A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries

作者：Ye, Min; Guo, Hui; Xiong, Rui^*; Yu, Quanqing^*

来源：Energy, 2018, 144: 789-799.

DOI：10.1016/j.energy.2017.12.061

• Electric vehicles
• Battery
• Multi-time scales
• State estimation
• Dual particle filters

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.

• 出版日期2018-2-1
• 单位北京理工大学; 长安大学