锂电池叉流流道液冷结构设计及散热特性分析

王学章; 李科群

doi:10.7498/aps.71.20220212

摘要

为使锂离子电池组的散热达到更高的安全性, 设计了锂离子电池组的一种液冷冷却模型, 该模型采用两种流体进行冷却. 对模型中不同雷诺数、不同微通道个数、不同微通道半径等条件下的电池温度分布进行了模拟研究. 模拟结果表明: 雷诺数对锂离子电池组的散热影响存在临界值; 对各条件进行优化分析, 优化后的液冷冷却模型在理论上能有效降低锂离子电池组的最高温度, 与单体电池在2C放电倍率的工况下相比, 锂电池最高温度下降了26.24 K, 并改善了锂离子电池组的温度分布均匀性.

关键词:

Abstract

A detailed three-dimensional thermal model is developed to examine the thermal behaviour of a lithium-ion battery. This model is a cross-flow liquid cooling model, which can make the heat dissipation of lithium-ion battery pack achieve higher safety. Two kinds of fluids are used for cooling, and the polynomial fitting function is used as the heat source term of lithium battery pack. The battery temperature distribution under the conditions of different Reynolds numbers, different numbers of micro-channel and different micro-channel radii are studied for this model. The simulation results show that the maximum battery temperature is 295.84 K, the minimum battery temperature is 293.14 K, and the maximum temperature difference of the battery pack is 2.7 K. The maximum temperature and temperature difference of the battery under the model are in line with the reasonable operating temperature range of lithium-ion battery pack. The maximum temperature of battery pack decreases with the increase of Reynolds number, but the effect of Reynolds number on heat dissipation of lithium-ion battery pack has a critical value. As the number of micro-channels increases, the maximum temperature of the battery string decreases. However, when the number of micro-channels increases to a certain value, the maximum temperature of the battery pack decreases slowly. The maximum temperature of the battery pack does not decrease monotonically as the radius of the micro-channel increases. Orthogonal analysis results show that the Reynolds number has the greatest influence on the cooling effect of the model, followed by the size of the micro-channel radius, and the number of micro-channels has the least influence. The optimized liquid cooling model can effectively reduce the maximum temperature of lithium-ion battery in theory, and the maximum temperature of lithium-ion battery decreases by 26.24 K in comparison with that of single battery at 2C discharge rate. The reliability of the cross-flow channel model is proved by numerical analysis, and it is also proved that the cross-flow channel has an equilibrium point between the perturbation gain and the flow retarding effect. The heat dissipation effect of lithium- ion battery pack is correlated with the number and radius of micro-channels, but not a single positive correlation. Reasonably increasing the number and size of micro-channels can effectively enhance the heat dissipation effect of battery pack.

Keywords:

作者及机构信息

上海理工大学能源与动力工程学院, 上海　200093

通信作者: 李科群, kequn_li@126.com

Authors and contacts

Energy and Power Engineering College, University of Shanghai for Science and Technology, Shanghai 200093, China

Corresponding author: Li Ke-Qun, kequn_li@126.com

文章全文 : translate this paragraph

参考文献

[1]	Saw L H, Tay A, Zhang L W 2015 Thermal Measurement Modeling and Management Symposium San Francisco, USA, March 28, 2015 p298
[2]	Nitta N, Wu F, Lee J T, Yushin G 2015 Mater. Today Phys. 18 5 Google Scholar
[3]	Chen S C, Wan C C, Wang Y Y 2005 J. Power Sources 140 111 Google Scholar
[4]	Jaguemont J, Boulon L, Dubé Y 2016 Appl. Energy 164 99 Google Scholar
[5]	Jung D Y, Lee B H, Sun W K 2002 J. Power Sources 109 1 Google Scholar
[6]	Rao Z, Wang S, Zhang G 2011 Energy Convers. Manage. 52 3408 Google Scholar
[7]	Pesaran A A 2002 J. Power Sources 110 377 Google Scholar
[8]	Menale C, D’Annibale F, Mazzarotta B, Bubbico R 2019 Energy 182 57 Google Scholar
[9]	Liu H, Wei Z, He W, Zhao J 2017 Energy Convers. Manage. 150 304 Google Scholar
[10]	Sardari P T, Giddings D, Gillott M, Walker G S, Grant D 2019 Energy Convers. Manage. 201 112 Google Scholar
[11]	Mahdi J M, Mohammed H I, Hashim E T, Talebizadehsardari P, Nsofor E C 2020 Appl. Energy 257 113 Google Scholar
[12]	Shah K, Mckee C, Chalise D, Jain A 2016 Energy 113 852 Google Scholar
[13]	Pesaran A A 2001 Advanced Automotive Battery Conference Las Vegas, USA, February 6–8, 2001 p2
[14]	Pesaran A A, Keyser M, Burch S1999 Proceedings of the 4th Vehicle Thermal Management Systems London, UK, May 24–27, 1999 p1
[15]	Jarrett A, Kim I Y 2011 J. Power Sources 196 10359 Google Scholar
[16]	Han J Q, Qiu D D, Zhu A 2018 Appl. Therm. Eng. 132 508 Google Scholar
[17]	Bernadi D, Pawlikowski E 1985 J. Electrochem. Soc. 132 5 Google Scholar
[18]	王一宇 2020 硕士学位论文 (青岛: 青岛科技大学) Wang Y Y 2020 M. S. Thesis (Qingdao: Qingdao University of Science and Technology) (in Chinese)
[19]	廖永平 1984 正交试验法在机械工业中的应用 (北京: 中国农业机械出版) 第39—45页 Liao Y P 1984 Application of Orthogonal Test Method in Mechanical Industry (Beijing: China Agricultural Machinery Press) pp39–45

施引文献

图 1 芯体模型正视图

Fig. 1. Core body front view model diagram.

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图 2 芯体模型俯视图

Fig. 2. Top view of core body model.

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图 3 放电欧姆内阻拟合函数

Fig. 3. Discharge Ohm resistance fitting function.

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图 4 放电极化内阻拟合函数

Fig. 4. Fitting function of internal resistance of discharge polarization.

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图 5 电池内热源函数

Fig. 5. Heat source function in the battery.

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图 6 单体电池热性能的实验验证

Fig. 6. Experimental verification of thermal performance of single battery.

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图 7 网格的无关性验证

Fig. 7. Grid independence verification.

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图 8 时间步长无关性验证

Fig. 8. Time step independence verification.

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图 9 电池组温度分布云图(2C)

Fig. 9. Cloud map of battery pack temperature distribution (2C).

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图 10 电池温度分布云图(2C)

Fig. 10. Cloud map of battery temperature distribution (2C)

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图 11 放电过程中不同电池的最高温度(2C)

Fig. 11. The maximum temperature of different batteries during discharge (2C).

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图 12 放电过程中不同电池的最大温差(2C)

Fig. 12. The maximum temperature difference of different batteries during discharge (2C).

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图 13 电池截面温度云图

Fig. 13. Temperature cloud of battery section.

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图 14 在不同雷诺数条件下电池组的最高温度(2C)

Fig. 14. Compare the maximum temperature of different Reynolds number battery packs (2C).

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图 15 在不同雷诺数条件下电池组的最大温差(2C)

Fig. 15. Comparison of the maximum temperature difference of battery packs with different Reynolds numbers (2C)

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图 16 在不同雷诺数条件下电池组的最高温度(1C)

Fig. 16. Comparison of the maximum temperature of different Reynolds number battery packs (1C).

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图 17 在不同雷诺数条件下电池组的最大温差(1C)

Fig. 17. Compare the maximum temperature difference of different Reynolds number battery packs (1C).

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图 18 微通道个数对电池组最高温度的影响(2C)

Fig. 18. Influence of the number of micro-channels on the maximum temperature of the battery pack (2C).

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图 19 微通道半径对电池组最高温度的影响(2C)

Fig. 19. Influence of the micro-channel radius on the maximum temperature of the battery pack (2C).

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图 20 微通道半径对电池组最大温差的影响(2C)

Fig. 20. Influence of the micro-channel radius on the maximum temperature difference of the battery pack (2C).

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表 1 三维传热模型参数

Table 1. Three-dimensional heat transfer model parameters

参数	ρ/(kg·m^–3)	C_p/(J·kg^–1·K^–1)	Λ/(W·m^–1·K^–1)	Μ/(Pa·s)
锂电池	2776	1075	各向异性	—
乙二醇	1068	3319	0.387	—
水	ρ(T)	C_p(T)	λ(T)	μ(T)
铝	2702	237	903	—

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表 2 具体信息表

Table 2. Specific information.

微通道半径/mm	电池组最高温度/K	电池组最大温差/K
0.3	296.26	3.16
0.5	295.86	2.73
0.8	295.84	2.70
1.0	295.46	2.31
1.3	295.34	2.19
1.6	295.52	2.37
2.0	295.74	2.65

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表 3 正交试验表

Table 3. Orthogonal test.

序号	雷诺数 (因素A)	微通道半径/mm (因素B)	微通道个数/个 (因素C)	锂电池组的温度/K
1	2500	0.3	3	296.33
2	2500	0.5	4	296.35
3	2500	0.8	5	295.54
4	2500	1.3	7	295.47
5	7500	0.3	4	295.57
6	7500	0.5	3	295.18
7	7500	0.8	7	295.13
8	7500	1.3	5	294.96
9	12500	0.3	5	295.03
10	12500	0.5	7	294.50
11	12500	0.8	4	294.91
12	12500	1.3	3	294.84
13	15500	0.3	4	294.91
14	15500	0.5	3	294.83
15	15500	0.8	7	294.87
16	15500	1.3	5	294.71

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表 4 极差分析表

Table 4. Range analysis.

名称	因素A	因素B	因素C
Z1	1183.74	1181.84	1181.18
Z2	1180.84	1180.86	1181.74
Z3	1179.28	1180.50	1180.29
Z4	1179.32	1179.98	1179.97
Z1算术平均值	295.935	295.46	295.295
Z2算术平均值	295.21	295.215	295.435
Z3算术平均值	294.82	295.125	295.0725
Z4算术平均值	294.83	294.995	294.9925
极差	0.725	0.465	0.4425
主次顺序	A>B>C
优水平	A4	B4	C3
优组合	A4 B4 C3

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map

[1]	Saw L H, Tay A, Zhang L W 2015 Thermal Measurement Modeling and Management Symposium San Francisco, USA, March 28, 2015 p298
[2]	Nitta N, Wu F, Lee J T, Yushin G 2015 Mater. Today Phys. 18 5 Google Scholar
[3]	Chen S C, Wan C C, Wang Y Y 2005 J. Power Sources 140 111 Google Scholar
[4]	Jaguemont J, Boulon L, Dubé Y 2016 Appl. Energy 164 99 Google Scholar
[5]	Jung D Y, Lee B H, Sun W K 2002 J. Power Sources 109 1 Google Scholar
[6]	Rao Z, Wang S, Zhang G 2011 Energy Convers. Manage. 52 3408 Google Scholar
[7]	Pesaran A A 2002 J. Power Sources 110 377 Google Scholar
[8]	Menale C, D’Annibale F, Mazzarotta B, Bubbico R 2019 Energy 182 57 Google Scholar
[9]	Liu H, Wei Z, He W, Zhao J 2017 Energy Convers. Manage. 150 304 Google Scholar
[10]	Sardari P T, Giddings D, Gillott M, Walker G S, Grant D 2019 Energy Convers. Manage. 201 112 Google Scholar
[11]	Mahdi J M, Mohammed H I, Hashim E T, Talebizadehsardari P, Nsofor E C 2020 Appl. Energy 257 113 Google Scholar
[12]	Shah K, Mckee C, Chalise D, Jain A 2016 Energy 113 852 Google Scholar
[13]	Pesaran A A 2001 Advanced Automotive Battery Conference Las Vegas, USA, February 6–8, 2001 p2
[14]	Pesaran A A, Keyser M, Burch S1999 Proceedings of the 4th Vehicle Thermal Management Systems London, UK, May 24–27, 1999 p1
[15]	Jarrett A, Kim I Y 2011 J. Power Sources 196 10359 Google Scholar
[16]	Han J Q, Qiu D D, Zhu A 2018 Appl. Therm. Eng. 132 508 Google Scholar
[17]	Bernadi D, Pawlikowski E 1985 J. Electrochem. Soc. 132 5 Google Scholar
[18]	王一宇 2020 硕士学位论文 (青岛: 青岛科技大学) Wang Y Y 2020 M. S. Thesis (Qingdao: Qingdao University of Science and Technology) (in Chinese)
[19]	廖永平 1984 正交试验法在机械工业中的应用 (北京: 中国农业机械出版) 第39—45页 Liao Y P 1984 Application of Orthogonal Test Method in Mechanical Industry (Beijing: China Agricultural Machinery Press) pp39–45

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