CN112526376A - Method and device for estimating abnormity of single automobile battery - Google Patents
Method and device for estimating abnormity of single automobile battery Download PDFInfo
- Publication number
- CN112526376A CN112526376A CN202011388995.5A CN202011388995A CN112526376A CN 112526376 A CN112526376 A CN 112526376A CN 202011388995 A CN202011388995 A CN 202011388995A CN 112526376 A CN112526376 A CN 112526376A
- Authority
- CN
- China
- Prior art keywords
- battery
- model
- value
- polarization
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000010287 polarization Effects 0.000 claims abstract description 118
- 239000000178 monomer Substances 0.000 claims abstract description 81
- 238000003745 diagnosis Methods 0.000 claims abstract description 70
- 230000036541 health Effects 0.000 claims abstract description 42
- 230000002159 abnormal effect Effects 0.000 claims abstract description 33
- 238000013179 statistical model Methods 0.000 claims abstract description 30
- 238000012549 training Methods 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 12
- 238000013499 data model Methods 0.000 claims abstract description 7
- 238000011002 quantification Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 27
- 230000005856 abnormality Effects 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 15
- 230000032683 aging Effects 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 238000013075 data extraction Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000004880 explosion Methods 0.000 description 4
- 230000003862 health status Effects 0.000 description 4
- 230000001737 promoting effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
Images
Classifications
-
- 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- 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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- 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
-
- 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
-
- 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
- H01M10/4285—Testing apparatus
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides a method and a device for estimating the abnormity of a single automobile battery, wherein the method comprises the following steps: monitoring and extracting battery use data in real time when a vehicle is started and flamed out; calculating the polarization voltage value of any battery cell; respectively calculating and analyzing a polarization voltage Z fractional value of the battery monomer, an accumulated deviation value of the polarization voltage of the battery monomer and a cosine set variance value of an included angle between any two battery monomer polarization voltages by using a Z fractional model, a statistical model and an angle variance model; performing big data model training to obtain the relation between the battery health state and the abnormal quantification, and establishing a battery health state diagnosis database; according to the three values, whether the battery reaches the preset condition of the battery health state diagnosis database is judged to obtain the battery diagnosis result.
Description
Technical Field
The invention relates to the field of automobiles, in particular to a method and a device for estimating the abnormity of an automobile battery monomer.
Background
Some battery systems may have abnormalities and faults due to changes in the material and material parameters inside the battery cells, but some battery systems may not quickly react to the operation of the battery system despite the changes in the material and material parameters inside the battery cells, so that some battery systems may still be in a stage of risk of faults although they can operate normally.
At present, most of vehicle enterprises only detect and judge a battery system which is abnormal or has a fault, but do not detect the material and material parameters in a battery monomer, so that the abnormality of the battery monomer cannot be estimated in advance, and the safety problem and the fault of the electric vehicle cannot be avoided by taking protective measures in advance for the possible fault problem.
Disclosure of Invention
The invention aims to provide an automobile battery monomer abnormity estimation method to solve the problems that most of vehicle enterprises in the prior art only detect and judge a battery system with abnormity or faults, but do not detect material and material parameters in a battery monomer, so that abnormity of the battery monomer cannot be estimated in advance, and protection measures cannot be taken in advance for possible fault problems to avoid safety problems and faults of an electric automobile.
The invention provides an automobile battery monomer abnormity prediction method, which comprises the following steps:
monitoring and extracting battery use data in real time when a vehicle is started and flamed out, and processing abnormal and invalid data in the extracted battery use data, wherein the battery use data comprises time, a current value, a total voltage value, a single battery voltage value and a single battery serial number;
calculating the polarization voltage value U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2
Wherein, Ut1、Ut2Battery cell terminal voltages corresponding to times t1 and t2 respectively;
respectively calculating and analyzing a polarization voltage Z fractional value of the battery monomer, an accumulated deviation value of the polarization voltage of the battery monomer and a cosine set variance value of an included angle between any two battery monomer polarization voltages by using a Z fractional model, a statistical model and an angle variance model;
carrying out big data model training on vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database;
and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
The method for estimating the abnormity of the single automobile battery provided by the invention has the following beneficial effects:
the device is suitable for all types of electric automobiles, only needs the relevant data of the electric automobiles, and based on the voltage data of the battery monomer extracted in the real-time running process of the electric automobiles, a polarization voltage calculation method is applied, the polarization voltage obtained by calculation is used as a parameter, then a Z fraction model, a statistical model and an angle variance model are applied to carry out real-time monitoring on the health state of the battery, and the abnormal monomer in the battery pack is identified, so that early warning is achieved.
The invention can find the abnormal problem of the power battery system in advance, give early warning before the safety problem and the fault occur, can effectively reduce the occurrence of safety accidents such as the fire and the explosion of the electric automobile, and avoid the huge economic loss of life and property. In addition, the vehicle enterprises can know vehicles which are likely to break down in advance through the early warning information, the broken batteries are replaced in advance, the satisfaction degree of customers to the vehicle enterprises is increased while the after-sale service quality is improved, and the vehicle enterprises have a great promoting effect on increasing sales volume and market share.
In addition, according to the method for estimating the abnormality of the single automobile battery provided by the invention, the following additional technical characteristics can be provided:
further, in the step of calculating and analyzing the Z-fraction value of the polarization voltage of the battery cell by using a Z-fraction model, the calculation formula of the Z-fraction model is as follows:
wherein, Zpoli,jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σpolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
Further, in the step of calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, the calculation formula of the statistical model is as follows:
wherein, HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
Further, in the step of calculating and analyzing the variance value of the cosine set of the included angle between the polarization voltages of any two battery cells by using an angle variance model, the calculation formula of the angle variance model is as follows:
wherein, PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
the vector is formed by the polarization voltages of any two single batteries in the battery pack.
Further, the battery health state diagnosis database comprises a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database and a battery aging diagnosis database.
The invention provides an automobile battery monomer abnormity estimation device, which comprises:
a data extraction module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring battery use data of a vehicle when the vehicle is started and flameout in real time, and processing abnormal and invalid data in the acquired battery use data, and the battery use data comprises time, a current value, a total voltage value, a single battery voltage value and a single battery serial number;
a polarization voltage calculation module: for calculating the polarization voltage value U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2
Wherein, Ut1、Ut2Battery cell terminal voltages corresponding to times t1 and t2 respectively;
a model analysis module: the system comprises a Z-fraction model, a statistical model and an angle variance model, wherein the Z-fraction model, the statistical model and the angle variance model are used for respectively calculating and analyzing a polarization voltage Z-fraction value of a battery monomer, an accumulated deviation value of polarization voltages of the battery monomer and a cosine set variance value of an included angle between any two polarization voltages of the battery monomer;
the battery health state diagnosis database establishing module comprises: carrying out big data model training on vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database;
the early warning processing module: and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
Further, the model analysis module includes:
z score model analysis module: the Z-fraction model is used for calculating and analyzing a polarization voltage Z-fraction value of the battery cell, and the calculation formula of the Z-fraction model is as follows:
wherein, Zpoli,jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σpolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
Further, the model analysis module further comprises:
a statistical model analysis module: the method is used for calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, and the calculation formula of the statistical model is as follows:
wherein, HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
Further, the model analysis module further comprises:
an angle variance model analysis module: the method is used for calculating and analyzing the cosine set variance value of the included angle between the polarization voltages of any two battery monomers by using an angle variance model, and the calculation formula of the angle variance model is as follows:
wherein, PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
the vector is formed by the polarization voltages of any two single batteries in the battery pack.
Further, the battery health state diagnosis database comprises a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database and a battery aging diagnosis database.
The device for estimating the abnormity of the single automobile battery provided by the invention has the following beneficial effects:
the device is suitable for all types of electric automobiles, only needs the relevant data of the electric automobiles, and based on the voltage data of the battery monomer extracted in the real-time running process of the electric automobiles, a polarization voltage calculation method is applied, the polarization voltage obtained by calculation is used as a parameter, then a Z fraction model, a statistical model and an angle variance model are applied to carry out real-time monitoring on the health state of the battery, and the abnormal monomer in the battery pack is identified, so that early warning is achieved.
The invention can find the abnormal problem of the power battery system in advance, give early warning before the safety problem and the fault occur, can effectively reduce the occurrence of safety accidents such as the fire and the explosion of the electric automobile, and avoid the huge economic loss of life and property. In addition, the vehicle enterprises can know vehicles which are likely to break down in advance through the early warning information, the broken batteries are replaced in advance, the satisfaction degree of customers to the vehicle enterprises is increased while the after-sale service quality is improved, and the vehicle enterprises have a great promoting effect on increasing sales volume and market share.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart illustrating a method for predicting an abnormality of a battery cell of an automobile according to a first embodiment of the present invention;
fig. 2 is a block diagram of an abnormality estimation device for a battery cell of an automobile according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. Several embodiments of the invention are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Referring to fig. 1, a first embodiment of the invention provides a method for estimating abnormality of a battery cell of an automobile, which includes steps S101 to S104.
S101: the method comprises the steps of monitoring and extracting battery use data in real time when a vehicle starts and stops, and processing abnormal and invalid data in the extracted battery use data, wherein the battery use data comprise time, a current value, a total voltage value, a single battery voltage value and a single battery serial number.
The extracted voltage value of the battery cell is between U +/-0.2V, and U is the rated voltage of the battery.
The extracted current value is between 0 and 1A.
The complexity of the data transmission environment and the possible existence of interference and other reasons often cause that some invalid or abnormal data exist in the vehicle battery usage data recorded on the vehicle networking platform, for example, there are often incomplete data, abnormal data, null data and the like. Therefore, abnormal and invalid data in the battery use data on the internet of vehicles need to be processed, so that real battery use data are obtained, and interference of data problems on early warning results is eliminated.
S102: calculating the polarization voltage value U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2 (1)
In the formula (1), Ut1、Ut2The cell terminal voltages at times t1 and t2, respectively.
S103: and respectively calculating and analyzing a polarization voltage Z fraction value of the battery monomer, an accumulated deviation value of the polarization voltage of the battery monomer and a cosine set variance value of an included angle between any two battery monomer polarization voltages by using a Z fraction model, a statistical model and an angle variance model.
S104: and (3) carrying out big data model training on the vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database.
The battery health state diagnosis database comprises a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database and a battery aging diagnosis database.
S105: and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
And if the preset conditions of the battery health state diagnosis database are met, timely alarming is carried out, battery diagnosis result data are sent to relevant personnel in the forms of cloud, mails or short messages and the like, and the relevant personnel correspondingly process the early warning vehicle.
In the step of calculating and analyzing the polarization voltage Z fraction value of the battery cell by using a Z fraction model, the calculation formula of the Z fraction model is as follows:
in the formula (2), Zpoli,jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σpolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
And training Z fraction model data to obtain a quantitative relation: if the absolute value of Zpoli, j is less than or equal to 2, no abnormal monomer exists, and the health state is good; if the absolute value Zpoli, j is more than 2, the health state is sub-healthy, but alarm early warning is not carried out, and only important monitoring is carried out.
In the step of calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, the calculation formula of the statistical model is as follows:
in the formula (3), HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
Training statistical model data to obtain a quantitative relation: calculating Hpol in t1-t2 time periodi98 th percentile of value D98If D isi＜D98The battery health status is good; if D isi≥D98The battery may be in a sub-healthy state.
In the step of calculating and analyzing the cosine set variance value of the included angle between the polarization voltages of any two battery monomers by using an angle variance model, the calculation formula of the angle variance model is as follows:
in the formula (4), PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
the vector is formed by the polarization voltages of any two single batteries in the battery pack.
Training angle variance model data to obtain a quantitative relation: mean value PolCos for calculating cosine set of included angle between any two monomer polarization voltagesaveIf, if
The battery health status is good; if it is
The health state is sub-healthy, but alarm and early warning are not carried out, and only important monitoring is carried out.
In conclusion, the method for estimating the abnormality of the single automobile battery has the advantages that: the method is suitable for all types of electric automobiles, only the relevant data of the electric automobile operation is needed, the polarization voltage calculation method is applied based on the voltage data of the battery monomer extracted in the real-time operation process of the electric automobile, the polarization voltage obtained through calculation is used as a parameter, then a Z fraction model, a statistical model and an angle variance model are applied to carry out real-time monitoring on the health state of the battery, and the abnormal monomer in the battery pack is identified, so that early warning is achieved.
The invention can find the abnormal problem of the power battery system in advance, give early warning before the safety problem and the fault occur, can effectively reduce the occurrence of safety accidents such as the fire and the explosion of the electric automobile, and avoid the huge economic loss of life and property. In addition, the vehicle enterprises can know vehicles which are likely to break down in advance through the early warning information, the broken batteries are replaced in advance, the satisfaction degree of customers to the vehicle enterprises is increased while the after-sale service quality is improved, and the vehicle enterprises have a great promoting effect on increasing sales volume and market share.
Referring to fig. 2, a second embodiment of the present invention provides an apparatus for estimating abnormality of a battery cell of an automobile, including:
a data extraction module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring battery use data of a vehicle when the vehicle is started and flameout in real time, and processing abnormal and invalid data in the acquired battery use data, and the battery use data comprises time, a current value, a total voltage value, a single battery voltage value and a single battery serial number;
a polarization voltage calculation module: for calculating the polarization voltage value U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2 (1)
In the formula (1), Ut1、Ut2Battery cell terminal voltages corresponding to times t1 and t2 respectively;
a model analysis module: and the Z-fraction model, the statistical model and the angle variance model are used for respectively calculating and analyzing the polarization voltage Z-fraction value of the battery monomer, the accumulated deviation value of the polarization voltage of the battery monomer and the cosine set variance value of the polarization voltage included angle between any two battery monomers.
The model analysis module includes:
z score model analysis module: the Z-fraction model is used for calculating and analyzing a polarization voltage Z-fraction value of the battery cell, and the calculation formula of the Z-fraction model is as follows:
in the formula (2), Zpoli,jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σpolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
And training Z fraction model data to obtain a quantitative relation: if the absolute value of Zpoli, j is less than or equal to 2, no abnormal monomer exists, and the health state is good; if the absolute value Zpoli, j is more than 2, the health state is sub-healthy, but alarm early warning is not carried out, and only important monitoring is carried out.
The model analysis module further comprises:
a statistical model analysis module: the method is used for calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, and the calculation formula of the statistical model is as follows:
in the formula (3), HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
Training statistical model data to obtain a quantitative relation: calculating Hpol in t1-t2 time periodi98 th percentile of value D98If D isi＜D98The battery health status is good; if D isi≥D98The battery may be in a sub-healthy state.
The model analysis module further comprises:
an angle variance model analysis module: the method is used for calculating and analyzing the cosine set variance value of the included angle between the polarization voltages of any two battery monomers by using an angle variance model, and the calculation formula of the angle variance model is as follows:
in the formula (4), PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
polarize any two single batteries in the battery packThe vector formed by the voltages.
Training angle variance model data to obtain a quantitative relation: mean value PolCos for calculating cosine set of included angle between any two monomer polarization voltagesaveIf, if
The battery health status is good; if it is
The health state is sub-healthy, but alarm and early warning are not carried out, and only important monitoring is carried out.
If | Zpoli, j | > 2 and Di≥D98And if the condition is met, alarming and early warning are carried out.
If | Zpoli, j | > 2 and
and if the condition is met, alarming and early warning are carried out.
If D isi≥D98And
and if the condition is met, alarming and early warning are carried out.
If | Zpoli, j | > 2, Di≥D98And
and if the condition is met, alarming and early warning are carried out.
The battery health state diagnosis database establishing module comprises: and (3) carrying out big data model training on the vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database.
The battery health state diagnosis database comprises a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database and a battery aging diagnosis database.
The early warning processing module: and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
And if the preset conditions of the battery health state diagnosis database are met, timely alarming is carried out, battery diagnosis result data are sent to relevant personnel in the forms of cloud, mails or short messages and the like, and the relevant personnel correspondingly process the early warning vehicle.
In summary, the device for estimating the abnormality of the single automobile battery provided by the invention has the advantages that: the device is suitable for all types of electric automobiles, only needs the relevant data of the electric automobiles, and based on the voltage data of the battery monomer extracted in the real-time running process of the electric automobiles, a polarization voltage calculation method is applied, the polarization voltage obtained by calculation is used as a parameter, then a Z fraction model, a statistical model and an angle variance model are applied to carry out real-time monitoring on the health state of the battery, and the abnormal monomer in the battery pack is identified, so that early warning is achieved.
The invention can find the abnormal problem of the power battery system in advance, give early warning before the safety problem and the fault occur, can effectively reduce the occurrence of safety accidents such as the fire and the explosion of the electric automobile, and avoid the huge economic loss of life and property. In addition, the vehicle enterprises can know vehicles which are likely to break down in advance through the early warning information, the broken batteries are replaced in advance, the satisfaction degree of customers to the vehicle enterprises is increased while the after-sale service quality is improved, and the vehicle enterprises have a great promoting effect on increasing sales volume and market share.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. The method for estimating the abnormity of the single automobile battery is characterized by comprising the following steps of:
monitoring and extracting battery use data in real time when a vehicle is started and flamed out, and processing abnormal and invalid data in the extracted battery use data, wherein the battery use data comprises time, a current value, a total voltage value, a single battery voltage value and a single battery serial number;
calculating the polarization voltage U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2
Wherein, Ut1、Ut2Battery cell terminal voltages corresponding to times t1 and t2 respectively;
respectively calculating and analyzing a polarization voltage Z fractional value of the battery monomer, an accumulated deviation value of the polarization voltage of the battery monomer and a cosine set variance value of an included angle between any two battery monomer polarization voltages by using a Z fractional model, a statistical model and an angle variance model;
carrying out big data model training on vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database;
and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
2. The method for predicting the abnormality of the battery cell of the vehicle as claimed in claim 1, wherein in the step of calculating and analyzing the Z-fraction value of the polarization voltage of the battery cell by using a Z-fraction model, the Z-fraction model is calculated as follows:
wherein, Zpoli，jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σpolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
3. The method for estimating the abnormality of the battery cell of the vehicle as claimed in claim 1, wherein in the step of calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, the statistical model is calculated as follows:
wherein, HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
4. The method for estimating the abnormality of the battery cells of the vehicle as claimed in claim 1, wherein in the step of calculating and analyzing the variance value of the cosine set of the included angle between the polarization voltages of any two battery cells by using an angle variance model, the calculation formula of the angle variance model is as follows:
in the formula (4), PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
the vector is formed by the polarization voltages of any two single batteries in the battery pack.
5. The method for estimating the abnormality of the single automobile battery according to claim 1, wherein the battery health state diagnosis database includes a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database, and a battery aging diagnosis database.
6. An automobile battery cell abnormality prediction device is characterized by comprising:
a data extraction module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring battery use data of a vehicle when the vehicle is started and flameout in real time, and processing abnormal and invalid data in the acquired battery use data, and the battery use data comprises time, a current value, a total voltage value, a single battery voltage value and a single battery serial number;
a polarization voltage calculation module: for calculating the polarization voltage value U of any battery cell by adopting the following formulapol：
Upol＝Ut1-Ut2
Wherein, Ut1、Ut2Battery cell terminal voltages corresponding to times t1 and t2 respectively;
a model analysis module: the system comprises a Z-fraction model, a statistical model and an angle variance model, wherein the Z-fraction model, the statistical model and the angle variance model are used for respectively calculating and analyzing a polarization voltage Z-fraction value of a battery monomer, an accumulated deviation value of polarization voltages of the battery monomer and a cosine set variance value of an included angle between any two polarization voltages of the battery monomer;
the battery health state diagnosis database establishing module comprises: carrying out big data model training on vehicles with different types of faults and normal vehicles to obtain the relationship between the battery health state represented by the polarization voltage characteristics under the evaluation of each model and the abnormal quantification, and establishing a battery health state diagnosis database;
the early warning processing module: and judging whether the battery reaches the preset condition of the battery health state diagnosis database according to the polarization voltage Z fraction value, the accumulated deviation value of the polarization voltages of the battery monomers and the cosine set variance value of the included angle between any two polarization voltages of the battery monomers so as to obtain a battery diagnosis result, wherein the battery diagnosis result data comprises risk levels, fault types, abnormal monomers and recommended measures.
7. The device for estimating abnormality of vehicle battery cell according to claim 6, wherein the model analysis module includes:
z score model analysis module: the Z-fraction model is used for calculating and analyzing a polarization voltage Z-fraction value of the battery cell, and the calculation formula of the Z-fraction model is as follows:
wherein, Zpoli，jIs the polarization voltage Z fraction of monomer j at time i, Upolave、σoolThe mean and standard deviation of the polarization voltage of each monomer at time i are respectively.
8. The device for estimating abnormality of vehicle battery cell according to claim 6, wherein the model analysis module further includes:
a statistical model analysis module: the method is used for calculating and analyzing the accumulated deviation value of the polarization voltage of the battery cell by using a statistical model, and the calculation formula of the statistical model is as follows:
wherein, HpoliIs the accumulated deviation of the polarization voltage of the No. i single battery in the time period from t1 to t2, UoltThe total polarization voltage of the battery system at the moment t, and n is the number of the single batteries.
9. The device for estimating abnormality of vehicle battery cell according to claim 6, wherein the model analysis module further includes:
an angle variance model analysis module: the method is used for calculating and analyzing the cosine set variance value of the included angle between the polarization voltages of any two battery monomers by using an angle variance model, and the calculation formula of the angle variance model is as follows:
wherein, PolR is the variance of cosine set of included angle between any two monomer polarization voltages, CalVar is the variance,
and
the vector is formed by the polarization voltages of any two single batteries in the battery pack.
10. The device for estimating abnormality of vehicle battery cell as claimed in claim 6, wherein the battery health state diagnosis database includes a battery under-voltage diagnosis database, a battery over-voltage diagnosis database, a battery internal short circuit diagnosis database, a battery temperature difference diagnosis database, and a battery aging diagnosis database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011388995.5A CN112526376A (en) | 2020-12-01 | 2020-12-01 | Method and device for estimating abnormity of single automobile battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011388995.5A CN112526376A (en) | 2020-12-01 | 2020-12-01 | Method and device for estimating abnormity of single automobile battery |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112526376A true CN112526376A (en) | 2021-03-19 |
Family
ID=74996131
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011388995.5A Pending CN112526376A (en) | 2020-12-01 | 2020-12-01 | Method and device for estimating abnormity of single automobile battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112526376A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435688A (en) * | 2021-05-14 | 2021-09-24 | 天津力神电池股份有限公司 | Risk checking method for power battery system |
| CN114062943A (en) * | 2021-10-21 | 2022-02-18 | 合肥国轩高科动力能源有限公司 | Lithium ion battery system polarization abnormity early warning method and system |
| CN114312319A (en) * | 2021-12-15 | 2022-04-12 | 重庆长安新能源汽车科技有限公司 | Battery safety monitoring method based on voltage accumulated value, storage medium and vehicle |
| CN114487888A (en) * | 2021-12-30 | 2022-05-13 | 国联汽车动力电池研究院有限责任公司 | Abnormal battery screening and monitoring method for vehicle battery system |
| CN114583301A (en) * | 2022-04-29 | 2022-06-03 | 国网浙江省电力有限公司电力科学研究院 | Power station thermal runaway early warning method and system based on safety characteristic parameter representation system |
| WO2023201532A1 (en) * | 2022-04-19 | 2023-10-26 | 宁德时代新能源科技股份有限公司 | Abnormal battery cell identification method and apparatus, electronic device, and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106515480A (en) * | 2016-11-08 | 2017-03-22 | 彩虹无线（北京）新技术有限公司 | Automobile storage battery intelligent monitoring method based on car networking big data technology |
| CN110018425A (en) * | 2019-04-10 | 2019-07-16 | 北京理工大学 | A kind of power battery fault diagnosis method and system |
| CN111044909A (en) * | 2019-12-31 | 2020-04-21 | 北京理工大学 | Battery state prediction method and device |
| US20200371163A1 (en) * | 2019-05-24 | 2020-11-26 | Contemporary Amperex Technology Co., Limited | Method and apparatus for correcting soc, battery management system and storage medium |
-
2020
- 2020-12-01 CN CN202011388995.5A patent/CN112526376A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106515480A (en) * | 2016-11-08 | 2017-03-22 | 彩虹无线（北京）新技术有限公司 | Automobile storage battery intelligent monitoring method based on car networking big data technology |
| CN110018425A (en) * | 2019-04-10 | 2019-07-16 | 北京理工大学 | A kind of power battery fault diagnosis method and system |
| US20200371163A1 (en) * | 2019-05-24 | 2020-11-26 | Contemporary Amperex Technology Co., Limited | Method and apparatus for correcting soc, battery management system and storage medium |
| CN111044909A (en) * | 2019-12-31 | 2020-04-21 | 北京理工大学 | Battery state prediction method and device |
Non-Patent Citations (1)
| Title |
|---|
| 高迪驹 等: "基于 Petri 网的锂电池故障诊断系统", 《系统仿真学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435688A (en) * | 2021-05-14 | 2021-09-24 | 天津力神电池股份有限公司 | Risk checking method for power battery system |
| CN114062943A (en) * | 2021-10-21 | 2022-02-18 | 合肥国轩高科动力能源有限公司 | Lithium ion battery system polarization abnormity early warning method and system |
| CN114062943B (en) * | 2021-10-21 | 2024-02-09 | 合肥国轩高科动力能源有限公司 | Polarization abnormality early warning method and system for lithium ion battery system |
| CN114312319A (en) * | 2021-12-15 | 2022-04-12 | 重庆长安新能源汽车科技有限公司 | Battery safety monitoring method based on voltage accumulated value, storage medium and vehicle |
| CN114312319B (en) * | 2021-12-15 | 2023-06-02 | 重庆长安新能源汽车科技有限公司 | Battery safety monitoring method based on voltage accumulation value, storage medium and vehicle |
| CN114487888A (en) * | 2021-12-30 | 2022-05-13 | 国联汽车动力电池研究院有限责任公司 | Abnormal battery screening and monitoring method for vehicle battery system |
| WO2023201532A1 (en) * | 2022-04-19 | 2023-10-26 | 宁德时代新能源科技股份有限公司 | Abnormal battery cell identification method and apparatus, electronic device, and storage medium |
| CN114583301A (en) * | 2022-04-29 | 2022-06-03 | 国网浙江省电力有限公司电力科学研究院 | Power station thermal runaway early warning method and system based on safety characteristic parameter representation system |
| CN114583301B (en) * | 2022-04-29 | 2022-08-23 | 国网浙江省电力有限公司电力科学研究院 | Power station thermal runaway early warning method and system based on safety characteristic parameter representation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112526376A (en) | Method and device for estimating abnormity of single automobile battery | |
| CN110018425B (en) | Power battery fault diagnosis method and system | |
| CN112098850B (en) | Lithium ion battery voltage fault diagnosis method and system based on SDO algorithm | |
| CN112394291A (en) | Battery thermal runaway early warning method and device | |
| CN112213657A (en) | Battery early warning method and system | |
| CN112487031B (en) | Single battery fault detection method and device, readable storage medium and electronic equipment | |
| CN112924887A (en) | Battery pack health detection method and device, readable storage medium and electronic equipment | |
| CN113676343A (en) | Method and device for positioning fault source of power communication network | |
| CN112550074A (en) | Safety early warning method and monitoring system for internal temperature of electric vehicle battery | |
| CN116664015A (en) | Intelligent charging pile management system and method thereof | |
| CN116381460A (en) | Power supply operation safety supervision system for vehicle-mounted shelter CT | |
| CN114814691A (en) | Lithium battery system voltage sensor fault diagnosis method based on digital twinning | |
| CN115792634A (en) | Battery monomer voltage sampling fault identification method based on cloud online data | |
| WO2022016862A1 (en) | Method and system for monitoring health status of battery pack | |
| CN113935423A (en) | Power battery fault early warning method and system for coupling fuzzy control rule with Elman neural network | |
| CN111382937A (en) | Safety state evaluation processing method based on FSAE racing car | |
| US20220216700A1 (en) | Methods and systems for detecting faulty behavior in a battery | |
| CN113567870A (en) | Battery safety early warning system and method based on edge calculation | |
| CN113572161A (en) | Online state monitoring and evaluating method for energy storage system of disaster-resistant bottom-protecting power grid | |
| CN117970127B (en) | Method and apparatus for identifying abnormal self-discharge of power battery | |
| CN115575846B (en) | Method, device, equipment and medium for detecting health state of battery pack | |
| Xiong et al. | A novel thermal runaway warning method of lithium-ion batteries | |
| CN111766531B (en) | Accumulated risk modeling method for judging voltage consistency of battery pack | |
| CN117367830B (en) | New energy vehicle health state business inspection platform and method | |
| CN116299019B (en) | Lithium battery data acquisition abnormality identification method, system, storage medium and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| AD01 | Patent right deemed abandoned |
Effective date of abandoning: 20240524 |
|
| AD01 | Patent right deemed abandoned |