期刊
ENERGY
卷 144, 期 -, 页码 789-799出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2017.12.061
关键词
Electric vehicles; Battery; Multi-time scales; State estimation; Dual particle filters
资金
- National Natural Science Foundation of China [U1564206]
- Beijing Municipal Science and Technology Project [Z171100000917013]
- Key Laboratory of Road Construction Technology and Equipment (Chang'an University), MOE [310825171105, 310825171133]
Obtaining an estimation of the parameters.and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is modeled using the Thevenin model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages. (C) 2017 Elsevier Ltd. All rights reserved.
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