scholarly journals Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

2019 ◽  
Vol 9 (15) ◽  
pp. 3012 ◽  
Author(s):  
Preetpal Singh ◽  
Che Chen ◽  
Cher Ming Tan ◽  
Shyh-Chin Huang
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Model Parameters ◽  
Capacity Fading ◽  
Discharge Cycle ◽  
On Line ◽  
Semi Empirical

A fast and accurate capacity estimation method for lithium-ion batteries is developed. This method applies our developed semi-empirical model to a discharge curve of a lithium-ion battery for the determination of its maximum stored charge capacity after each discharge cycle. This model provides an accurate state-of-health (SoH) estimation with a difference of less than 2.22% when compared with the electrochemistry-based electrical (ECBE) SoH calculation. The model parameters derived from a lithium-ion battery can also be applied to other cells in the same pack with less than 2.5% difference from the complex ECBE model, showing the extendibility of the model. The parameters (k1, k2, and k3) calculated in the work can also be used to study the changes in battery internal structure, such as capacity losses at normal conditions, as well as cycling at high temperatures. The time for estimation after each discharge cycle is only 5 s, making it is suitable for on-line in-situ estimation.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

Si/Cu/C Nanohybrid Lithium-Ion Battery Anode with in Situ Incorporation of Nonagglomerated Super-Small Copper Nanoparticles Based on Epoxy Resin

2021 ◽  
Vol 35 (7) ◽  
pp. 6250-6264
Author(s):  
Kai Fang ◽  
Liujia Ma ◽  
Ya-Jun Cheng ◽  
Senlin Xia ◽  
Zhaohui Yang ◽  
...  
Keyword(s):  
Epoxy Resin ◽  
Lithium Ion Battery ◽  
Copper Nanoparticles ◽  
Lithium Ion ◽  
Battery Anode

A Nonlinear Observer SOC Estimation Method Based on Electrochemical Model for Lithium-Ion Battery

2021 ◽  
Vol 57 (1) ◽  
pp. 1094-1104
Author(s):  
Yuntian Liu ◽  
Rui Ma ◽  
Shengzhao Pang ◽  
Liangcai Xu ◽  
Dongdong Zhao ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Nonlinear Observer ◽  
Soc Estimation ◽  
Electrochemical Model

In situ conductance measurement to investigate roles of conductive additives in lithium ion battery electrodes

1999 ◽  
Vol 1 (9) ◽  
pp. 375-378 ◽  
Author(s):  
Matsuhiko Nishizawa ◽  
Hiromichi Koshika ◽  
Takashi Itoh ◽  
Mohamed Mohamedi ◽  
Takayuki Abe ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Conductance Measurement ◽  
Conductive Additives
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