Capacity Estimation of lithium battery based on charging data and Long Short-term Memory Recurrent Neural Network

被引：5

作者：

You, Mingxing ^{[1
]}

Liu, Yonggang ^{[1
]}

Chen, Zheng ^{[2
]}

Zhou, Xuan ^{[3
]}

机构：

[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China

[2] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Yunnan, Peoples R China

[3] Kettering Univ, Dept Elect & Comp Engn, Flint, MI 48504 USA

来源：

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2022年

基金：

中国国家自然科学基金;

关键词：

Battery Management System; State of Health; LSTMs; Health Factors; STATE;

D O I：

10.1109/IV51971.2022.9827334

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Battery management system (BMS) plays an important role in ensuring the safe and stable operation of batteries. In BMS, the State of Health (SOH) status as a measure of the battery storage and release of the ability to change, in essence reflects the aging and damage of batteries. However, in actual operation, the capacity of the battery is difficult to measure directly. This paper presents a method, the voltage, current and temperature data extracted from the charging and discharging process of the battery are directly used as Health Factors(HF), which are divided into training set verification set and test set. The battery capacity estimation model is established based on the Long Short-term Memory Recurrent Neural Network (LSTM, RNN) to estimate SOH.

引用

页码：230 / 234

页数：5

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