期刊
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
卷 41, 期 10, 页码 1436-1451出版社
WILEY
DOI: 10.1002/er.3723
关键词
energy storage system; battery modelling; model design; battery capacity
资金
- Shantou University Scientific Research Foundation [NTF 16002, 15001]
- National Natural Science Foundation of China [51505269]
- Leading Talent Project of Guangdong Province, China
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 (c) 2017 John Wiley & Sons, Ltd.
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