Multiobjective optimization of thermal performance and grouping efficiency for air cooling battery module

doi:10.19799/j.cnki.2095-4239.2021.0407

Abstract

Abstract:

In this paper, a new type of air cooling battery thermal management system based on a heat spreader plate with a casing tube is proposed to extend the air cooling limit. The cylindrical lithium-ion batteries in the module are arranged in an orthogonal array. The bottom of the battery is connected to an aluminum base plate by a positioning insulation layer, whereas the heat spreader plate is allocated in the middle of the battery through a casing tube, thus enhancing the heat transfer from the battery to the air flow. First, the corresponding air cooling experimental system was constructed, and the feasibility of the numerical model was verified by comparing with the experimental results. Subsequently, the inlet velocity and structure parameters of the battery module were investigated by simulation; then, 25 cases were obtained on the basis of Central Composite Design to provide sample data for the buildup of surrogate models of optimization objectives. Desirability functions and surrogate models were introduced to conduct multiobjective optimization, including maximum temperature, maximum temperature difference, pressure drop, and grouping efficiency. Finally, an optimal configuration of the structural parameters of the heat spreader plate and the air velocity of the module were obtained. Compared to the design without a heat spreader plate, the maximum temperature and maximum temperature difference of the optimal design were decreased by 16.12% (6.36 ℃?) and 48.48% (2.72 ℃?), respectively, whereas the corresponding grouping efficiency was 87.1%, which is close to 89.73% of the conventional design. The temperature uniformity of the battery module was obviously improved while ensuring a high-level weight grouping efficiency for the air cooling battery modules.

Key words: heat spreader plate, air cooling battery module, multi-objective optimization, grouping efficiency, temperature uniformity

CLC Number:

TM 911

Xiaobin XU, Yefei XU, Hengyun ZHANG, Shunliang ZHU, Haifeng WANG. Multiobjective optimization of thermal performance and grouping efficiency for air cooling battery module[J]. Energy Storage Science and Technology, 2022, 11(2): 553-562.

Figures/Tables 11

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部件材料	密度/(kg/m³)	比热容/[J/(kg·K)]	导热系数/(W/m·K)	黏度/(kg/m·s)
空气(25 ℃)	1.165	1005	0.0267	0.0000186
电池	2510	1028	1.63(径向) 36.96(轴向)	—
热扩散板/底板	2719	871	155	—
电绝缘板	1000	1200	0.19	—

仿真案例	描述项	厚度/mm	套筒长度/mm	尾部长度/mm	进口风速/(m/s)
设计1	常规风冷	—	—	—	0.7
设计2	热扩散板配置(基准案例)	2	20.5	2	0.7
情形1	改变风速v₀	2	20.5	2	0.5～5
情形2	改变厚度H	0.5～5	20.5	2	0.7
情形3	改变套筒的长度H_s	2	10.5～40.5	2	0.7
情形4	改变尾部长度L	2	20.5	2～25	0.7
设计3	最优配置	0.5	10.5	24.7	1.5

					仿真结果
No.	H/mm	H_s/mm	L/mm	v₀/(m/s)	T_max/℃	?T/℃	?p/Pa	G_m
1	2.8	25.5	13.5	2.75	30.51	1.84	397.37	80.68%
2	1.6	18	7.75	3.88	29.39	1.84	920.30	84.26%
3	3.9	33	7.75	3.88	29.77	1.70	796.91	78.50%
4	5.0	25.5	13.5	2.75	30.57	1.75	426.27	76.56%
5	3.9	18	19.25	3.88	29.33	1.94	1310.91	78.63%
6	3.9	33	19.25	1.63	32.81	2.11	140.98	77.20%
7	2.8	25.5	13.5	5.00	29.19	2.05	1509.14	80.68%
8	2.8	10.5	13.5	2.75	30.29	1.75	347.42	82.25%
9	1.6	33	19.25	3.88	29.86	1.77	623.59	82.03%
10	1.6	18	19.25	1.63	32.35	2.36	124.52	83.64%
11	3.9	18	7.75	1.63	32.29	2.23	155.34	79.98%
12	2.8	25.5	13.5	0.50	40.14	4.38	15.48	80.68%
13	1.6	33	19.25	1.63	33.09	2.44	114.09	82.03%
14	1.6	33	7.75	3.88	29.87	1.74	615.87	82.62%
15	0.5	25.5	13.5	2.75	30.44	1.88	355.19	85.50%
16	3.9	33	7.75	1.63	32.99	2.27	141.58	78.50%
17	1.6	18	7.75	1.63	32.50	2.52	124.37	84.26%
18	1.6	18	19.25	3.88	29.38	1.87	935.16	83.64%
19	3.9	18	19.25	1.63	32.15	2.10	154.42	78.63%
20	2.8	40.5	13.5	2.75	30.70	1.73	450.27	79.18%
21	1.6	33	7.75	1.63	33.26	2.59	114.09	82.62%
22	3.9	18	7.75	3.88	29.34	1.93	1312.76	79.98%
23	3.9	33	19.25	3.88	29.76	1.72	779.54	77.20%
24	2.8	25.5	2	2.75	30.56	1.77	379.34	81.69%
25	2.8	25.5	25	2.75	30.50	1.86	398.84	79.70%

项目	最高温度T_max/℃	最大温差?T/℃	进出口压差?p/Pa	成组效率G_m
设计1(常规设计)	39.45	5.61	17.90	89.73%
设计2(基准案例)	37.98	4.28	26.41	83.59%
设计3(最优配置)	33.09	2.89	81.79	87.1%
设计3结果偏差(预测vs仿真)	-0.11 (0.33%)	+0.02 (+0.69%)	-0.59 (0.73%)	+0.02% (0.02%)

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