Statistics method-based optimization of electrolyte conductivity of lithium-ion battery

doi:10.19799/j.cnki.2095-4239.2022.0064

Abstract

Abstract:

Ionic conductivity is an important parameter in the evaluation of lithium-ion battery electrolyte performance. Ionic conductivity affects the low temperature and rate capability of the battery and provides guiding principles for electrolyte design. Traditional research and development methodologies are primarily based on trial and error, which involves many variables. This results in high experimental costs and a long discovery cycle. To solve the above issues, a conductivity optimization design method that combines a space filling mixture design and Gaussian process regression is proposed in this paper. According to the formulation parameters of the electrolyte, including different types of cyclic carbonate (ethylene carbonate), linear carbonates, and carboxylic acids as the model's input, the ionic conductivity is output by the model, and the maximum likelihood estimation is employed to solve the super parameters. The effectiveness and precision of the proposed model were verified in subsequent experiments, and we found that an electrolyte solvent recipe that satisfies any conductivity requirements can be predicted.

Key words: ionic conductivity, space filling mixture design, gaussian process regression, lithium-ion battery, electrolyte

CLC Number:

TM 912

Sifei ZHOU, Jun LI, Daoming ZHANG, Haoliang XUE, Xiaofei WANG. Statistics method-based optimization of electrolyte conductivity of lithium-ion battery[J]. Energy Storage Science and Technology, 2022, 11(10): 3364-3370.

Figures/Tables 8

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Table 1

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Table 2

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Table 3

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环状碳酸酯(EC)	线性碳酸酯	羧酸酯	EMC	DEC	DMC	EA	MP	EP
0.269	0.550	0.181	0.043	0.953	0.004	0.189	0.202	0.609
0.129	0.529	0.342	0.140	0.275	0.585	0.012	0.088	0.900
0.279	0.422	0.299	0.372	0.416	0.212	0.470	0.015	0.515
0.166	0.507	0.327	0.985	0.013	0.002	0.847	0.062	0.091
0.297	0.305	0.398	0.022	0.323	0.655	0.609	0.355	0.036
0.156	0.600	0.244	0.114	0.819	0.067	0.540	0.439	0.021
0.118	0.589	0.293	0.560	0.039	0.401	0.260	0.349	0.391
0.278	0.566	0.156	0.193	0.017	0.790	0.048	0.899	0.053
0.248	0.360	0.392	0.785	0.188	0.027	0.082	0.684	0.234
0.211	0.479	0.310	0.018	0.728	0.254	0.001	0.830	0.169

温度/℃	预测值/(ms/cm)	实测值/(ms/cm)	模型误差/(ms/cm)
-10	10.06	10.43	-0.37
25	14.87	14.42	0.45
45	17.31	16.95	0.36

模拟结果抽取样本	环状碳酸酯(EC)	线性碳酸酯	羧酸酯	EMC	DEC	DMC	EA	MP	EP
1	0.291	0.344	0.365	0.163	0.071	0.766	0.177	0.519	0.304
2	0.137	0.533	0.330	0.052	0.283	0.665	0.509	0.023	0.468
3	0.232	0.585	0.183	0.062	0.083	0.855	0.263	0.370	0.367
4	0.175	0.558	0.267	0.853	0.144	0.003	0.190	0.237	0.573

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