scholarly journals Cycle life testing of lithium batteries: The effect of load-leveling

2018 ◽  
pp. 1773-1786 ◽  
Author(s):  
Jingyuan Zhao ◽  
Keyword(s):  
Lithium Batteries ◽  
Cycle Life ◽  
Life Testing ◽  
Load Leveling

Cycle life testing and modeling of graphite/LiCoO2 cells under different state of charge ranges

2016 ◽  
Vol 327 ◽  
pp. 394-400 ◽  
Author(s):  
Saurabh Saxena ◽  
Christopher Hendricks ◽  
Michael Pecht
Keyword(s):  
State Of Charge ◽  
Cycle Life ◽  
Life Testing

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

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.

Performance of large-scale secondary lithium batteries for electric vehicles and home-use load-leveling systems

2003 ◽  
Vol 119-121 ◽  
pp. 887-892 ◽  
Author(s):  
K Takei ◽  
K Ishihara ◽  
K Kumai ◽  
T Iwahori ◽  
K Miyake ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Lithium Batteries ◽  
Large Scale ◽  
Secondary Lithium Batteries ◽  
Load Leveling

High-Energy All-Solid-State Lithium Batteries with Ultralong Cycle Life

Nano Letters ◽  
2016 ◽  
Vol 16 (11) ◽  
pp. 7148-7154 ◽  
Author(s):  
Xiayin Yao ◽  
Deng Liu ◽  
Chunsheng Wang ◽  
Peng Long ◽  
Gang Peng ◽  
...  
Keyword(s):  
Solid State ◽  
Lithium Batteries ◽  
High Energy ◽  
Cycle Life

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

Recent progress in advanced electrode materials, separators and electrolytes for lithium batteries

2018 ◽  
Vol 6 (42) ◽  
pp. 20564-20620 ◽  
Author(s):  
Hailin Zhang ◽  
Hongbin Zhao ◽  
Muhammad Arif Khan ◽  
Wenwen Zou ◽  
Jiaqiang Xu ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Power Density ◽  
Lithium Batteries ◽  
Recent Progress ◽  
Electrode Materials ◽  
Lithium Ion ◽  
High Energy ◽  
Cycle Life ◽  
Long Cycle Life ◽  
Negative Electrodes

This article comprehensively reviews the recent progress in the development of key components of lithium-ion batteries, including positive/negative electrodes, electrolytes and separators. The necessity of developing batteries with high energy/power density and long cycle-life is emphasized both in terms of industrial and academic perspectives.

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