TY - JOUR
T1 - Fractional order BPNN for estimating state of charge of Lithium-ion Battery under temperature influence
AU - Wang, Yanan
AU - Liao, Xiaozhong
AU - Lin, Da
AU - Yang, Xin
AU - Chen, Yangquan
N1 - Publisher Copyright:
Copyright © 2020 The Authors.
PY - 2020
Y1 - 2020
N2 - State of charge (SOC) estimation for lithium-ion battery (LIB) is always a vital issue for battery management system (BMS) of LIBs. Due to the complex nonlinear characteristics of LIBs, data-driven model and estimation methods have been proposed. Among them, back propagation neural network (BPNN) is one of the widely used machine learning (ML) method. To enhance the performance of BPNN of LIBs, a fractional-order BPNN (FO BPNN) based on fractional-order gradient method is designed for SOC estimation of LIB in this paper. Moreover, temperature acting as key factor is also taken into consideration. Hence, the charging or discharging current, voltage, and temperature are applied as the inputs of the proposed FO BPNN, and SOC is obtained from the network. By Dynamic Stress Test (DST) experiments under five different temperatures of four 18650 LIBs, it proves that the proposed FO BPNN is able to estimate SOC of LIBs accurately in a data-driven way.
AB - State of charge (SOC) estimation for lithium-ion battery (LIB) is always a vital issue for battery management system (BMS) of LIBs. Due to the complex nonlinear characteristics of LIBs, data-driven model and estimation methods have been proposed. Among them, back propagation neural network (BPNN) is one of the widely used machine learning (ML) method. To enhance the performance of BPNN of LIBs, a fractional-order BPNN (FO BPNN) based on fractional-order gradient method is designed for SOC estimation of LIB in this paper. Moreover, temperature acting as key factor is also taken into consideration. Hence, the charging or discharging current, voltage, and temperature are applied as the inputs of the proposed FO BPNN, and SOC is obtained from the network. By Dynamic Stress Test (DST) experiments under five different temperatures of four 18650 LIBs, it proves that the proposed FO BPNN is able to estimate SOC of LIBs accurately in a data-driven way.
KW - Back propagation neural network
KW - Dynamic stress test
KW - Fractional-order gradient method
KW - Lithium-ion battery
KW - State of charge estimation
UR - http://www.scopus.com/inward/record.url?scp=85108029851&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.2056
DO - 10.1016/j.ifacol.2020.12.2056
M3 - Conference article
AN - SCOPUS:85108029851
SN - 2405-8963
VL - 53
SP - 3707
EP - 3712
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -
