service state of the battery is changeable in the actual running process of a vehicle, the problem of accurately judging the voltage value cannot be effectively solved in the conventional battery diagnosis method.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method and a system for diagnosing the fault of a lithium ion power battery pack based on a confidence interval.
In order to achieve the purpose, the invention provides the following scheme:
a fault diagnosis method of a lithium ion power battery pack based on a confidence interval comprises the following steps:
acquiring historical current data and historical voltage data of a single automobile battery;
determining the use state of the automobile battery monomer according to the historical current data; the use state includes: a charging state, a discharging state and a braking energy recovery state;
determining the voltage mean value and the voltage standard deviation of the battery monomer under different use states according to the historical voltage data;
determining confidence intervals of the battery monomers in different use states according to the voltage mean value and the voltage standard deviation;
correspondingly determining the voltage threshold ranges of the battery monomers in different use states according to the confidence intervals of the battery monomers in different use states;
acquiring current data and voltage data of a single battery at the current moment;
determining the use state of the battery monomer according to the current data of the battery monomer at the current moment, and acquiring the voltage threshold range in the use state;
and determining whether the single battery fails at the current moment according to the voltage data and the voltage threshold range in the use state.
Preferably, the determining the use state of the automobile battery cell according to the historical current data specifically includes:
when the current value in the historical current data is less than or equal to zero, determining that the service state of the automobile battery monomer is a charging state or a braking energy recovery state; the charging state includes: a fast charge state and a slow charge state; the braking energy recovery state includes: a weak braking energy recovery state and a forced dynamic energy recovery state;
when the current value in the historical current data is larger than zero, determining that the service state of the automobile battery monomer is a discharging state; the discharge state includes: an accelerated discharge state and a uniform discharge state.
Preferably, when the current value in the historical current data is less than or equal to zero, determining that the use state of the automobile battery cell is a charging state or a braking energy recovery state, specifically including:
acquiring a first preset current threshold, a second preset current threshold, a third preset current threshold, a fourth preset current threshold and a fifth preset current threshold; the first preset current threshold, the second preset current threshold, the third preset current threshold, the fourth preset current threshold and the fifth preset current threshold are all less than or equal to zero;
when the current value in the historical current data is larger than or equal to the first preset current threshold value, determining that the charging state of the automobile battery monomer is a quick charging state;
when the current value in the historical current data is smaller than the first preset current threshold value, determining that the charging state of the automobile battery monomer is a slow charging state;
when the current value in the historical current data is greater than or equal to the second preset current threshold and less than or equal to the third preset current threshold, determining that the braking energy recovery state of the single automobile battery is a weak braking energy recovery state;
and when the current value in the historical current data is greater than or equal to the fourth preset current threshold and less than or equal to the fifth preset current threshold, determining that the braking energy recovery state of the single automobile battery is a forced braking energy recovery state.
Preferably, when the current value in the historical current data is greater than zero, determining that the use state of the automobile battery cell is a discharge state specifically includes:
acquiring a sixth preset current threshold, a seventh preset current threshold and an eighth preset current threshold; the sixth preset current threshold, the seventh preset current threshold and the eighth preset current threshold are all greater than zero;
when the current value in the historical current data is larger than the sixth preset current threshold value, determining that the discharging state of the automobile battery monomer is an accelerated discharging state;
and when the current value in the historical current data is greater than or equal to the seventh preset current threshold value and less than or equal to the eighth preset current threshold value, determining that the discharge state of the automobile battery monomer is a constant-speed discharge state.
Preferably, historical current data and historical voltage data of the automobile battery cell are acquired, and then the method further comprises the following steps:
and rejecting abnormal values in the historical current data and the historical voltage data according to a 3 sigma criterion.
Preferably, the determining whether the battery cell fails at the current time according to the voltage data and the voltage threshold range in the use state specifically includes:
when the voltage data is within the voltage threshold range, determining that the battery monomer is normal at the current moment;
and when the voltage data is out of the voltage threshold range, determining that the single battery fails at the current moment.
Preferably, the method further comprises the following steps:
and according to the aging characteristic of the battery monomer, re-determining the voltage threshold range of the battery monomer in different use states after a specific time period.
Preferably, the specific period of time is 6 months.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a lithium ion power battery pack fault diagnosis method based on a confidence interval, which distinguishes the use states of batteries by dividing different use states of the batteries, and further judges whether the voltage of the batteries is in the safety threshold range of the stage. And with the continuous use of the battery, the aging condition of the battery is considered, and a continuously updated reasonable threshold range of the battery voltage is provided. The reasonable confidence interval of the battery voltage is calculated according to historical voltage data of the vehicle type battery after the abnormal point is removed, so that whether the voltage of the battery is in a normal range or not is judged, the fault of the battery can be reasonably and effectively diagnosed and an alarm can be given, and the fault rate of the battery is reduced.
Corresponding to the method for diagnosing the fault of the lithium ion power battery pack based on the confidence interval, the invention also provides a system, which specifically comprises the following steps:
a system for fault diagnosis of a lithium ion power battery based on confidence intervals, comprising:
the historical data acquisition module is used for acquiring historical current data and historical voltage data of the single automobile battery;
the using state determining module is used for determining the using state of the automobile battery monomer according to the historical current data; the use state includes: a charging state, a discharging state and a braking energy recovery state;
the voltage mean value-standard deviation determining module is used for determining the voltage mean value and the voltage standard deviation of the battery monomer under different use states according to the historical voltage data;
the confidence interval determining module is used for determining confidence intervals of the battery monomers in different use states according to the voltage mean value and the voltage standard deviation;
the voltage threshold range determining module is used for correspondingly determining the voltage threshold ranges of the battery monomers in different using states according to the confidence intervals of the battery monomers in different using states;
the current data acquisition module is used for acquiring current data and voltage data of the battery monomer at the current moment;
the voltage threshold range selection module is used for determining the use state of the single battery according to the current data of the single battery at the current moment and acquiring the voltage threshold range in the use state;
and the fault judging module is used for determining whether the single battery has faults at the current moment according to the voltage data and the voltage threshold range in the using state.
In addition, the invention also provides a computer storage medium, which stores a computer running program; the computer runs the program to execute the fault diagnosis method of the lithium ion power battery pack based on the confidence interval.
Since the technical problems solved by the fault diagnosis system and the computer storage medium provided by the invention are the same as the fault diagnosis method provided by the invention, the technical effects achieved by the fault diagnosis system and the computer storage medium are not repeated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for diagnosing a fault of a lithium ion power battery pack based on a confidence interval according to the present invention;
fig. 2 is a frame diagram of data transmission of a vehicle-mounted battery cell according to an embodiment of the present invention;
fig. 3 is a flowchart of a fault diagnosis method based on the transmission framework shown in fig. 2 according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fault diagnosis system of a lithium ion power battery pack based on a confidence interval provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for diagnosing faults of a lithium ion power battery pack based on a confidence interval, so that the faults of batteries can be diagnosed reasonably and effectively and an alarm can be given, and the fault rate of the batteries is further reduced.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for diagnosing a fault of a lithium ion power battery pack based on a confidence interval according to the present invention includes:
step 100: and acquiring historical current data and historical voltage data of the single automobile battery.
Step 101: and determining the use state of the automobile battery cell according to the historical current data. The use state comprises: a charging state, a discharging state, and a braking energy recovery state.
Specifically, when the current value in the historical current data is less than or equal to zero (i.e. I ≦ 0, where I is the current value in the historical current data), it is determined that the use state of the automobile battery cell is the charging state or the braking energy recovery state. The charging state includes: a fast charge state and a slow charge state. The braking energy recovery state includes: a weak braking energy recovery state and a forced kinetic energy recovery state.
And when the current value in the historical current data is larger than zero (i.e. I >0), determining that the using state of the automobile battery cell is a discharging state. The discharge state includes: an accelerated discharge state and a uniform discharge state.
When the current value in the historical current data is greater than or equal to a first preset current threshold (i.e. I ≧ I)0，I0A first preset current threshold), the charging state of the automobile battery monomer is determined to be a quick charging state.
When the current value in the historical current data is less than a first preset current threshold (i.e. I)<I0) And if so, determining that the charging state of the automobile battery monomer is a slow charging state.
When the current value in the historical current data is greater than or equal to the second preset current threshold value and less than or equal to the third preset current threshold value (i.e. I)1≦I≦I2，I1Is a second predetermined current threshold, I2A third preset current threshold), it is determined that the braking energy recovery state of the automobile battery monomer is a weak braking energy recovery state.
When the current value in the historical current data is greater than or equal to the fourth preset current threshold value and less than or equal to the fifth preset current threshold value (i.e. I)3≦I≦I4，I3Is a fourth predetermined current threshold, I4A fifth preset current threshold), determining that the braking energy recovery state of the automobile battery monomer is a forced braking energy recovery state.
When the current value in the historical current data is larger than a sixth preset current threshold (i.e. I)>I5，I5A sixth preset current threshold), the discharge state of the automobile battery cell is determined to be an accelerated discharge state.
When the current value in the historical current data is greater than or equal to the seventh preset current threshold value and less than or equal to the eighth preset currentThreshold value (i.e. I)6≦I≦I7，I6Is a seventh predetermined current threshold, I7An eighth preset current threshold), the discharge state of the automobile battery monomer is determined to be a uniform discharge state.
The first preset current threshold, the second preset current threshold, the third preset current threshold, the fourth preset current threshold and the fifth preset current threshold are all less than or equal to zero, and the sixth preset current threshold, the seventh preset current threshold and the eighth preset current threshold are all greater than zero. The first preset current threshold, the second preset current threshold, the third preset current threshold, the fourth preset current threshold, the fifth preset current threshold, the sixth preset current threshold, the seventh preset current threshold and the eighth preset current threshold are all values artificially determined according to historical data and actual application conditions of the vehicle.
Step 102: and determining the voltage mean value and the voltage standard deviation of the battery cells under different use states according to the historical voltage data.
For example:
voltage mean value mu of a certain battery monomer under fast charging state1＝(Ut1+Ut2+······+Utn)/n。
Standard deviation of voltage of a battery cell in a rapid charging state
And n is the total number of the single batteries.
Then, the voltage average value μ of a certain battery cell in the slow charging state is calculated sequentially in the same manner2Standard deviation of voltage σ2. Voltage mean value mu of a certain battery cell in acceleration state3Standard deviation of voltage σ3. Voltage mean value mu of a certain battery monomer under uniform speed state4Standard deviation of voltage σ4. Voltage mean value mu of certain battery monomer in weak braking energy recovery state5Standard deviation of voltage σ5. Voltage mean value mu of certain battery monomer in forced dynamic energy recovery state6Standard deviation of voltage σ6。
Step 103: and determining confidence intervals of the battery cells in different use states according to the voltage mean value and the voltage standard deviation.
Step 104: and correspondingly determining the voltage threshold ranges of the battery monomers in different use states according to the confidence intervals of the battery monomers in different use states.
Specifically, a 95% confidence level is selected, and a confidence interval of the cell voltage in the use state is further calculated according to the calculated voltage mean and voltage standard deviation.
The calculation formula of the confidence interval is as follows:
wherein j is 1,2, …, 6.
And respectively calculating voltage confidence intervals in the states of quick charging, slow charging, acceleration, constant speed, weak braking energy recovery, forced dynamic energy recovery and the like based on the calculation formula.
Step 105: and acquiring current data and voltage data of the battery monomer at the current moment.
Step 106: and determining the use state of the battery monomer according to the current data of the battery monomer at the current moment, and acquiring the voltage threshold range in the use state.
Wherein the voltage threshold range of the fast charging state is [ U ]1，U2]. The voltage threshold range of the slow charging state is [ U ]3,U4]. The voltage threshold range of the accelerated discharge state is [ U ]5,U6]. The voltage threshold range of the uniform discharge state is [ U ]7,U8]. The voltage threshold range of the forced kinetic energy recovery state is [ U ]9,U10]The voltage threshold range of the weak braking energy recovery state is [ U ]11,U12]。
Step 107: and determining whether the single battery fails at the current moment according to the voltage data and the voltage threshold range in the use state.
Specifically, when the voltage data is within the voltage threshold range, it is determined that the battery cell is normal at the current time. Namely U1≦U≦U2、U3≦U≦U4、U5≦U≦U6、U7≦U≦U8、U9≦U≦U10Or U11≦U≦U12. And U is the voltage value of the battery monomer at the current moment.
And when the voltage data is out of the voltage threshold range, determining that the battery cell has a fault at the current moment.
In order to further ensure the accuracy of the determined voltage threshold range, after obtaining the historical current data and the historical voltage data of the automobile battery cell, the method preferably further comprises the following steps: and rejecting abnormal values in the historical current data and the historical voltage data according to a 3 sigma criterion. That is, historical voltage data of the battery cell is collected, and abnormal points in the obtained voltage data of the battery cell are removed according to a 3 σ criterion.
In order to further improve the accuracy of the diagnosis of the battery cells, the above-mentioned fault diagnosis method disclosed in the present invention further needs to re-determine the voltage threshold ranges of the battery cells in different use states after a specific period of time (preferably 6 months) according to the aging characteristics of the battery cells.
The following describes a specific application process of the method for diagnosing a fault of a lithium ion power battery pack based on a confidence interval, by taking a specific process of transmitting battery cell data shown in fig. 2 as an example.
As shown in fig. 3, the specific application process of the fault diagnosis method is as follows:
step 1: the battery management system transmits voltage and current data acquired by the voltage and current sensors to the vehicle-mounted terminal through the CAN bus, and the vehicle-mounted terminal transmits the voltage and current data to the new energy automobile big data platform by means of a wireless network. The specific data transmission process is shown in fig. 2.
Step 2: and acquiring n single voltages of a plurality of groups of single frames from the big data platform. The single frame data is as follows:
the No. 1 monomer voltage is 3.21V, the No. 2 monomer voltage is 3.22V and the like corresponding to 2020:09:1513:00:00 time.
A voltage matrix U is formed, the row direction representing the cell number and the column direction representing the time sequence.
And step 3: and judging the use states of the battery (the positive current is discharging, and the negative current is charging) according to the current data of the battery, and classifying the use states of the battery, wherein the use states comprise a charging state (quick charging and slow charging), a discharging state (accelerating and uniform speed) and a braking energy recovery state (forced dynamic energy recovery and weak braking energy recovery).
The charging state is divided into a fast charging state and a slow charging state. The discharge state is divided into an acceleration state and a uniform speed state. The braking energy recovery state is further divided into a weak braking energy recovery and a forced braking energy recovery.
And if I <0, the battery is in a charging state or a braking energy recovery state. If I >0, the battery is in a discharged state.
If the current I of the battery>I0And the battery is in a quick charging state. If I<I0The battery is in a slow charge state.
If the current I of the battery1≦I≦I2The battery is in a weak braking energy recovery stage. Current I of the battery3≦I≦I4The battery is in a forced energy recovery stage.
If the current I of the battery>I5The battery is in an acceleration state. I is6≦I≦I7And the battery is in a constant speed state.
And 4, step 4: historical voltage data of the battery cell is collected, and abnormal points in the obtained voltage data of the battery cell are removed according to a 3 sigma criterion.
And 5: according to different use states of the battery (fast charge, slow charge, accelerated discharge, uniform discharge, weak braking energy recovery and forced dynamic energy recovery), mathematical statistics is carried out on data in different use states, and the voltage mean value mu and the standard deviation sigma of the voltage of the battery monomer in the use state are respectively calculated.
For example:
in the fast charging stateVoltage mean value mu of a certain battery cell1＝(Ut1+Ut2+······+Utn)/n
Standard deviation of voltage of a battery cell in a rapid charging state
Sequentially calculating the voltage mean value mu of a certain battery cell in the slow charging state in the same way2Standard deviation of voltage σ2. Voltage mean value mu of a certain battery cell in acceleration state3Standard deviation of voltage σ3. Voltage mean value mu of a certain battery monomer under uniform speed state4Standard deviation of voltage σ4. Voltage mean value mu of certain battery monomer in weak braking energy recovery state5Standard deviation of voltage σ5. Voltage mean value mu of certain battery monomer in forced dynamic energy recovery state6Standard deviation of voltage σ6。
Step 6: a confidence level of 95% is selected and a confidence interval for the cell voltage in this use state is further calculated from the calculated mean μ and standard deviation σ.
The calculation formula of the confidence interval is as follows:
based on the formula, voltage confidence intervals in the states of quick charge, slow charge, accelerated discharge, uniform discharge, weak braking energy recovery and forced dynamic energy recovery are calculated respectively.
And 7: and dividing the voltage threshold values in different use states (namely the threshold values in different use states which are the confidence intervals of the voltage of the single battery) according to the voltage confidence intervals in different use states of the battery in the step 6. The quick charging voltage threshold is U1≦U≦U2. Slow charging voltage threshold U3≦U≦U4. Acceleration phase voltage threshold U5≦U≦U6. Voltage threshold U at uniform speed stage7≦U≦U8. The braking energy recovery stage is divided into two types, namely a forced dynamic energy recovery voltage threshold U9≦U≦U10Weak braking energy recovery voltage threshold U11≦U≦U12。
And 8, continuously adjusting and changing the determined threshold range (updating the threshold range every 6 months) according to the aging condition of the battery so as to meet the influence of the continuous aging of the battery on the change of the battery voltage.
Namely, the voltage threshold of the battery cell under different use states is recalculated every six months (the steps 4 to 7 are repeated)
And step 9: and judging whether the voltage of the battery is within a normal threshold range of the section (the use state) or not according to the voltage of the battery, and if so, judging that the voltage of the battery is normal. If not, firstly checking whether the battery cell has communication faults or not. If no communication fault exists, the battery voltage data is diagnosed to be abnormal, and an alarm is given.
Based on the above content, the method for diagnosing the fault of the lithium ion power battery pack based on the confidence interval provided by the invention judges the use state of the battery according to the current by using the voltage and current data parameters of the single battery transmitted in real time, divides the confidence intervals of different use stages of the battery according to the historical voltage of the battery, and then judges whether the voltage of the battery is in the corresponding confidence interval, so that the method is simple and has high real-time performance. In addition, whether the voltage is in the corresponding confidence interval or not can be quickly diagnosed and an alarm can be given through the method, so that the voltage fault of the power battery can be effectively prevented.
Corresponding to the method for diagnosing the fault of the lithium ion power battery pack based on the confidence interval, the invention also provides a system, as shown in fig. 4, the system for diagnosing the fault of the lithium ion power battery pack based on the confidence interval comprises: the device comprises a historical data acquisition module 1, a use state determination module 2, a voltage mean value-standard deviation determination module 3, a confidence interval determination module 4, a voltage threshold range determination module 5, a current data acquisition module 6, a voltage threshold range selection module 7 and a fault determination module 8.
The historical data acquisition module 1 is used for acquiring historical current data and historical voltage data of the single automobile battery.
The using state determining module 2 is used for determining the using state of the automobile battery cell according to the historical current data. The use state comprises: a charging state, a discharging state, and a braking energy recovery state.
The voltage mean value-standard deviation determination module 3 is used for determining the voltage mean value and the voltage standard deviation of the battery cells under different use states according to historical voltage data.
The confidence interval determination module 4 is used for determining confidence intervals of the battery cells in different use states according to the voltage mean value and the voltage standard deviation.
The voltage threshold range determining module 5 is configured to correspondingly determine the voltage threshold ranges of the battery cells in different usage states according to the confidence intervals of the battery cells in different usage states.
The current data acquisition module 6 is configured to acquire current data and voltage data of the battery cell at the current moment.
The voltage threshold range selection module 7 is configured to determine a use state of the battery cell according to current data of the battery cell at the current time, and acquire a voltage threshold range in the use state.
The fault determination module 8 is configured to determine whether a battery cell has a fault at the current time according to the voltage data and the voltage threshold range in the use state.
In addition, the invention also provides a computer storage medium, and the computer storage medium stores a computer running program. And the computer runs a program to execute the fault diagnosis method of the lithium ion power battery pack based on the confidence interval.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A method for diagnosing faults of a lithium ion power battery pack based on a confidence interval is characterized by comprising the following steps:
acquiring historical current data and historical voltage data of a single automobile battery;
determining the use state of the automobile battery monomer according to the historical current data; the use state includes: a charging state, a discharging state and a braking energy recovery state;
determining the voltage mean value and the voltage standard deviation of the battery monomer under different use states according to the historical voltage data;
determining confidence intervals of the battery monomers in different use states according to the voltage mean value and the voltage standard deviation;
correspondingly determining the voltage threshold ranges of the battery monomers in different use states according to the confidence intervals of the battery monomers in different use states;
acquiring current data and voltage data of a single battery at the current moment;
determining the use state of the battery monomer according to the current data of the battery monomer at the current moment, and acquiring the voltage threshold range in the use state;
and determining whether the single battery fails at the current moment according to the voltage data and the voltage threshold range in the use state.
2. The method for diagnosing the fault of the lithium-ion power battery pack based on the confidence interval according to claim 1, wherein the determining the use state of the automobile battery cell according to the historical current data specifically comprises:
when the current value in the historical current data is less than or equal to zero, determining that the service state of the automobile battery monomer is a charging state or a braking energy recovery state; the charging state includes: a fast charge state and a slow charge state; the braking energy recovery state includes: a weak braking energy recovery state and a forced dynamic energy recovery state;
when the current value in the historical current data is larger than zero, determining that the service state of the automobile battery monomer is a discharging state; the discharge state includes: an accelerated discharge state and a uniform discharge state.
3. The method for diagnosing the fault of the lithium-ion power battery pack based on the confidence interval according to claim 2, wherein when the current value in the historical current data is less than or equal to zero, it is determined that the usage state of the vehicle battery cell is a charging state or a braking energy recovery state, and specifically includes:
acquiring a first preset current threshold, a second preset current threshold, a third preset current threshold, a fourth preset current threshold and a fifth preset current threshold; the first preset current threshold, the second preset current threshold, the third preset current threshold, the fourth preset current threshold and the fifth preset current threshold are all less than or equal to zero;
when the current value in the historical current data is larger than or equal to the first preset current threshold value, determining that the charging state of the automobile battery monomer is a quick charging state;
when the current value in the historical current data is smaller than the first preset current threshold value, determining that the charging state of the automobile battery monomer is a slow charging state;
when the current value in the historical current data is greater than or equal to the second preset current threshold and less than or equal to the third preset current threshold, determining that the braking energy recovery state of the single automobile battery is a weak braking energy recovery state;
and when the current value in the historical current data is greater than or equal to the fourth preset current threshold and less than or equal to the fifth preset current threshold, determining that the braking energy recovery state of the single automobile battery is a forced braking energy recovery state.
4. The method for diagnosing the fault of the lithium-ion power battery pack based on the confidence interval as claimed in claim 2, wherein when the current value in the historical current data is greater than zero, the method for determining the use state of the automobile battery cell as the discharge state specifically comprises the following steps:
acquiring a sixth preset current threshold, a seventh preset current threshold and an eighth preset current threshold; the sixth preset current threshold, the seventh preset current threshold and the eighth preset current threshold are all greater than zero;
when the current value in the historical current data is larger than the sixth preset current threshold value, determining that the discharging state of the automobile battery monomer is an accelerated discharging state;
and when the current value in the historical current data is greater than or equal to the seventh preset current threshold value and less than or equal to the eighth preset current threshold value, determining that the discharge state of the automobile battery monomer is a constant-speed discharge state.
5. The method for diagnosing the fault of the lithium-ion power battery pack based on the confidence interval according to claim 1, wherein historical current data and historical voltage data of a vehicle battery cell are acquired, and then the method further comprises the following steps:
and rejecting abnormal values in the historical current data and the historical voltage data according to a 3 sigma criterion.
6. The method for diagnosing the fault of the lithium-ion power battery pack based on the confidence interval according to claim 1, wherein the step of determining whether the battery cell has the fault at the current moment according to the voltage data and the voltage threshold range in the use state specifically comprises the following steps:
when the voltage data is within the voltage threshold range, determining that the battery monomer is normal at the current moment;
and when the voltage data is out of the voltage threshold range, determining that the single battery fails at the current moment.
7. The method for fault diagnosis of a lithium-ion power battery pack based on confidence intervals of claim 1, further comprising:
and according to the aging characteristic of the battery monomer, re-determining the voltage threshold range of the battery monomer in different use states after a specific time period.
8. The method of fault diagnosis of a lithium ion power battery based on confidence interval according to claim 7, characterized in that the specific period of time is 6 months.
9. A system for diagnosing faults of a lithium ion power battery based on confidence intervals, comprising:
the historical data acquisition module is used for acquiring historical current data and historical voltage data of the single automobile battery;
the using state determining module is used for determining the using state of the automobile battery monomer according to the historical current data; the use state includes: a charging state, a discharging state and a braking energy recovery state;
the voltage mean value-standard deviation determining module is used for determining the voltage mean value and the voltage standard deviation of the battery monomer under different use states according to the historical voltage data;
the confidence interval determining module is used for determining confidence intervals of the battery monomers in different use states according to the voltage mean value and the voltage standard deviation;
the voltage threshold range determining module is used for correspondingly determining the voltage threshold ranges of the battery monomers in different using states according to the confidence intervals of the battery monomers in different using states;
the current data acquisition module is used for acquiring current data and voltage data of the battery monomer at the current moment;
the voltage threshold range selection module is used for determining the use state of the single battery according to the current data of the single battery at the current moment and acquiring the voltage threshold range in the use state;
and the fault judging module is used for determining whether the single battery has faults at the current moment according to the voltage data and the voltage threshold range in the using state.
10. A computer storage medium, wherein the computer storage medium stores a computer running program; the computer runs a program to perform the method of fault diagnosis of a lithium ion power battery pack based on confidence intervals as claimed in any one of claims 1 to 8.
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