TY - GEN
T1 - Estimation of battery parameters of the equivalent circuit models using meta-heuristic techniques
AU - Sangwan, Venu
AU - Sharma, Avinash
AU - Kumar, Rajesh
AU - Rathore, A. K.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/13
Y1 - 2017/2/13
N2 - This paper describes the parametrization and comparison of dynamic battery models for Li-ion battery. The estimation of battery parameters deploys experimental methods that are expensive, require high computational power and are time-consuming. Hence, two commonly used equivalent circuit battery models are tested using GA, PSO, ASMO and DE optimization techniques. Estimation has been done by the estimated voltage curves closeness to the known catalog voltage curve. Feasibility of various optimization techniques is evaluated by the accuracy of predicted model and the rate of convergence in predicting the model parameters. Investigation showed that DE algorithm has the best accuracy among meta-heuristic optimizers for battery parameter estimation for first order model while ASMO has best accuracy for the second order model. Further analysis showed that for both the models, DE algorithm was reliable as well as computationally less expensive compared to other optimization techniques. Also the second order RC model proved out to be more robust as for almost all scenarios the performance of optimization algorithm improved by use of this model.
AB - This paper describes the parametrization and comparison of dynamic battery models for Li-ion battery. The estimation of battery parameters deploys experimental methods that are expensive, require high computational power and are time-consuming. Hence, two commonly used equivalent circuit battery models are tested using GA, PSO, ASMO and DE optimization techniques. Estimation has been done by the estimated voltage curves closeness to the known catalog voltage curve. Feasibility of various optimization techniques is evaluated by the accuracy of predicted model and the rate of convergence in predicting the model parameters. Investigation showed that DE algorithm has the best accuracy among meta-heuristic optimizers for battery parameter estimation for first order model while ASMO has best accuracy for the second order model. Further analysis showed that for both the models, DE algorithm was reliable as well as computationally less expensive compared to other optimization techniques. Also the second order RC model proved out to be more robust as for almost all scenarios the performance of optimization algorithm improved by use of this model.
KW - Battery Electric Vehicle
KW - Equivalent Models
KW - Meta-Heuristics Techniques
KW - Parameter Estimation
UR - http://www.scopus.com/inward/record.url?scp=85015913928&partnerID=8YFLogxK
U2 - 10.1109/ICPEICES.2016.7853240
DO - 10.1109/ICPEICES.2016.7853240
M3 - Conference contribution
AN - SCOPUS:85015913928
T3 - 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016
BT - 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016
Y2 - 4 July 2016 through 6 July 2016
ER -