CN111597747A - Multipoint-triggering ternary lithium power battery module thermal runaway simulation and prediction method - Google Patents
Multipoint-triggering ternary lithium power battery module thermal runaway simulation and prediction method Download PDFInfo
- Publication number
- CN111597747A CN111597747A CN202010417278.4A CN202010417278A CN111597747A CN 111597747 A CN111597747 A CN 111597747A CN 202010417278 A CN202010417278 A CN 202010417278A CN 111597747 A CN111597747 A CN 111597747A
- Authority
- CN
- China
- Prior art keywords
- thermal runaway
- battery module
- power battery
- trigger
- time
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 28
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 28
- 238000004088 simulation Methods 0.000 title claims abstract description 19
- 230000001960 triggered effect Effects 0.000 claims abstract description 12
- 230000020169 heat generation Effects 0.000 claims abstract description 3
- 239000000178 monomer Substances 0.000 claims description 13
- 238000012544 monitoring process Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 claims description 2
- 238000002474 experimental method Methods 0.000 claims description 2
- 230000001939 inductive effect Effects 0.000 claims description 2
- 238000010187 selection method Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims 1
- 230000000977 initiatory effect Effects 0.000 claims 1
- 238000010606 normalization Methods 0.000 claims 1
- 238000011160 research Methods 0.000 description 7
- 239000000523 sample Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000010438 heat treatment Methods 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000013021 overheating Methods 0.000 description 2
- 238000011076 safety test Methods 0.000 description 2
- SOXUFMZTHZXOGC-UHFFFAOYSA-N [Li].[Mn].[Co].[Ni] Chemical compound [Li].[Mn].[Co].[Ni] SOXUFMZTHZXOGC-UHFFFAOYSA-N 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Images
Classifications
-
- 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
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a multipoint-triggered thermal runaway simulation and prediction method for a ternary lithium power battery module, which comprises the following steps: establishing a three-dimensional model of a power battery module, and selecting a thermal runaway multipoint triggering position in the battery module; the method comprises the steps of introducing a three-dimensional model of a battery module into finite element software, establishing a battery heat generation model for a battery pack according to a heat abuse model, applying a thermal runaway trigger heat source Q to a battery at a selected multi-point trigger position, dividing a battery domain into quadrangles to form a main free grid, setting heat exchange modes among batteries as convection and conduction, and performing thermal runaway simulation to obtain temperature, trigger points, heat sources and thermal runaway time data of the battery module; acquiring the number n of different trigger points and the average value t of thermal runaway time triggered by a thermal runaway trigger heat source Q, fitting a relational expression of the time t, the thermal runaway heat source Q and the number n of the trigger points by a nonlinear least square method, and predicting the thermal runaway time of the battery module; and calculating the overall thermal runaway prediction relation of the battery module, and calculating the overall thermal runaway time of the module.
Description
Technical Field
The invention relates to the technical field of power batteries of electric vehicles, in particular to a multipoint-trigger-based thermal runaway simulation and prediction method for a ternary lithium power battery module.
Background
The power battery is used as a core component of the electric automobile, and the safety of the power battery has an important influence on the development of the electric automobile. The power battery is easy to generate a thermal runaway condition under mechanical abuse, electrical abuse and thermal abuse, which causes serious damage to electric automobiles and personnel safety, and a large amount of heat generated by the mechanical abuse and the electrical abuse can cause the battery temperature to be overhigh, so that the thermal abuse is caused, and further the thermal runaway condition is generated, namely the thermal abuse is a root cause for the thermal runaway of the power battery. The power battery module is used as an important component of a power battery system and has important significance for the research on the safety of the power battery module, so that a simulation and prediction method for generating multi-point triggering on the power battery module under different collision working conditions to cause the thermal runaway condition of the module is needed.
At present, when the thermal runaway safety test is carried out on the power lithium battery in China, a method for carrying out the safety test on a power battery monomer is mostly adopted, although the method can carry out the safety control on the battery, the safety of a power battery module existing in an electric automobile in a module form in practical application is not considered, and the condition that the thermal runaway of the power battery is caused due to the fact that the electric automobile is overheated at multiple points generated when meeting various collision working conditions in practical running is not considered.
1) In a paper published by Liu gently (2018) of the university of fertilizer combining industry, a central point heating mode is adopted for a nickel-cobalt-manganese-lithium ion power battery module with a 3X 3 structure in the research on thermal runaway and expansion characteristics of the battery under a heating condition, and the thermal runaway rule of the power battery module at different intervals and in different heat exchange modes is explored. The battery at the center of the battery module is heated in the literature to cause thermal runaway of the power battery module, although the thermal runaway law among the battery modules can be visually observed by adopting the method, the situation that multiple points in the battery module are overheated when an electric automobile collides and the like in the actual working condition is not considered, and the method is ideal. According to the invention, on the premise of considering different collision working conditions to cause overheating at different positions in the battery module, the ternary lithium battery with high energy density is adopted as a research object to search and research the overall thermal runaway rule of the power battery module, so that the method has more practical applicability, parameters can be flexibly changed according to different battery materials, and the overall thermal runaway time prediction of the power battery module under different collision working conditions is realized.
2) And the patent number CN110911772A, namely an early warning method for thermal runaway of a power lithium ion battery. The invention provides an early warning method for thermal runaway of a power lithium ion battery, which is characterized in that a three-layer power lithium battery thermal runaway warning mechanism is constructed by respectively detecting temperature, smoke and characteristic gas through sensors, and the problems of simple monitoring and warning form and single mode in the prior art are solved. The thermal runaway prediction method provided by the invention adopts simulation as a main means, can be flexibly set according to batteries made of different materials, and has the advantages of low cost and high accuracy.
3) The Changan university Tuaoqiang and Huangqing are published in 2018 in the NCM ternary lithium power battery thermal runaway research on the date 5 of the university of Jia wood, journal of 36, and the article is based on a thermal abuse model to perform thermal runaway behavior research on an NCM ternary lithium square battery monomer. The literature researches the thermal runaway time of the ternary lithium square power battery monomer under different heating furnace temperatures and different heat dissipation conditions, but the considered object is only limited to the battery monomer, the thermal runaway triggering mode of the battery monomer is environmental high-temperature heating, and the battery monomer is uniformly heated. The invention carries out thermal runaway simulation on the NCM ternary lithium power battery module formed by the most common cylindrical batteries on the market, adopts the heat source to accurately heat the battery in the thermal runaway triggering mode, better accords with the condition that the battery is unevenly heated in practical application, considers the heat influence of different heated positions of the battery on the whole module under different collision working conditions, and ensures that the simulation condition better accords with the practical application condition and has higher reliability.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a multipoint-trigger-based ternary lithium power battery module thermal runaway simulation and prediction method.
The purpose of the invention is realized by the following technical scheme:
a thermal runaway simulation and prediction method for a ternary lithium power battery module based on multipoint triggering comprises the following steps:
s1, establishing a three-dimensional model of the power battery module according to the size, shape and arrangement structure of the sample batteries, and selecting a thermal runaway multipoint triggering position in the power battery module according to different collision working conditions;
s2, importing a three-dimensional model of a power battery module into finite element software, establishing a battery heat generation model for the power battery pack according to a heat abuse model, applying a thermal runaway trigger heat source Q to batteries at a selected multi-point trigger position, dividing a battery domain into quadrilateral-based free grids, setting a heat exchange mode among the batteries as convection and conduction, and performing thermal runaway simulation to obtain temperature, trigger points, heat sources and thermal runaway time data of the battery module;
s3 collecting the average value of the time when the thermal runaway of the whole thermal runaway in the module is triggered by the number n of different trigger points and the thermal runaway trigger heat source Q under different collision working conditions
Time is calculated by fitting with a non-linear least square method
Predicting the time when the power battery module is subjected to thermal runaway integrally according to a relational expression of a thermal runaway heat source Q and the number n of trigger points;
s4, calculating the overall thermal runaway time of the power battery module caused by different trigger positions and heat sources under different collision conditions according to the overall thermal runaway prediction relational expression of the power battery module calculated under different collision conditions.
One or more embodiments of the present invention may have the following advantages over the prior art:
the method can simulate the condition that the power battery module generates overall thermal runaway due to multi-point overheating caused by different collision working conditions, and predict the time for the power battery module to generate the overall thermal runaway at the position, thereby providing a reliable basis for a thermal runaway blocking method and safety design of the power battery.
Drawings
FIG. 1 is a flow chart of a method for simulating and predicting thermal runaway of a ternary lithium power battery module based on multi-point triggering;
FIG. 2 is a block diagram of a process of a method for simulating and predicting thermal runaway of a ternary lithium power battery module based on multi-point triggering;
FIG. 3 is a schematic diagram illustrating calculation of thermal runaway trigger point selection in a thermal runaway simulation and prediction method for a ternary lithium power battery module based on multi-point triggering;
FIG. 4 is a three-dimensional schematic view of a ternary lithium power battery module;
FIG. 5 shows that n is 3 and heat source Q is 1e7(W/m3) When t is 0(s) and t is 12(s), the three-dimensional temperature cloud graph of the battery module is obtained;
FIGS. 6a and 6b show that n is 3 and heat source Q is 1e7(W/m3) A time battery module temperature probe diagram;
fig. 7 and 8 are schematic diagrams of fitting data using a non-linear least squares method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
As shown in fig. 1 and fig. 2, a workflow of a multipoint triggered thermal runaway simulation method for a power battery module includes: constructing a three-dimensional model of the power battery module, selecting a multi-point trigger position, constructing the model by using a quadrilateral free grid, and applying heatCalculating a battery thermal field by a source, acquiring data, and performing fitting calculation to obtain the overall thermal runaway time of the power battery module
And (4) a relational expression. The method specifically comprises the following steps:
as shown in fig. 3, a rectangular coordinate system is constructed by the corners of the power battery module, distances from the center of the battery cell to the x axis and the y axis are dl and dh respectively, and when the hot trigger point is selected according to different collision conditions, the battery at the position corresponding to the number n of the trigger points should satisfy:
fig. 4 shows a three-dimensional model of a power battery module constructed for a sample battery ternary lithium cylindrical battery according to the present invention, wherein the battery size values are shown in table 1.
TABLE 1
| Single point pool radius r (unit: mm) | Single point pool height h (unit: mm) | Battery spacing d (unit: mm) |
| 16 | 65 | 10 |
Taking the corner impact condition as an example, when the power battery module is impacted by a corner, it may cause n batteries in the power battery module to generate thermal runaway. When 3 batteries generate thermal runaway, the positions of the 3 batteries respectively meet the requirement according to the thermal runaway position calculation method
Therefore, when n is 3 for battery No. 1, battery No. 2, and battery No. 5 shown in fig. 4, the multi-point thermal trigger battery is selected.
the index for judging the thermal runaway of the power battery is as follows:
the battery monomer: temperature of monitoring point>TCritical temperature；
The battery module: temperature of each cell in the module>TCritical temperature。
The selection method for collecting the temperature position of the battery monomer in the power lithium battery module comprises the following steps:
as shown in fig. 5, the battery is equivalent to a cylinder with a height h and a radius r under a cylindrical coordinate system, and is generated on the surface of the battery by a monte carlo method (h)i，θi) N, where hi∈(0,h)，θi∈ (0,360 degree), let Delta T be the difference between the temperature of the monitoring point and the surface temperature of the battery monomer, Delta T be the difference between the thermal trigger time of the monitoring point and the surface trigger time of the battery monomer, T0For the battery thermal trigger start time, trThe maximum temperature of the battery thermal runaway corresponds to the time.
For ∑ Delta T (h)i,θi)、Δt(hi,θi) Two indexes are normalizedCarrying out standardization treatment:
the two indexes are summed, and the position (h) at i corresponding to the minimum valuei，θi) Namely the positions of the selected battery surface temperature monitoring points:
average value of thermal runaway time of other batteries in module triggered by multipoint thermal runaway
The calculation method is as follows:
defining the time t for the 1 st battery in the power battery module to generate thermal runaway caused by the heat source1The thermal runaway time of the other batteries is t2，t3…tn. The average time for the thermal runaway of the power battery module caused by the thermal runaway battery is as follows:
under the corner striking operating mode, get trigger point number n and become 3 (No. 1, No. 2, No. 5 battery), set up the trigger heat source Q and become 1e7(W/m3)，TCritical temperatureThe battery surface temperature cloud graph and each battery temperature probe graph obtained by performing thermal runaway simulation on the power battery module by using simulation software are shown in fig. 5, 6a and 6b, wherein the temperature cloud graph is 200 ℃. It can be seen from the figure that the battery module absorbs heat under the influence of the high temperature of the thermal runaway trigger battery, and the temperature rises, so that the overall thermal runaway of the module is triggered.
Calculating the time of the whole thermal runaway of the power battery module to heat source Q (W/m)3) Is divided intoRespectively take 0.5e7、0.6e7、0.75e7、0.9e7、1e7、1.25e7N is 1-6 respectively, and the average time value of thermal runaway of other batteries in the module is initiated
The data were calculated and the data obtained are shown in table 2 below.
TABLE 2
calculating time
The method of the relation between the heat source Q and the number n of the trigger points is as follows:
defining the time average value for inducing the thermal runaway of other batteries in the power battery module
For predicting the system output in thermal runaway, the trigger point n and the heat source Q are system inputs, a, b, c and d are parameters, and data (n) obtained by x times of experiments are obtained1，Q1，t1)，(n2，Q2，t2)…(nx，Qx，tx) The following relations are obtained after fitting by a nonlinear least square method:
due to f [ (n)x,Qx),(a,b,c,e))]The target function is a nonlinear function, so that an iterative algorithm is adopted to solve the target function, the sum of squared errors is minimum, namely when the target function is minimum, the obtained parameter value is the solution of the target function, and the heat source Q, the number n of trigger points and the predicted overall thermal runaway time of the battery module in the power battery module can be obtained
The relationship is as follows:
wherein a, b, c, d, f and g are coefficients and are obtained by nonlinear least square fitting.
And (3) performing nonlinear least square fitting on the data of the table 2 according to the fitting method, and finally solving the relation of the overall thermal runaway time of the power lithium battery module through fitting as shown in fig. 7 and 8:
and step 40, calculating the integral thermal runaway prediction relation of the power battery module according to the calculated integral thermal runaway prediction relation under different collision working conditions, wherein the integral thermal runaway time of the module is triggered by different trigger positions and heat sources under each collision working condition.
The above-mentioned calculation is gained under corner collision operating mode, and by the whole thermal runaway time relational expression of the whole thermal runaway time of the prediction power lithium battery module that thermal runaway trigger point number n and heat source Q fit gained, when trigger point number n equals 7, heat source Q equals 1e7(W/m3) Then the power battery module will be at
Thermal runaway occurs overall.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A multipoint-triggered thermal runaway simulation and prediction method for a ternary lithium power battery module is characterized by comprising the following steps:
s1, establishing a three-dimensional model of the power battery module according to the size, shape and arrangement structure of the sample batteries, and selecting a thermal runaway multipoint triggering position in the power battery module according to different collision working conditions;
s2, importing a three-dimensional model of a power battery module into finite element software, establishing a battery heat generation model for the power battery pack according to a heat abuse model, applying a thermal runaway trigger heat source Q to batteries at a selected multi-point trigger position, dividing a battery domain into quadrangles as main free grids, setting a heat exchange mode among the batteries as convection and conduction, and performing thermal runaway simulation to obtain temperature, trigger points, heat sources and thermal runaway time data of the battery module;
s3 collecting the average value of the time when the thermal runaway of the whole thermal runaway in the module is triggered by the number n of different trigger points and the thermal runaway trigger heat source Q under different collision working conditions
Calculating time by nonlinear least square method
Predicting the time when the power battery module is subjected to thermal runaway integrally according to a relational expression of a thermal runaway heat source Q and the number n of trigger points;
s4, calculating the integral thermal runaway time of the power battery module caused by different trigger positions and heat sources under different collision conditions according to the calculated integral thermal runaway prediction relational expression of the power battery module under different collision conditions.
2. The multi-point triggered thermal runaway simulation and prediction method for ternary lithium power battery modules according to claim 1, wherein in step S1:
the battery is cylindrical in shape;
different collision conditions include rear-end collision, side collision, bottom collision and corner collision.
3. The method for simulating and predicting the thermal runaway of the multi-point triggering ternary lithium power battery module as claimed in claim 1, wherein in the step S1: the method for selecting the thermal runaway multipoint trigger position comprises the following steps: with power battery module corner construction rectangular coordinate system, battery monomer center is dl, dh to x and y axle distance respectively, and when then selecting hot trigger point according to different collision operating mode, trigger point number n corresponds position department battery and should satisfy:
4. the method for simulating and predicting the thermal runaway of the multi-point triggering ternary lithium power battery module as claimed in claim 1, wherein the index for determining the thermal runaway of the power battery in the step S2 is as follows: battery monomer and battery module, wherein:
the battery monomer: temperature of monitoring point>TCritical temperature；
The battery module: temperature of each cell in the module>TCritical temperature。
5. The method for simulating and predicting thermal runaway of a multi-point triggering ternary lithium power battery module according to claim 1, wherein the step S2 further comprises: the selection method for collecting the temperature position of the battery monomer in the power battery module comprises the following steps:
under a cylindrical coordinate system, the battery is equivalent to a cylinder, the height of the cylinder is h, the radius of the cylinder is r, and the cylinder is generated on the surface of the battery by a Monte Carlo method(hi，θi) N, where hi∈(0,h)，θi∈ (0,360 degree), let Δ T be the difference between the temperature of the monitoring point and the surface temperature of the battery cell, Δ T be the difference between the thermal trigger time of the monitoring point and the surface trigger time of the battery cell, T0For the battery thermal trigger start time, trThe time corresponds to the maximum thermal runaway temperature of the battery;
for ∑ Delta T (h)i,θi)、Δt(hi,θi) And carrying out normalization treatment on the two indexes:
the two indexes are summed, and the position (h) at i corresponding to the minimum valuei，θi) Namely the position of the selected battery surface temperature monitoring point:
6. the method for simulating and predicting thermal runaway of a multi-point triggered ternary lithium power battery module as claimed in claim 1, wherein the thermal runaway time average of other batteries in the multi-point thermal runaway initiating module in the step S3 is
The calculation method is as follows:
defining the time t for the 1 st battery in the power battery module to generate thermal runaway caused by the heat source1The thermal runaway time of the other batteries is t2，t3…tn. The average time for the thermal runaway of the power battery module caused by the thermal runaway battery is as follows:
7. the method for simulating and predicting thermal runaway of a multi-point triggered ternary lithium power battery module as claimed in claim 1, wherein the time calculated in step S3 is
The method of the relation between the heat source Q and the number n of the trigger points is as follows:
defining the time average value for inducing the thermal runaway of other batteries in the power battery module
For predicting the system output in thermal runaway, the trigger point n and the heat source Q are system inputs, a, b, c and d are parameters, and data (n) obtained by x times of experiments are obtained1，Q1，t1)，(n2，Q2，t2)…(nx，Qx，tx) The following relations are obtained after fitting by a nonlinear least square method:
due to f [ (n)x,Qx),(a,b,c,d))]The target function is a nonlinear function, so that an iterative algorithm is adopted to solve the target function, the sum of squared errors is minimum, namely when the sum is minimum, the solved parameter value is the solution of the target function, and the heat source Q, the number n of trigger points and the overall thermal runaway time of the prediction battery module in the power battery module can be obtained
The relationship is as follows:
wherein a, b, c, d, f and g are coefficients and are obtained by nonlinear least square fitting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010417278.4A CN111597747B (en) | 2020-05-18 | 2020-05-18 | Multi-point triggering ternary lithium power battery module thermal runaway simulation and prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010417278.4A CN111597747B (en) | 2020-05-18 | 2020-05-18 | Multi-point triggering ternary lithium power battery module thermal runaway simulation and prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111597747A true CN111597747A (en) | 2020-08-28 |
| CN111597747B CN111597747B (en) | 2024-04-16 |
Family
ID=72191330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010417278.4A Active CN111597747B (en) | 2020-05-18 | 2020-05-18 | Multi-point triggering ternary lithium power battery module thermal runaway simulation and prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111597747B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112485674A (en) * | 2020-11-20 | 2021-03-12 | 清华大学 | Modeling method for short circuit thermal runaway in forward lithium ion battery |
| CN112649101A (en) * | 2020-12-15 | 2021-04-13 | 中国电力科学研究院有限公司 | Battery module early warning method and system and fire detection device |
| CN112928360A (en) * | 2021-01-22 | 2021-06-08 | 中国第一汽车股份有限公司 | Power battery heat conduction transmission method, system, terminal and storage medium |
| CN113085555A (en) * | 2021-03-24 | 2021-07-09 | 东风汽车集团股份有限公司 | Warning method for battery pack after thermal runaway |
| CN116451383A (en) * | 2023-06-13 | 2023-07-18 | 苏州精控能源科技有限公司 | Thermal runaway side-spraying battery and design method thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110111269A1 (en) * | 2009-11-11 | 2011-05-12 | Tse David Yuanjei | Flash Cooling System for Increased Battery Safety |
| US20120013341A1 (en) * | 2010-07-16 | 2012-01-19 | Tesla Motors, Inc. | Method and Apparatus for Electrically Cycling a Battery Cell to Simulate an Internal Short |
| US20140091772A1 (en) * | 2012-09-24 | 2014-04-03 | Robert Del Core | Adaptive thermal management of an electric energy storage method and system apparatus |
| CN108647432A (en) * | 2018-05-09 | 2018-10-12 | 西安交通大学 | Battery thermal runaway prediction technique based on thermal resistance network model |
| CN109581223A (en) * | 2018-11-29 | 2019-04-05 | 吉林大学 | The DIE Temperature estimation method of Li-ion batteries piles based on Kalman filtering |
| CN110968968A (en) * | 2019-12-05 | 2020-04-07 | 西南交通大学 | Thermal runaway simulation method based on needling internal short circuit lithium ion battery |
-
2020
- 2020-05-18 CN CN202010417278.4A patent/CN111597747B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110111269A1 (en) * | 2009-11-11 | 2011-05-12 | Tse David Yuanjei | Flash Cooling System for Increased Battery Safety |
| US20120013341A1 (en) * | 2010-07-16 | 2012-01-19 | Tesla Motors, Inc. | Method and Apparatus for Electrically Cycling a Battery Cell to Simulate an Internal Short |
| US20140091772A1 (en) * | 2012-09-24 | 2014-04-03 | Robert Del Core | Adaptive thermal management of an electric energy storage method and system apparatus |
| CN108647432A (en) * | 2018-05-09 | 2018-10-12 | 西安交通大学 | Battery thermal runaway prediction technique based on thermal resistance network model |
| CN109581223A (en) * | 2018-11-29 | 2019-04-05 | 吉林大学 | The DIE Temperature estimation method of Li-ion batteries piles based on Kalman filtering |
| CN110968968A (en) * | 2019-12-05 | 2020-04-07 | 西南交通大学 | Thermal runaway simulation method based on needling internal short circuit lithium ion battery |
Non-Patent Citations (4)
| Title |
|---|
| AHMED O. SAID ET AL: "Experimental investigation of cascading failure in 18650 lithium ion cell arrays: Impact of cathode chemistry", JOURNAL OF POWER SOURCES, vol. 446, 30 November 2019 (2019-11-30), pages 1 - 14, XP085937004, DOI: 10.1016/j.jpowsour.2019.227347 * |
| HUANG LI ET AL: "Experimental study on thermal runaway risk of 18650 lithium ion battery under side-heating condition", JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, vol. 61, 30 June 2019 (2019-06-30), pages 122 - 129, XP085794031, DOI: 10.1016/j.jlp.2019.06.012 * |
| 罗星娜 等: "基于计算流体动力学的锂离子电池热失控多米诺效应研究", 科学技术与工程, vol. 14, no. 33, 30 November 2014 (2014-11-30), pages 327 - 332 * |
| 谭春华 等: "锂离子电池针刺热失控仿真研究", 电源技术, vol. 42, no. 11, 30 November 2018 (2018-11-30), pages 1604 - 1607 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112485674A (en) * | 2020-11-20 | 2021-03-12 | 清华大学 | Modeling method for short circuit thermal runaway in forward lithium ion battery |
| CN112485674B (en) * | 2020-11-20 | 2021-12-10 | 清华大学 | Modeling method for short circuit thermal runaway in forward lithium ion battery |
| CN112649101A (en) * | 2020-12-15 | 2021-04-13 | 中国电力科学研究院有限公司 | Battery module early warning method and system and fire detection device |
| CN112928360A (en) * | 2021-01-22 | 2021-06-08 | 中国第一汽车股份有限公司 | Power battery heat conduction transmission method, system, terminal and storage medium |
| CN113085555A (en) * | 2021-03-24 | 2021-07-09 | 东风汽车集团股份有限公司 | Warning method for battery pack after thermal runaway |
| CN113085555B (en) * | 2021-03-24 | 2022-06-03 | 东风汽车集团股份有限公司 | Warning method for battery pack after thermal runaway |
| CN116451383A (en) * | 2023-06-13 | 2023-07-18 | 苏州精控能源科技有限公司 | Thermal runaway side-spraying battery and design method thereof |
| CN116451383B (en) * | 2023-06-13 | 2023-08-15 | 苏州精控能源科技有限公司 | Thermal runaway side-spraying battery and design method thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111597747B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111597747A (en) | Multipoint-triggering ternary lithium power battery module thermal runaway simulation and prediction method | |
| Qian et al. | Heat dissipation optimization of lithium-ion battery pack based on neural networks | |
| Panchal et al. | Electrochemical thermal modeling and experimental measurements of 18650 cylindrical lithium-ion battery during discharge cycle for an EV | |
| Shi et al. | Optimization of air-cooling technology for LiFePO4 battery pack based on deep learning | |
| Mei et al. | An investigation on expansion behavior of lithium ion battery based on the thermal-mechanical coupling model | |
| Wang et al. | Real-time estimation of temperature distribution for cylindrical lithium-ion batteries under boundary cooling | |
| CN106021810A (en) | Thermal model modeling method for lithium ion battery pack based on air-cooling heat dissipating mode | |
| CN111475933A (en) | Lithium ion battery thermal runaway simulation method and system | |
| CN111144029B (en) | Modeling method for thermoelectric coupling characteristics of lithium ion power battery | |
| CN114692244B (en) | Lithium battery pack thermal abuse safety risk assessment method based on multi-physical field simulation | |
| Cui et al. | An application of evolutionary computation algorithm in multidisciplinary design optimization of battery packs for electric vehicle | |
| CN114325406A (en) | Method and system for predicting thermal runaway of battery based on machine learning thinking | |
| CN103900733B (en) | The measuring method of internal temperature of battery field distribution | |
| Cao et al. | A full-scale electrical-thermal-fluidic coupling model for li-ion battery energy storage systems | |
| CN111767625A (en) | Thermal simulation method for lithium ion battery pack | |
| Cho et al. | An LSTM-PINN hybrid method to estimate lithium-ion battery pack temperature | |
| CN113297738A (en) | Lithium ion battery thermal runaway modeling method | |
| Pordanjani et al. | Thermo-electrochemical simulation of the cooling process in a compact battery pack considering various configurations | |
| Liao et al. | A framework of optimal design of thermal management system for lithium-ion battery pack using multi-objectives optimization | |
| Zheng et al. | Electrical and thermal active co-management for lithium-ion batteries | |
| CN113626946A (en) | Method for determining boundary value of thickness and thermal conductivity of material for blocking battery thermal diffusion | |
| CN117195713A (en) | Lithium battery module temperature field reconstruction method based on deep operator learning | |
| CN113722926B (en) | Square lithium battery electric heating coupling modeling error source analysis method | |
| Shen et al. | A karhunen-loeve Galerkin online modeling approach for the thermal dynamics of li-ion batteries | |
| Zhu et al. | Analysis of the structure arrangement on the thermal characteristics of Li‐ion battery pack in thermoelectric generator |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |