CN106546924A - A kind of dynamic prediction method of automobile lithium battery performance - Google Patents
A kind of dynamic prediction method of automobile lithium battery performance Download PDFInfo
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- CN106546924A CN106546924A CN201610877185.3A CN201610877185A CN106546924A CN 106546924 A CN106546924 A CN 106546924A CN 201610877185 A CN201610877185 A CN 201610877185A CN 106546924 A CN106546924 A CN 106546924A
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- G—PHYSICS
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- 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]
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention discloses a kind of dynamic prediction method of automobile lithium battery performance, the method sends lithium battery internal resistance value to server end by data acquisition, lithium battery internal resistance of the service end to the vehicle is analyzed, the performance of the lithium battery may determine that by the analysis of multi-group data, so as to be used for the lithium battery performance prediction of the same brand of large-scale electric automobile.Lithium battery internal resistance of the present invention by monitoring electric automobile, the performance of prediction lithium battery, the internal resistance data of lithium battery are obtained daily at every moment, once note abnormalities and can go immediately to overhaul or stop using the vehicle, avoiding lithium battery performance issue occur in advance causes vehicle to go wrong using in.
Description
Technical field
The invention belongs to electric vehicle engineering field, and in particular to a kind of dynamic prediction method of automobile lithium battery performance.
Background technology
Automobile is the important vehicles of people's life, and with the improvement of people's living standards, increasing people starts
Purchase automobile.But, a large amount of use of automobile brings a series of problems, such as energy resource consumption, shortage of resources, environmental pollution, these
Problem promotes Ge great motor corporations competitively to develop the environment-friendly vehicle of various Novel pollution-frees.And electric automobile be with electric energy as the energy,
Mechanical energy is converted electrical energy into by motor, this complies fully with the theory for developing no pollution automobile, therefore electric automobile is obtained
Quick development.
The development of electric automobile be unable to do without the innovation of battery, has the battery of mentioning electric automobile in many articles
The current problems faced of electric automobile such as endurance, charging inconvenience；Large manufacturer is also in the electricity for constantly reforming electric automobile
The problem of pond quality and capacitance.For lithium battery, there are many method for testing performance and system, such as in lithium battery system
The static internal resistance of lithium battery and quiescent voltage are detected in multiple links during making, to set of cells after burn-in test
The unloaded total voltage and band of string carries total voltage to be carried out the method and system such as detecting.
Now many local business for all occurring in that lease electric automobile, such as place such as park, school, recreation ground, very
Even there is a set of rent-a-car system in many cities, use for civic lease at ordinary times.Intelligent Radix Raphani car leasing system is exactly one
The leasing system based on the Internet is planted, intelligent carriage is leased by mobile phone, the input of a large amount of vehicles uses and can cause to safeguard
Cost uprises, maintenance dynamics die down.It cannot be guaranteed that the lithium battery performance in each lease electric automobile, manages the huge rent of data
Rent electric automobile, need detection in real time, prediction pinpoint the problems in time, remind attendant's maintenance or change lithium electricity
Pond, avoiding lithium battery performance issue occur in advance causes vehicle to go wrong using in.Attendant is obtained in real time by mobile phone A PP
The battery performance of pick-up, note abnormalities vehicle and its position in time, it is to avoid failure in traveling occurs in Radix Raphani car.
The content of the invention
In view of the demand, the invention provides a kind of dynamic prediction method of automobile lithium battery performance, it is right to realize
The real-time detection of lithium battery performance, prediction, remind attendant's maintenance or change lithium battery, avoid lithium battery appearance property in advance
Energy problem causes vehicle to go wrong using in.
A kind of dynamic prediction method of automobile lithium battery performance, its process is：
First, the automobile lithium battery dynamic internal resistance average data of a large amount of same brand models is collected, it is pre- by respective algorithms
Survey internal resistance interval normal range and its error ratio of the brand and model automobile lithium battery；
For automobile lithium battery to be predicted, its internal resistance meansigma methods is calculated every the set time；Collect the lithium battery one day
Interior all internal resistance average datas, judge the property of the lithium battery according to interval above-mentioned internal resistance normal range and its error ratio
Energy.
The dynamic internal resistance average data of the automobile lithium battery is made up of a series of continuous internal resistance meansigma methodss.
The circular of the internal resistance meansigma methodss is as follows：
(1) voltage and discharge current for obtaining automobile lithium battery is gathered every 50ms；
(2) based on current time and the voltage and discharge current of upper moment automobile lithium battery, calculate current time vapour
The internal resistance of car lithium battery is simultaneously preserved；
(3) all internal resistances to preserving before are averaging, that is, obtain described internal resistance meansigma methodss.
The internal resistance of current time automobile lithium battery is calculated in the step (2) according to below equation：
RCurrent time=(UCurrent time-UA upper moment)/(ICurrent time-IA upper moment)
Wherein：RCurrent timeFor the internal resistance of current time automobile lithium battery, UCurrent timeAnd UA upper momentRespectively current time and upper one
The voltage of moment automobile lithium battery, ICurrent timeAnd IA upper momentThe electric discharge of respectively current time and upper moment automobile lithium battery is electric
Stream.
Before execution step (2), judge that the discharge current of automobile lithium battery then judges to work as whether less than 1 milliampere, if so,
Vehicle in front remains static, and internal resistance is not calculated.
It is responsible for predicting internal resistance interval normal range of automobile lithium battery and its error ratio by far-end server；As car
, then the internal resistance meansigma methodss of automobile lithium battery are calculated every 10s and its unique identification information with the lithium battery is together uploaded
To described far-end server.
For arbitrary automobile lithium battery, the intraday all internal resistance meansigma methodss of the automobile lithium battery are collected by far-end server
Data, according to internal resistance normal range is interval and its error ratio judges the performance of the lithium battery, and then by corresponding judged result
It is sent to correspondence vehicle management personnel.
Forecasting Methodology of the present invention sends lithium battery internal resistance value to server end by data acquisition, and service end is to the vehicle
Lithium battery internal resistance is analyzed, and may determine that the performance of the lithium battery by the analysis of multi-group data, big so as to be used for
The lithium battery performance prediction of the same brand of the electric automobile of scale.
Lithium battery internal resistance of the present invention by monitoring electric automobile, predicts the performance of lithium battery, at every moment obtains daily
The internal resistance data of lithium battery, once note abnormalities and can go immediately to overhaul or stop using the vehicle, avoid lithium battery in advance
Performance issue occur causes vehicle to go wrong using in.
Description of the drawings
The step of Fig. 1 is the inventive method schematic flow sheet.
Fig. 2 is the internal resistance schematic diagram data that normal lithium battery is acquired.
Fig. 3 is the internal resistance schematic diagram data that abnormal lithium battery is acquired.
In Fig. 2 and Fig. 3, abscissa is the number of times () of measurement per 10s once, and vertical coordinate is the internal resistance value of measurement.
Specific embodiment
In order to more specifically describe the present invention, below in conjunction with the accompanying drawings and specific embodiment is to technical scheme
It is described in detail.
The dynamic prediction method of automobile lithium battery performance of the present invention, primarily to solving the real-time detection of lithium battery performance
And prediction, to remind attendant's maintenance or change lithium battery, which implements as shown in Figure 1：
(1) the voltage U and current discharge current I of current lithium battery are obtained every 50ms.
(2) discharge current I being judged less than 1 milliampere, then assert Current vehicle in resting state, the data are not calculated.
(3) voltage difference by this with last time calculates the internal resistance R of the lithium battery in vehicle traveling, tool divided by difference between current
Body formula is as follows：
R=(U1-U2)/(I1-I2)
Wherein：U1 is the voltage of this measurement, and I1 is the discharge current of this measurement, the voltage that U2 last time measures, and I2 is
The discharge current of last time measurement.
(4) meansigma methodss R' of the internal resistance R obtained before calculating per 10s.
(5) information such as the internal resistance R' for calculating this and Vehicular battery unique mark uploads onto the server.
(6) the internal resistance multi-group data for collecting multiple dynamic lithium batteries according to step (1) to (5) is uploaded to remote service
Device, is analyzed to data traversal by far-end server and predicts that this kind of lithium battery internal resistance normal range value exists by respective algorithms
[R1, R2], and there are the probability η % of error.
Algorithm employed in present embodiment is：Collect normal data fit one kind to be just distributed very much, make more than 95%
Data all can be in the range of [+2 σ of μ -2 σ, μ], then R1=μ -2 σ ,+2 σ of R2=μ, wherein η=5, μ are the average of one group of data,
σ is standard deviation.
(7) for lithium battery to be predicted, repeat to be calculated according to step (1) to (5) and collect the lithium battery one
It internal resistance value.
(8) one day internal resistance Value Data of the lithium battery is analyzed by far-end server, the lithium battery internal resistance value is judged by algorithm
It is whether interior in normal range [R1, R2], and the probability of error is less than or equal to η %, and then judge to draw the performance of the lithium battery.
Algorithm employed in present embodiment is：One group of internal resistance Value Data is traveled through, judges internal resistance value whether at [R1, R2]
Between, whole group data number not between [R1, R2] is counted, probability η of this group of data not between [R1, R2] is obtained1%.
Judge η1Whether % is less than or equal to η %, represents that the lithium battery performance is normal if less than or equal to η %, otherwise abnormal.
Present embodiment statistical computation draw this kind of lithium battery under normal circumstances internal resistance value normal data scope [-
0.177487, -0.032503], η=5.
Fig. 2 is the internal resistance data that normal lithium battery is acquired, and wherein internal resistance Value Data is not in the number of normal data scope
It is 3.7% that strong point accounts for the ratio of total data, less than 5%, illustrates that the lithium battery performance is normal.Fig. 3 is obtained for abnormal lithium battery
The ratio that the internal resistance data for arriving, wherein internal resistance Value Data do not account for total data in the data point of normal data scope is 13.06%, far
Much larger than 5%, the lithium battery property abnormality is illustrated, need to overhaul in time.
Forecasting Methodology of the present invention sends lithium battery internal resistance value to server end by data acquisition, and service end is to the vehicle
Lithium battery internal resistance is analyzed, and may determine that the performance of the lithium battery by the analysis of multi-group data, big so as to be used for
The lithium battery performance prediction of the same brand of the electric automobile of scale.
Lithium battery internal resistance of the present invention by monitoring electric automobile, predicts the performance of lithium battery, at every moment obtains daily
The internal resistance data of lithium battery, once note abnormalities and can go immediately to overhaul or stop using the vehicle, avoid lithium battery in advance
Performance issue occur causes vehicle to go wrong using in.
The above-mentioned description to embodiment is to be understood that for ease of those skilled in the art and apply the present invention.
Person skilled in the art obviously easily can make various modifications to above-described embodiment, and described herein general
Principle is applied in other embodiment without through performing creative labour.Therefore, the invention is not restricted to above-described embodiment, ability
Field technique personnel announcement of the invention, the improvement made for the present invention and modification all should be in protection scope of the present invention
Within.
Claims (7)
1. a kind of dynamic prediction method of automobile lithium battery performance, it is characterised in that：
First, the automobile lithium battery dynamic internal resistance average data of a large amount of same brand models is collected, should by respective algorithms prediction
Internal resistance interval normal range of brand and model automobile lithium battery and its error ratio；
For automobile lithium battery to be predicted, its internal resistance meansigma methods is calculated every the set time；Collect the lithium battery intraday
All internal resistance average datas, judge the performance of the lithium battery according to interval above-mentioned internal resistance normal range and its error ratio.
2. dynamic prediction method according to claim 1, it is characterised in that：The dynamic internal resistance of the automobile lithium battery is average
Data are made up of a series of continuous internal resistance meansigma methodss.
3. dynamic prediction method according to claim 1 and 2, it is characterised in that：The concrete calculating of the internal resistance meansigma methodss
Method is as follows：
(1) voltage and discharge current for obtaining automobile lithium battery is gathered every 50ms；
(2) based on current time and the voltage and discharge current of upper moment automobile lithium battery, calculate current time automobile lithium
The internal resistance of battery is simultaneously preserved；
(3) all internal resistances to preserving before are averaging, that is, obtain described internal resistance meansigma methodss.
4. dynamic prediction method according to claim 3, it is characterised in that：According to below equation meter in the step (2)
Calculate the internal resistance of current time automobile lithium battery：
RCurrent time=(UCurrent time-UA upper moment)/(ICurrent time-IA upper moment)
Wherein：RCurrent timeFor the internal resistance of current time automobile lithium battery, UCurrent timeAnd UA upper momentRespectively current time and a upper moment
The voltage of automobile lithium battery, ICurrent timeAnd IA upper momentThe respectively discharge current of current time and upper moment automobile lithium battery.
5. dynamic prediction method according to claim 3, it is characterised in that：Before execution step (2), automobile lithium is judged
Whether the discharge current of battery is less than 1 milliampere, if so, then judges that Current vehicle remains static, and does not calculate to internal resistance.
6. dynamic prediction method according to claim 1, it is characterised in that：It is responsible for predicting automobile lithium electricity by far-end server
Internal resistance interval normal range in pond and its error ratio；As vehicle, then the internal resistance meansigma methodss of automobile lithium battery are calculated every 10s
And its unique identification information with the lithium battery is together uploaded to into described far-end server.
7. dynamic prediction method according to claim 1, it is characterised in that：For arbitrary automobile lithium battery, taken by distal end
Business device collects the intraday all internal resistance average datas of the automobile lithium battery, according to internal resistance interval normal range and its error ratio
Rate judges the performance of the lithium battery, and then corresponding judged result is sent to correspondence vehicle management personnel.
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Cited By (6)
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| CN108051746A (en) * | 2017-11-02 | 2018-05-18 | 福建联迪商用设备有限公司 | A kind of determination methods and terminal of battery normal power supply |
| CN108646188A (en) * | 2018-04-28 | 2018-10-12 | 北京新能源汽车股份有限公司 | A kind of power battery dynamic inner walkway method, apparatus, equipment and automobile |
| CN109490788A (en) * | 2018-12-21 | 2019-03-19 | 国网北京市电力公司 | The prediction technique and device of battery group capacity |
| CN111562508A (en) * | 2020-05-08 | 2020-08-21 | 上海电享信息科技有限公司 | Method for online detecting internal resistance abnormality of single battery in battery pack |
| CN112415401A (en) * | 2020-10-26 | 2021-02-26 | 潍柴动力股份有限公司 | Battery monitoring method, device and equipment applied to vehicle |
| CN113533985A (en) * | 2021-06-28 | 2021-10-22 | 合肥国轩高科动力能源有限公司 | Identification method of battery pack internal resistance abnormal module and storage medium thereof |
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| CN112415401A (en) * | 2020-10-26 | 2021-02-26 | 潍柴动力股份有限公司 | Battery monitoring method, device and equipment applied to vehicle |
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| CN113533985A (en) * | 2021-06-28 | 2021-10-22 | 合肥国轩高科动力能源有限公司 | Identification method of battery pack internal resistance abnormal module and storage medium thereof |
| CN113533985B (en) * | 2021-06-28 | 2024-05-03 | 合肥国轩高科动力能源有限公司 | Identification method of battery pack internal resistance abnormal module and storage medium thereof |
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