A data-driven evaluation method for low-temperature performance of lithium-ion batteries

被引：4

作者：

Xu, Helin ^{[1
]}

Cheng, Lin ^{[1
]}

Paizulamu, Daniyaer ^{[1
]}

Zhang, Yongmi ^{[2
]}

Zhu, Changyu ^{[2
]}

机构：

[1] Tsinghua Univ, Key Lab Control & Simulat Power Syst & Generat Eq, Beijing 100084, Peoples R China

[2] Huadian Inner Mongolia Energy Co LTD, Hohhot 010010, Inner Mongolia, Peoples R China

来源：

ENERGY REPORTS | 2023年 / 9卷

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Lithium-ion battery; Low temperature; Capacity; Machine learning; CHEMICAL DIFFUSION-COEFFICIENT; INTERCALATION; ELECTROLYTES; ELECTRODES; IMPEDANCE; BEHAVIOR;

D O I：

10.1016/j.egyr.2022.11.009

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Energy storage system plays an important role in smoothing out the electricity supply from renewable energy and improving stability of the power system. At present, most energy storage systems are still battery energy storage systems (BESS). However, the time-varying temperature condition has a significant impact on discharge capacity of lithium-ion batteries. When lithium-ion battery operates in a low temperature environment, the discharge capacity of the battery decreases. Therefore, this paper develops a discharge capacity evaluation method for lithium-ion batteries at low temperature. Firstly, we analyze the battery discharge characteristics. On this basis, battery tests have been conducted and we proposed some health indicators. Finally input the measured data and health indicators into the machine learning model. The applicability and effectiveness of this method are analyzed through numerical results. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

引用

页码：912 / 921

页数：10

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