TY - GEN
T1 - Optimal parameter estimation of battery model for pivotal automotive battery management system
AU - Sangwan, Venu
AU - Sharma, Avinash
AU - Kumar, Rajesh
AU - Rathore, Akshay Kumar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/12
Y1 - 2017/7/12
N2 - The battery management system (BMS) is an integral part of a battery electric vehicle (BEV). To ensure the optimal performance of the battery, BMS should measure estimation battery parameters and battery capacity over battery life accurately. The traditional procedure for evaluation of battery parameters are time-consuming, high-priced and demand high computational power. A methodology based on heuristic optimization techniques has been implemented to overcome this problem. The proposed model was tested using six different state-of-the-art heuristic optimization approaches. Evaluation of parameters has been performed by optimally resembling predicted voltage curve's from model to curve acquired from manufacturers catalog. The practicability of particular optimization approaches is assessed by the precision of estimated model and convergence rate of prediction. Investigation showed that Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO) algorithms demonstrate sufficient accuracy amongst heuristic optimizers for parameter evaluation of battery. Further analysis showed that DE algorithm produced the most consistent results with high convergence rate for accurate estimation of the parameter.
AB - The battery management system (BMS) is an integral part of a battery electric vehicle (BEV). To ensure the optimal performance of the battery, BMS should measure estimation battery parameters and battery capacity over battery life accurately. The traditional procedure for evaluation of battery parameters are time-consuming, high-priced and demand high computational power. A methodology based on heuristic optimization techniques has been implemented to overcome this problem. The proposed model was tested using six different state-of-the-art heuristic optimization approaches. Evaluation of parameters has been performed by optimally resembling predicted voltage curve's from model to curve acquired from manufacturers catalog. The practicability of particular optimization approaches is assessed by the precision of estimated model and convergence rate of prediction. Investigation showed that Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO) algorithms demonstrate sufficient accuracy amongst heuristic optimizers for parameter evaluation of battery. Further analysis showed that DE algorithm produced the most consistent results with high convergence rate for accurate estimation of the parameter.
KW - Battery Electric Vehicle
KW - Battery management System
KW - Equivalent electrical model
KW - Heuristic optimizations
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=85026733055&partnerID=8YFLogxK
U2 - 10.1109/EEEIC.2017.7977705
DO - 10.1109/EEEIC.2017.7977705
M3 - Conference contribution
AN - SCOPUS:85026733055
T3 - Conference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017
BT - Conference Proceedings - 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2017
Y2 - 6 June 2017 through 9 June 2017
ER -
