基于EGO加点策略的动力电池包多目标优化

doi:10.19562/j.chinasae.qcgc.2021.10.006

Abstract

Abstract:

For the lightweighting of battery pack and increasing its modal frequency， a multi-objective optimization scheme based on efficient global optimization （EGO） strategy with additive sample points is proposed. Firstly， by using the design of experiment and Pareto rule， the effects of design variables on the optimization objectives are analyzed， and the variables having more significant influences on the mass and 1st-order modal frequency of battery pack are chosen to be optimized so as to reduce the problem-solving difficulty. Then， multi-objective particle swarm optimization （MOPSO） algorithm is adopted assisted with Kriging surrogate model to solve the optimization problem， and the EGO strategy with additive sample points is employed to get the new design points and samples respectively， with the surrogate model updated until the optimization procedure converges. Finally， the test functions are utilized to verify the effectiveness of the scheme proposed， which is then applied to the multi-objective optimization of battery pack. The results show the scheme is efficient and feasible， with which the mass of battery pack reduces by 4.89 kg while maintaining a higher 1st order modal frequency.

Key words: battery pack, multi?objective optimization, surrogate model, EGO with additive sample points

Puyi Wang,Yingchun Bai,Cheng Lin,Zhenjiang Wu,Baohua Wang. Multi⁃objective Optimization of Traction Battery Pack Based on EGO Strategy with Additive Sample Points[J].Automotive Engineering, 2021, 43(10): 1457-1465.

Figures/Tables 20

序号	质量/kg	1阶模态频率/Hz	最大1 $σ$ 应力/MPa
1	540.43	30.592	75.782
2	540.53	30.595	78.204
3	542.80	31.067	78.881
4	544.61	31.567	46.259
5	544.79	31.572	46.495

References 21

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零件	材料代号	厚度/mm	密度/（g·mm^-3）	弹性模量/GPa	泊松比	屈服强度/MPa
上箱盖	DC06	0.7	7.85	210	0.3	180
下箱体	DC06	1.2	7.85	210	0.3	180
支板	HC420/780DP	1.5	7.85	210	0.3	460
横梁	HC420/780DP	1.5	7.85	210	0.3	460
纵梁	HC420/780DP	1.5	7.85	210	0.3	460
托架	HC420/780DP	2.0	7.85	210	0.3	460
压板	B280VK	2.5	7.80	206	0.3	360

代号	x₁	x₂	x₃	x₄	x₅	x₆	x₇
取值	［0.6，1.2］	［0.8，1.5］	［1，3］	［1，3］	［1，3］	［1，3］	［1，3］
初始值	0.7	1.2	1.5	1.5	1.5	1.5	1.5

样本序号	MOPSO		EGO?MOPSO
样本序号	平均IGD	平均耗时/s	平均IGD	平均耗时/s
1	0.025 63	284	0.021 26	305
2	0.025 51	273	0.020 75	287
3	0.024 37	295	0.226 30	310

样本序号	MOPSO		EGO?MOPSO
样本序号	平均IGD	平均耗时/s	平均IGD	平均耗时/s
1	0.213 5	1 034	0.204 9	1 045
2	0.208 1	1 100	0.203 7	1 281
3	0.224 6	1 106	0.196 0	1 121

评价指标	质量		1阶模态频率
评价指标	优化前	优化后	优化前	优化后
决定系数R²	0.956 9	0.979 9	0.888 2	0.913 1
均方根误差RMSE	1.39 kg	0.98 kg	0.15 Hz	0.14 Hz

Multi⁃objective Optimization of Traction Battery Pack Based on EGO Strategy with Additive Sample Points

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序号	x₁	x₂	x₃	x₄	x₅	x₆	x₇
1	0.6	1	1	1	2.2	1	1.3
2	0.6	1	1	1	2.8	1	1.4
3	0.6	1.1	1	1	3	1	1.7
4	0.6	1	1	2	3	1	1.5
5	0.6	1	1	2	3	1.2	1.5

频率/Hz	功率谱密度/（g²·Hz^-1）
频率/Hz	Z轴	Y轴	X轴
5	0.015	0.002	0.006
10		0.005
15	0.015
20		0.005
30			0.006
65	0.001
100	0.001
200	0.000 1	0.000 15	0.000 03
RMS	0.64g	0.45g	0.5g

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序号	x₁	x₂	x₃	x₄	x₅	x₆	x₇
1	0.6	1	1	1	2.2	1	1.3
2	0.6	1	1	1	2.8	1	1.4
3	0.6	1.1	1	1	3	1	1.7
4	0.6	1	1	2	3	1	1.5
5	0.6	1	1	2	3	1.2	1.5

序号	x₁	x₂	x₃	x₄	x₅	x₆	x₇
1	0.6	1	1	1	2.2	1	1.3
2	0.6	1	1	1	2.8	1	1.4
3	0.6	1.1	1	1	3	1	1.7
4	0.6	1	1	2	3	1	1.5
5	0.6	1	1	2	3	1.2	1.5