Design of Lithium Battery Intelligent Management System

Lithium batteries have the advantages of safe and reliable power supply, low maintenance costs, small footprint, often used as the preferred solution for power supply in data centers. To solve the problems of non-linear charging and discharging curves in lithium batteries, and uneven charging and discharging caused by multiple lithium batteries in series and parallel, we design an intelligent comprehensive management system for lithium power batteries used for data centers. This system includes a master control module and a slave control module. The latter is mainly responsible for collecting voltage, current, and temperature information of lithium batteries; The main control module will perform fault detection, estimate battery charge state estimation algorithm, display data on LCD screen, and communicate with the upper computer using CAN protocol based on the information collected from the control module, transmitting the battery data situation to the user in real-time. This system uses the Internet of Things communication technology to obtain the battery status information collected on the main control board, realize the information interaction between the computer and the lithium Battery management system, and design and optimize the state of charge estimation algorithm to improve the accuracy of lithium battery data so as to improve the security and stability of the lithium battery management box. Compared with existing technologies, the accuracy of the state of charge (SOC) estimation algorithm has been improved by using an improved ampere hour integration method combined with a neural network. At the same time, the system can transmit data to users in real time, further ensuring the security of the data center.

Authors

Institutions

Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/spic62469.2024.10691525
Primary Topic
Wireless Sensor Networks and IoT
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of Lithium Battery Intelligent Management System

Nan Xu, Lei Jin, Huadong Yang
Wireless Sensor Networks and IoT
article

Design of Lithium Battery Intelligent Management System

Nan Xu, Lei Jin, Huadong Yang
article en

Abstract

Lithium batteries have the advantages of safe and reliable power supply, low maintenance costs, small footprint, often used as the preferred solution for power supply in data centers. To solve the problems of non-linear charging and discharging curves in lithium batteries, and uneven charging and discharging caused by multiple lithium batteries in series and parallel, we design an intelligent comprehensive management system for lithium power batteries used for data centers. This system includes a master control module and a slave control module. The latter is mainly responsible for collecting voltage, current, and temperature information of lithium batteries; The main control module will perform fault detection, estimate battery charge state estimation algorithm, display data on LCD screen, and communicate with the upper computer using CAN protocol based on the information collected from the control module, transmitting the battery data situation to the user in real-time. This system uses the Internet of Things communication technology to obtain the battery status information collected on the main control board, realize the information interaction between the computer and the lithium Battery management system, and design and optimize the state of charge estimation algorithm to improve the accuracy of lithium battery data so as to improve the security and stability of the lithium battery management box. Compared with existing technologies, the accuracy of the state of charge (SOC) estimation algorithm has been improved by using an improved ampere hour integration method combined with a neural network. At the same time, the system can transmit data to users in real time, further ensuring the security of the data center.

Shenyang Ligong University (CN), Shenyang Jianzhu University (CN)
Affordable and clean energy
Openalex Percentile: Top 81%
Wireless Sensor Networks and IoT
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.