Hollow TiO2as an Anode for Lithium Ion Batteries: Synthesis and In Situ Visualization of State of Charge

2015 ◽  
Vol 1 (12) ◽  
pp. 1500256 ◽  
Author(s):  
Xingkang Huang ◽  
Shumao Cui ◽  
Richard C. Wieboldt ◽  
Peter B. Hallac ◽  
Christopher R. Fell ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Charge ◽  
In Situ Visualization

Lithium-Ion Batteries: Hollow TiO2 as an Anode for Lithium Ion Batteries: Synthesis and In Situ Visualization of State of Charge (Adv. Electron. Mater. 12/2015)

2015 ◽  
Vol 1 (12) ◽  
Author(s):  
Xingkang Huang ◽  
Shumao Cui ◽  
Richard C. Wieboldt ◽  
Peter B. Hallac ◽  
Christopher R. Fell ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Charge ◽  
In Situ Visualization

Rapid electrolyte wetting of lithium-ion batteries containing laser structured electrodes: in situ visualization by neutron radiography

2019 ◽  
Vol 102 (9-12) ◽  
pp. 2769-2778 ◽  
Author(s):  
Jan Bernd Habedank ◽  
Florian J. Günter ◽  
Nicolas Billot ◽  
Ralph Gilles ◽  
Tobias Neuwirth ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Neutron Radiography ◽  
Lithium Ion ◽  
In Situ Visualization ◽  
Electrolyte Wetting

scholarly journals Chemical Strain of Graphite-Based Anode during Lithiation and Delithiation at Various Temperatures

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zeyu Xu ◽  
Xiuling Shi ◽  
Xiaoqiang Zhuang ◽  
Zihan Wang ◽  
Sheng Sun ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Mechanical Model ◽  
Lithium Ion ◽  
Testing Temperature ◽  
State Of Charge ◽  
Electrochemical Lithiation ◽  
Different Temperatures ◽  
Strain Cycling ◽  
Chemical Strain

Electrochemical lithiation/delithiation of electrodes induces chemical strain cycling that causes fatigue and other harmful influences on lithium-ion batteries. In this work, a homemade in situ measurement device was used to characterize simultaneously chemical strain and nominal state of charge, especially residual chemical strain and residual nominal state of charge, in graphite-based electrodes at various temperatures. The measurements indicate that raising the testing temperature from 20°C to 60°C decreases the chemical strain at the same nominal state of charge during cycling, while residual chemical strain and residual nominal state of charge increase with the increase of temperature. Furthermore, a novel electrochemical-mechanical model is developed to evaluate quantitatively the chemical strain caused by a solid electrolyte interface (SEI) and the partial molar volume of Li in the SEI at different temperatures. The present study will definitely stimulate future investigations on the electro-chemo-mechanics coupling behaviors in lithium-ion batteries.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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