Virtual Temperature Sensor in Battery Thermal Management System Using LSTM-DNN

被引：1

作者：

Bamati, Safieh ^{[1
]}

Chaoui, Hicham ^{[1
]}

Gualous, Hamid ^{[2
]}

机构：

[1] Carleton Univ, Dept Elect, Ottawa, ON, Canada

[2] Univ Caen Normandie, LUSAC Lab, Cherbourg, France

来源：

2023 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, VPPC | 2023年

关键词：

Lithium-ion batteries (LIBs); long short-term memory (LSTM); recurrent neural network (RNN); deep neural Network (DNN); battery surface temperature (ST); REMAINING USEFUL LIFE;

D O I：

10.1109/VPPC60535.2023.10403358

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The LIB's performance, safety, and longevity in electric vehicles are significantly affected by temperature. Battery thermal management maintains the batteries' operation to a safe region to improve their performance and extends their lifetime, especially during fast charging and high power discharge, and in extreme weather conditions. Therefore, by utilizing a surface temperature estimation model, the multitude temperature sensors which typically monitor battery operation temperature can be reduced, thereby reducing its cost and improving its reliability. This paper presents a novel deep learning method based on long-Short term memory (LSTM) and deep neural network (DNN) algorithm for battery surface temperature estimation using cylindrical lithium ion battery. The estimation results are well validated on test battery.

引用

页数：6

共 50 条

[1] PID-based CNN-LSTM for accuracy-boosted virtual sensor in battery thermal management system
Xie, Jiahang
Yang, Rufan
Gooi, Hoay Beng
Nguyen, Hung Dinh
[J]. APPLIED ENERGY, 2023, 331
[2] Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
Zhen, Tao
Yan, Lei
Yuan, Peng
[J]. ALGORITHMS, 2019, 12 (12)
[3] Walking gait phase detection based on acceleration signals using LSTM-DNN algorithm
Zhen T.
Yan L.
Yuan P.
[J]. Algorithms, 2019, 12 (02):
[4] Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN
Farooq, Sehar Shahzad
Fiaz, Mustansar
Mehmood, Irfan
Bashir, Ali Kashif
Nawaz, Raheel
Kim, KyungJoong
Jung, Soon Ki
[J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (03): : 4087 - 4108
[5] Battery Management using LSTM for Manhole Underground System
Nurcahyanto, Himawan
Prihatno, Aji Teguh
Jang, Yeong Min
[J]. 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (IEEE ICAIIC 2021), 2021, : 500 - 503
[6] Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model
Ospina, Juan
Newaz, Alvi
Faruque, M. Omar
[J]. IET RENEWABLE POWER GENERATION, 2019, 13 (07) : 1087 - 1095
[7] A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networks
El Aouifi, Houssam
El Hajji, Mohamed
Es-Saady, Youssef
[J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2024,
[8] Effect of Battery Thermal Management System on Temperature Distribution and Uniformity
Wu, Wenlong
Yuan, Qiuqi
Xu, Xiaoming
[J]. JOURNAL OF ENERGY ENGINEERING, 2022, 148 (05)
[9] Battery Thermal Management System
Venkatakrishnan, S.
Sudhan, Vikram V. M.
Kandappan, Sabari S.
Vishwanath, S.
Saravanan, S.
Pandiyan, P.
[J]. 2020 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATION SYSTEMS (ICACCS), 2020, : 877 - 882
[10] Effect of thermal management system parameters on the temperature characteristics of the battery module
Guo, Qiang
Guo, Jiayong
Liu, Jie
Wang, Hewu
[J]. JOURNAL OF ENERGY STORAGE, 2023, 73

← 1 2 3 4 5 →