TY - GEN
T1 - Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter
AU - Sangwan, Venu
AU - Vakacharla, Venkata R.
AU - Kumar, Rajesh
AU - Rathore, Akshay K.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.
AB - An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.
KW - Battery Electric Vehicle
KW - Battery Management System
KW - Extended Kalman Filter
KW - Li-ion batteries
KW - State of Charge estimation
UR - http://www.scopus.com/inward/record.url?scp=85049888580&partnerID=8YFLogxK
U2 - 10.1109/ICPES.2017.8387385
DO - 10.1109/ICPES.2017.8387385
M3 - Conference contribution
AN - SCOPUS:85049888580
T3 - 2017 7th International Conference on Power Systems, ICPS 2017
SP - 726
EP - 731
BT - 2017 7th International Conference on Power Systems, ICPS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Power Systems, ICPS 2017
Y2 - 21 December 2017 through 23 December 2017
ER -
