TY - GEN
T1 - Design of Modular Battery Management System with Point-to-point SoC Estimation Algorithm
AU - Caliwag, Angela
AU - Muh, Kumbayoni Lalu
AU - Kang, So Hee
AU - Park, Jeonghun
AU - Lim, Wansu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Lithium-ion (Li-ion) batteries are proven to be the best type of battery in the market especially for electric vehicle (EV) applications. However, Li-ion battery must be monitored properly to prevent operation outside the safe operating area (SOA). In using the Li-ion battery, the voltage, current and SoC must be monitored properly to avoid overvoltage, undervoltage, overcurrent, undercurrent, overcharge and overdischarge scenarios which could be harmful to both the Li-ion battery and to the users. Especially in dynamic systems such as EV in which the load supplied by the battery is not constant and could vary drastically. Thus, battery management systems (BMS) are often designed to perform such monitoring functions. BMS must be designed well to fit a specific application. In this study we design a battery management system that is capable of battery voltage and current monitoring, and SoC estimation. For SoC estimation we propose a point-to-point estimation technique which utilizes machine learning algorithm to estimate the SoC consumed as the EV is driven from one point to another.
AB - Lithium-ion (Li-ion) batteries are proven to be the best type of battery in the market especially for electric vehicle (EV) applications. However, Li-ion battery must be monitored properly to prevent operation outside the safe operating area (SOA). In using the Li-ion battery, the voltage, current and SoC must be monitored properly to avoid overvoltage, undervoltage, overcurrent, undercurrent, overcharge and overdischarge scenarios which could be harmful to both the Li-ion battery and to the users. Especially in dynamic systems such as EV in which the load supplied by the battery is not constant and could vary drastically. Thus, battery management systems (BMS) are often designed to perform such monitoring functions. BMS must be designed well to fit a specific application. In this study we design a battery management system that is capable of battery voltage and current monitoring, and SoC estimation. For SoC estimation we propose a point-to-point estimation technique which utilizes machine learning algorithm to estimate the SoC consumed as the EV is driven from one point to another.
KW - battery management system
KW - circuit design
KW - lithium-ion battery
KW - point-to-point
KW - SoC estimation
UR - https://www.scopus.com/pages/publications/85084089972
U2 - 10.1109/ICAIIC48513.2020.9065224
DO - 10.1109/ICAIIC48513.2020.9065224
M3 - Conference contribution
AN - SCOPUS:85084089972
T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
SP - 701
EP - 704
BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Y2 - 19 February 2020 through 21 February 2020
ER -
