TY - GEN
T1 - An ARIMA-NARX Model to Predict Li-Ion State of Charge for Unknown Charge/Discharge Rates
AU - Khalid, Asadullah
AU - Sundararajan, Aditya
AU - Sarwat, Arif I.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - State of charge (SOC) prediction for Li-ion batteries is an essential feature of a battery management system (BMS). This paper proposes two Autoregressive Integrated Moving Average (ARIMA) models to independently forecast cell current and voltage, respectively and a Nonlinear Autoregressive neural network (NARX-net) model. The battery parameters corresponding to an unknown higher C-rate are forecasted using the parameters corresponding to known C-rates obtained experimentally using a 3.7V, 3.5Ah Li-ion battery. Four algorithms are then used to train a NARX-net to predict SOC for an unknown higher C-rate, performances of which are compared with the experimentally obtained SOC for C/10. The resulting proposed model combining ARIMA and NARX-net predicts SOC with very low error values.
AB - State of charge (SOC) prediction for Li-ion batteries is an essential feature of a battery management system (BMS). This paper proposes two Autoregressive Integrated Moving Average (ARIMA) models to independently forecast cell current and voltage, respectively and a Nonlinear Autoregressive neural network (NARX-net) model. The battery parameters corresponding to an unknown higher C-rate are forecasted using the parameters corresponding to known C-rates obtained experimentally using a 3.7V, 3.5Ah Li-ion battery. Four algorithms are then used to train a NARX-net to predict SOC for an unknown higher C-rate, performances of which are compared with the experimentally obtained SOC for C/10. The resulting proposed model combining ARIMA and NARX-net predicts SOC with very low error values.
KW - ARIMA
KW - NARX
KW - battery analyzer
KW - state of charge prediction
UR - http://www.scopus.com/inward/record.url?scp=85085166776&partnerID=8YFLogxK
U2 - 10.1109/ITEC-India48457.2019.ITECIndia2019-1
DO - 10.1109/ITEC-India48457.2019.ITECIndia2019-1
M3 - Conference contribution
AN - SCOPUS:85085166776
T3 - 2019 IEEE Transportation Electrification Conference, ITEC-India 2019
BT - 2019 IEEE Transportation Electrification Conference, ITEC-India 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Transportation Electrification Conference, ITEC-India 2019
Y2 - 17 December 2019 through 19 December 2019
ER -
