TY - GEN
T1 - Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System
AU - Kim, Min Joon
AU - Chae, Sung Hun
AU - Moon, Yeon Kug
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.
AB - In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.
KW - Battery management system (BMS)
KW - Electric vehicle (EV)
KW - Extended kalman filter (EKF)
KW - State-of-charge (SOC)
UR - https://www.scopus.com/pages/publications/85100791879
U2 - 10.1109/ISOCC50952.2020.9332950
DO - 10.1109/ISOCC50952.2020.9332950
M3 - Conference contribution
AN - SCOPUS:85100791879
T3 - Proceedings - International SoC Design Conference, ISOCC 2020
SP - 288
EP - 289
BT - Proceedings - International SoC Design Conference, ISOCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International System-on-Chip Design Conference, ISOCC 2020
Y2 - 21 October 2020 through 24 October 2020
ER -