CN105912807A - Modeling method of power battery pack - Google Patents
Modeling method of power battery pack Download PDFInfo
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- CN105912807A CN105912807A CN201610278961.8A CN201610278961A CN105912807A CN 105912807 A CN105912807 A CN 105912807A CN 201610278961 A CN201610278961 A CN 201610278961A CN 105912807 A CN105912807 A CN 105912807A
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- battery core
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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Abstract
The invention discloses a modeling method of a power battery pack. The modeling method comprises the following steps: selecting a plurality of monomer cells from monomer cells to be grouped to carry out a cell parameter distinguishing experiment, and obtaining the characteristic parameter of each monomer cell; carrying out normal distribution statistics on the characteristic parameter obtained by the cell parameter distinguishing experiment; and according to a normal distribution statistics result, generating a corresponding normal distribution monomer cell parameter for the monomer cells to be grouped, and establishing a battery pack model according to the generated monomer cell parameter. The modeling method does not need to carry out the cell parameter distinguishing experiment on each cell to be grouped, greatly reduces experiment cost, simultaneously greatly reduces a characteristic parameter calculated amount and is high in modeling efficiency.
Description
Technical field
The present invention relates to power battery technology field, more specifically, relate to building of power battery pack
Mould method.
Background technology
In large-scale energy storage field or electric automobiles, need by by multiple monomer battery cores with certain
Series-parallel system is attached, and composition has the power battery pack of big output capacity, high output voltage.
But there is certain inconsistency during producing and using in monomer battery core, and the differing of monomer battery core
Cause property is along with monomer battery core uses the time and recycles number of times, and the inconsistency of monomer battery core constantly adds
Greatly, therefore power battery pack partial monosomy battery core existence in charge and discharge process overcharges or crosses the hidden danger put.
In order to ensure electrokinetic cell security in use, needed before reality is applied power electric
Pond group is modeled and tests, in order to the battery management system for power battery pack formulates corresponding management
Strategy.In prior art, traditional power battery pack when power battery pack is modeled, is used to model
Method: Thevenin equivalent-circuit model based on monomer battery core is combined.Described Thevenin
Equivalent-circuit model: a kind of use component built-up circuit network such as constant pressure source, resistance simulates reality
Border battery core characteristic.The method needs to do the experiment of battery core parameter recognition for each monomer battery core parameter, along with
The increase of power battery pack battery core number, required calculating resource will increase considerably, therefore at power electric
The situation that pond group battery core number is more, the method amount of calculation is bigger, it is difficult to application.
Described battery core parameter recognition is tested: refer to by monomer battery core volume test, OCV-SOC song
The series of experiments such as line test, HPPC internal resistance volume test, draw the capacity Q of monomer battery core, open
Road voltage UOCV, battery core ohmic internal resistance RΩ, battery core polarization resistance Rp, electric capacity CPEtc. parameter, its
Middle Rp、CPNeed to try to achieve according to series of complex computing according to experiment is several.
Summary of the invention
It is an object of the invention to overcome drawbacks described above of the prior art, it is provided that a kind of amount of calculation is little,
The modeling method of the power battery pack that efficiency is high.
For achieving the above object, the technical scheme that the present invention provides is as follows:
The invention provides the modeling method of power battery pack, the method comprises the following steps:
In treating groups of monomer battery core, choose several monomer battery cores carry out battery core parameter recognition experiment,
Obtain the characterisitic parameter of each monomer battery core；
The characterisitic parameter obtaining the experiment of battery core parameter recognition carries out normal distribution statistical；
It is to treat the list of the corresponding normal distribution of monomer battery core generation in groups according to normal distribution statistical result
Body battery core parameter, the monomer battery core parameter according to being generated sets up battery pack model.
Preferably, the characterisitic parameter of the described monomer battery core to choosing carries out normal distribution statistical
Step be: average A of the characterisitic parameter of monomer battery core when calculating different SOCIAnd standard deviation BJ,
And according to average AIAnd standard deviation BJGenerate corresponding normal distribution.
Preferably, the described average calculating different monomer battery core characterisitic parameter corresponding for SOC
AIAnd standard deviation BJStep be: calculate SOC characterisitic parameter of monomer battery core when being 0.1*n respectively
Average AIAnd standard deviation BJ, it is wherein the integer of 1 to 10.
Preferably, described treating that groups of monomer battery core is for a batch of monomer battery core.
Preferably, the mode of several monomer battery cores is chosen described in for randomly selecting.
Preferably, the characterisitic parameter of described monomer battery core includes capacity Q.
Preferably, the characterisitic parameter of described monomer battery core includes open-circuit voltage UOCV。
Preferably, the characterisitic parameter of described monomer battery core includes battery core ohmic internal resistance RΩ。
Preferably, the characterisitic parameter of described monomer battery core includes battery core polarization resistance Rp。
Preferably, the characterisitic parameter of described monomer battery core includes electric capacity CP。
Compared with prior art, the beneficial effects of the present invention is: by treating groups of monomer battery core
In choose several monomer battery cores and carry out the experiment of battery core parameter recognition and obtain the characterisitic parameter of monomer battery core
And characterisitic parameter is carried out normal distribution statistical, generate corresponding monomer according to normal distribution statistical result
Battery core parameter, it is not necessary to treat that groups of battery core carries out battery core parameter recognition experiment to each, be substantially reduced
Experimental cost, is greatly reduced characterisitic parameter amount of calculation simultaneously, and modeling efficiency is high.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below by right
In embodiment or description of the prior art, the required accompanying drawing used is briefly described, it should be apparent that,
Accompanying drawing in describing below is some embodiments of the present invention, for those of ordinary skill in the art,
On the premise of not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the modeling method flow chart of the power battery pack of the embodiment of the present invention one；
Fig. 2 is the modeling method flow chart of the power battery pack of the embodiment of the present invention two.
Detailed description of the invention
For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below in conjunction with this
Accompanying drawing in inventive embodiments, clearly and completely retouches the technical scheme in the embodiment of the present invention
State, it is clear that described embodiment is a part of embodiment of the present invention rather than whole embodiments.
Based on the embodiment in the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, broadly falls into the scope of protection of the invention.
Embodiment one
Embodiments of the invention one provide the modeling method of a kind of power battery pack, and the method process is such as
Under:
Step 11: according to treating the quantity of groups of battery pack demand monomer battery core, with a batch of list
Body battery core is chosen several monomer battery cores and carries out battery core parameter recognition experiment, obtain each monomer battery core
Characterisitic parameter.
Step 12: the characterisitic parameter obtaining the experiment of battery core parameter recognition carries out normal distribution statistical.
Step 13: divide for treating that monomer battery core generates corresponding normal state in groups according to normal distribution statistical result
The monomer battery core parameter of cloth.
Step 14: the monomer battery core parameter according to being generated sets up battery pack model.
The present invention carries out battery core parameter recognition experiment by choosing several monomer battery cores, to battery core parameter
Distinguish that the characterisitic parameter that experiment obtains carries out normal distribution statistical, and according to normal distribution statistical result be
Treat the monomer battery core parameter of the corresponding normal distribution of monomer battery core generation in groups, it is not necessary to treat into each
The battery core of group carries out battery core parameter recognition experiment, is substantially reduced experimental cost, is greatly reduced characteristic simultaneously
Parameter amount of calculation, modeling efficiency is high.
Embodiment two
Embodiments of the invention two provide the modeling method of a kind of power battery pack, are in embodiment one
Basis on the improvement that carries out.
A kind of modeling method of power battery pack, the method process is as follows:
Step 21: according to treating the quantity of groups of battery pack demand monomer battery core, with a batch of list
Body battery core randomly selects several monomer battery cores and carries out battery core parameter recognition experiment, obtain each monomer
The characterisitic parameter of battery core.The quantity wherein choosing the monomer battery core carrying out battery core parameter recognition experiment can root
Determine according to the quantity of monomer battery core needed for treating groups of battery pack, needed for according to treating groups of battery pack
The quantity of monomer battery core chooses the number of the monomer battery core doing the experiment of battery core parameter recognition according to a certain percentage
Amount.As the quantity of monomer battery core is N number of needed for treating groups of battery pack, M=" N/4 " can be chosen individual
Monomer battery core carries out battery core parameter recognition experiment.The characterisitic parameter of described monomer battery core includes different monomers
Capacity Q, open-circuit voltage U corresponding at battery core difference SOC pointOCV, battery core ohmic internal resistance RΩ, electricity
Core polarization internal resistance RpAnd electric capacity CP.Described SOC can be 0.1*n, wherein, n={1,2,3 ...
10}, i.e. n are the integer of 1 to 10, and choosing of SOC point can be chosen according to actual needs.Then
At each SOC point, correspondence M monomer battery core obtains characterisitic parameter through the experiment of battery core parameter recognition is:
Q={Q (1), Q (2) ... Q (M) }；UOCV={ UOCV(1)、UOCV(2)……
UOCV(M)}；RΩ={ RΩ(1)、RΩ(2)……RΩ(M)}；Rp={ Rp(1)、
Rp(2)……Rp(M)}；CP={ CP(1)、CP(2)……CP(M)}。
Step 22: M selected monomer battery core is carried out after battery core parameter recognition experiment battery core
The characterisitic parameter that parameter recognition experiment obtains carries out normal distribution statistical, and i.e. calculating SOC is at 0.1*n
Average A of the characterisitic parameter of the monomer battery core being selectedIAnd standard deviation BJ, and according to average AIAnd mark
Quasi-difference BJGenerate corresponding normal distribution, wherein, n={1,2,3 ... 10}.
Step 23: corresponding for treating that groups of N number of monomer battery core generates according to normal distribution statistical result
The monomer battery core parameter of normal distribution.
Step 24: the monomer battery core parameter according to being generated sets up battery pack model.
One of ordinary skill in the art will appreciate that realize in above-described embodiment method all or part of
Step can be by program and completes to instruct relevant hardware, and described program can be stored in
In one computer read/write memory medium, described storage medium, such as ROM/RAM, disk, light
Dish etc..
Above-described embodiment is the present invention preferably embodiment, but embodiments of the present invention are not by upper
Stating the restriction of embodiment, that is made under other any Spirit Essence without departing from the present invention and principle changes
Become, modify, substitute, combine, simplify, all should be the substitute mode of equivalence, be included in the present invention
Protection domain within.
Claims (10)
1. the modeling method of power battery pack, it is characterised in that the method comprises the following steps:
In treating groups of monomer battery core, choose several monomer battery cores carry out battery core parameter recognition experiment,
Obtain the characterisitic parameter of each monomer battery core；
The characterisitic parameter obtaining the experiment of battery core parameter recognition carries out normal distribution statistical；
It is to treat the list of the corresponding normal distribution of monomer battery core generation in groups according to normal distribution statistical result
Body battery core parameter, the monomer battery core parameter according to being generated sets up battery pack model.
The modeling method of power battery pack the most according to claim 1, it is characterised in that institute
State the characterisitic parameter to the monomer battery core chosen and carry out the step of normal distribution statistical and be: calculate difference
Average A of the characterisitic parameter of monomer battery core during SOCIAnd standard deviation BJ, and according to average AIAnd standard
Difference BJGenerate corresponding normal distribution.
The modeling method of power battery pack the most according to claim 2, it is characterised in that institute
State average A calculating different monomer battery core characterisitic parameters corresponding for SOCIAnd standard deviation BJStep be:
Calculate average A of the characterisitic parameter of monomer battery core when SOC is 0.1*n respectivelyIAnd standard deviation BJ, its
In, n is the integer of 1 to 10.
The modeling method of power battery pack the most according to claim 1, it is characterised in that institute
State and treating that groups of monomer battery core is for a batch of monomer battery core.
The modeling method of power battery pack the most according to claim 1, it is characterised in that institute
State the mode choosing several monomer battery cores for randomly selecting.
The modeling method of power battery pack the most according to claim 1, it is characterised in that: institute
The characterisitic parameter stating monomer battery core includes capacity Q.
The modeling method of power battery pack the most according to claim 1, it is characterised in that: institute
The characterisitic parameter stating monomer battery core includes open-circuit voltage UOCV。
The modeling method of power battery pack the most according to claim 1, it is characterised in that: institute
The characterisitic parameter stating monomer battery core includes battery core ohmic internal resistance RΩ。
The modeling method of power battery pack the most according to claim 1, it is characterised in that: institute
The characterisitic parameter stating monomer battery core includes battery core polarization resistance Rp。
The modeling method of power battery pack the most according to claim 1, it is characterised in that: institute
The characterisitic parameter stating monomer battery core includes electric capacity CP。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109031145A (en) * | 2018-08-10 | 2018-12-18 | 山东大学 | A kind of series-parallel battery pack model and implementation method considering inconsistency |
| CN110806540A (en) * | 2018-08-01 | 2020-02-18 | 广州汽车集团股份有限公司 | Battery cell test data processing method, device and system and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104793146A (en) * | 2015-04-21 | 2015-07-22 | 上海科梁信息工程有限公司 | Battery pack simulation work method and system |
| CN105510845A (en) * | 2016-01-11 | 2016-04-20 | 北京北交新能科技有限公司 | Method for analyzing burn-in path dependence of lithium-ion battery |
| CN105510847A (en) * | 2016-01-20 | 2016-04-20 | 四川长虹电器股份有限公司 | Method for screening consistency of lithium ion batteries |
-
2016
- 2016-04-29 CN CN201610278961.8A patent/CN105912807A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104793146A (en) * | 2015-04-21 | 2015-07-22 | 上海科梁信息工程有限公司 | Battery pack simulation work method and system |
| CN105510845A (en) * | 2016-01-11 | 2016-04-20 | 北京北交新能科技有限公司 | Method for analyzing burn-in path dependence of lithium-ion battery |
| CN105510847A (en) * | 2016-01-20 | 2016-04-20 | 四川长虹电器股份有限公司 | Method for screening consistency of lithium ion batteries |
Non-Patent Citations (4)
| Title |
|---|
| 姜久春 等: ""纯电动汽车用锂离子电池的建模和模型参数识别"", 《电力科学与技术学报》 * |
| 张陈斌 等: ""基于统计模型的动力电池静态配组过程分析"", 《第13届中国系统仿真技术及其应用学术年会论文集》 * |
| 李香龙 等: ""梯次利用锂离子动力电池试验特性分析"", 《电源技术》 * |
| 杨帆: ""锂离子电池组不一致性及其弥补措施"", 《汽车电器》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110806540A (en) * | 2018-08-01 | 2020-02-18 | 广州汽车集团股份有限公司 | Battery cell test data processing method, device and system and storage medium |
| CN110806540B (en) * | 2018-08-01 | 2021-03-19 | 广州汽车集团股份有限公司 | Battery cell test data processing method, device and system and storage medium |
| CN109031145A (en) * | 2018-08-10 | 2018-12-18 | 山东大学 | A kind of series-parallel battery pack model and implementation method considering inconsistency |
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Application publication date: 20160831 |