scholarly journals Improving Cell Resistance and Cycle Life with Solvate-Coated Thiophosphate Solid Electrolytes in Lithium Batteries

2018 ◽  
Vol 11 (2) ◽  
pp. 2014-2021 ◽  
Author(s):  
Maria A. Philip ◽  
Patrick T. Sullivan ◽  
Ruixian Zhang ◽  
Griffin A. Wooley ◽  
Stephanie A. Kohn ◽  
...  
Keyword(s):  
Lithium Batteries ◽  
Solid Electrolytes ◽  
Cycle Life ◽  
Cell Resistance

scholarly journals Realizing poly(ethylene oxide) as a polymer for solid electrolytes in high voltage lithium batteries via simple modification of the cell setup

2021 ◽  
Author(s):  
Lukas Stolz ◽  
Gerrit Homann ◽  
Martin Winter ◽  
Johannes Kasnatscheew
Keyword(s):  
Ethylene Oxide ◽  
High Voltage ◽  
Lithium Batteries ◽  
Polymer Electrolytes ◽  
Solid Electrolytes ◽  
Simple Modification ◽  
Poly Ethylene Oxide ◽  
Benchmark System ◽  
Short Circuits ◽  
Poly Ethylene

Cell failure of polymer electrolytes is rather the result of short circuits instead of assumed electrolyte oxidation. A spacer with a constant and defined distance can avoid this failure, thus realize a benchmark system for a more systematic R&D.

scholarly journals Unveiling Interfacial Li-Ion Dynamics in Li7La3Zr2O12/PEO(LiTFSI) Composite Polymer-Ceramic Solid Electrolytes for All-Solid-State Lithium Batteries

2021 ◽  
Author(s):  
Mauricio R. Bonilla ◽  
Fabián A. García Daza ◽  
Pierre Ranque ◽  
Frederic Aguesse ◽  
Javier Carrasco ◽  
...  
Keyword(s):  
Solid State ◽  
Lithium Batteries ◽  
Solid Electrolytes ◽  
Ion Dynamics ◽  
Composite Polymer ◽  
Li Ion

Kinetically Stable Anode Interface for Li3YCl6-Based All-Solid-State Lithium Batteries

2021 ◽  
Author(s):  
Weixiao Ji ◽  
Dong Zheng ◽  
Xiaoxiao Zhang ◽  
Tianyao Ding ◽  
Deyang Qu
Keyword(s):  
Solid State ◽  
Ionic Conductivity ◽  
Oxidative Stability ◽  
Lithium Batteries ◽  
Solid Electrolytes ◽  
Metal Anode ◽  
Li Metal Anode

Despite excellent ionic conductivity and electrochemical oxidative stability, the emerging halide-based solid electrolytes suffer from inherent instability toward Li metal anode. A thick and resistive interface can be formed by...

Double-Protected Layers with Solid–Liquid Hybrid Electrolytes for Long-Cycle-Life Lithium Batteries

2022 ◽  
Author(s):  
Jiantao Tang ◽  
Leidanyang Wang ◽  
Changhao Tian ◽  
Chunguang Chen ◽  
Tao Huang ◽  
...  
Keyword(s):  
Lithium Batteries ◽  
Cycle Life ◽  
Long Cycle ◽  
Long Cycle Life ◽  
Solid Liquid

Utmost limits of various solid electrolytes in all-solid-state lithium batteries: A critical review

2019 ◽  
Vol 109 ◽  
pp. 367-385 ◽  
Author(s):  
Zhijun Wu ◽  
Zhengkun Xie ◽  
Akihiro Yoshida ◽  
Zhongde Wang ◽  
Xiaogang Hao ◽  
...  
Keyword(s):  
Solid State ◽  
Lithium Batteries ◽  
Critical Review ◽  
Solid Electrolytes

scholarly journals Probabilistic Prediction Algorithm for Cycle Life of Energy Storage in Lithium Battery

2019 ◽  
Vol 10 (1) ◽  
pp. 7 ◽  
Author(s):  
Xue Wang ◽  
Chunbin Gao ◽  
Meng Sun
Keyword(s):  
Neural Network ◽  
Energy Storage ◽  
Probability Distribution ◽  
Lithium Batteries ◽  
Residual Life ◽  
Future Trend ◽  
Wavelet Neural Network ◽  
Cycle Life ◽  
Prediction Algorithm ◽  
Probabilistic Prediction

Lithium batteries are widely used in energy storage power systems such as hydraulic, thermal, wind and solar power stations, as well as power tools, military equipment, aerospace and other fields. The traditional fusion prediction algorithm for the cycle life of energy storage in lithium batteries combines the correlation vector machine, particle filter and autoregressive model to predict the cycle life of lithium batteries, which are subjected to many uncertainties in the prediction process and to inaccurate prediction results. In this paper, a probabilistic prediction algorithm for the cycle life of energy storage in lithium batteries is proposed. The LS-SVR prediction model was trained by a Bayesian three-layer reasoning. In the iterative prediction phase, the Monte Carlo method was used to express and manage the uncertainty and its transitivity in a multistep prediction and to predict the future trend of a lithium battery’s health status. Based on the given failure threshold, the probability distribution of the residual life was obtained by counting the number of particles passing through the threshold. The wavelet neural network was used to study the sample data of lithium batteries, and the mapping relationship between the probability distribution of the residual life of lithium batteries and the unknown values were established. According to this mapping relation and the probability distribution of the residual life of lithium batteries, the health data could be deduced and then iterated into the input of the wavelet neural network. In this way, the predicted degradation curve and the cycle life of lithium batteries could be obtained. The experimental results show that the proposed algorithm has good adaptability and high prediction efficiency and accuracy, with the mean error of 0.17 and only 1.38 seconds by average required for prediction.

scholarly journals Progress and Perspective of Ceramic/Polymer Composite Solid Electrolytes for Lithium Batteries

2020 ◽  
Vol 7 (5) ◽  
pp. 1903088 ◽  
Author(s):  
Song Li ◽  
Shi‐Qi Zhang ◽  
Lu Shen ◽  
Qi Liu ◽  
Jia‐Bin Ma ◽  
...  
Keyword(s):  
Polymer Composite ◽  
Lithium Batteries ◽  
Solid Electrolytes ◽  
Composite Solid Electrolytes ◽  
Composite Solid
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