Premium
Design of robust battery capacity model for electric vehicle by incorporation of uncertainties
Author(s) -
Garg Akhil,
Vijayaraghavan V.,
Zhang Jian,
Li Shui,
Liang Xinyu
Publication year - 2017
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3723
Subject(s) - robustness (evolution) , battery (electricity) , battery pack , artificial neural network , electric vehicle , lithium ion battery , sensitivity (control systems) , battery capacity , range (aeronautics) , computer science , engineering , machine learning , power (physics) , physics , biochemistry , chemistry , quantum mechanics , electronic engineering , gene , aerospace engineering
Summary The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.