A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries

2021 ◽  
Author(s):  
Xinyan Liu ◽  
Xue-Qiang Zhang ◽  
Xiang Chen ◽  
Gao-Long Zhu ◽  
Chong Yan ◽  
...  
Keyword(s):  
Lithium Batteries ◽  
Data Driven ◽  
Capacity Degradation

scholarly journals Solid Polymer Electrolytes with Flexible Framework of SiO2 Nanofibers for Highly Safe Solid Lithium Batteries

Polymers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1324 ◽  
Author(s):  
Jin Cui ◽  
Zehao Zhou ◽  
Mengyang Jia ◽  
Xin Chen ◽  
Chuan Shi ◽  
...  
Keyword(s):  
Lithium Batteries ◽  
Elevated Temperatures ◽  
Specific Capacity ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Solid Polymer Electrolytes ◽  
Solid Polymer ◽  
Total Conductivity ◽  
Composite Electrolytes ◽  
Capacity Degradation

Composite electrolytes consisting of polymers and three-dimensional (3D) fillers are considered to be promising electrolytes for solid lithium batteries owing to their virtues of continuous lithium-ion pathways and good mechanical properties. In the present study, an electrolyte with polyethylene oxide–lithium (bis trifluoromethyl) sulfate–succinonitrile (PLS) and frameworks of three-dimensional SiO2 nanofibers (3D SiO2 NFs) was prepared. Taking advantage of the highly conductive interfaces between 3D SiO2 NFs and PLS, the total conductivity of the electrolyte at 30 °C was approximately 9.32 × 10−5 S cm−1. With a thickness of 27 μm and a tensile strength of 7.4 MPa, the electrolyte achieved an area specific resistance of 29.0 Ω cm2. Moreover, such a 3D configuration could homogenize the electrical field, which was beneficial for suppressing dendrite growth. Consequently, Li/LiFePO4 cells assembled with PLS and 3D SiO2 NFs (PLS/3D SiO2 NFs), which delivered an original specific capacity of 167.9 mAh g−1, only suffered 3.28% capacity degradation after 100 cycles. In particular, these cells automatically shut down when PLS was decomposed above 400 °C, and the electrodes were separated by the solid framework of 3D SiO2 NFs. Therefore, the solid lithium batteries based on composite electrolytes reported here offer high safety at elevated temperatures.

scholarly journals Data-driven prediction of battery cycle life before capacity degradation

Nature Energy ◽  
2019 ◽  
Vol 4 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Kristen A. Severson ◽  
Peter M. Attia ◽  
Norman Jin ◽  
Nicholas Perkins ◽  
Benben Jiang ◽  
...  
Keyword(s):  
Cycle Life ◽  
Data Driven ◽  
Capacity Degradation

Data Driven Prognostics With Lack of Training Data Sets

2015 ◽  
Author(s):  
Zhimin Xi ◽  
Xiangxue Zhao
Keyword(s):  
Lithium Ion ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Data Sets ◽  
The Neural Network ◽  
Typical Data ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Network Similarity

Data-driven prognostics typically requires sufficient offline training data sets for accurate remaining useful life (RUL) prediction of engineering products. This paper investigates performances of typical data-driven methodologies when the amount of training data sets is insufficient. The purpose is to better understand these methodologies especially when offline training datasets are insufficient. The neural network, similarity-based approach, and copula-based sampling approach were investigated when only three run-to-failure training units were available. The example of lithium-ion (Li-ion) battery capacity degradation was employed for the demonstration.

Elucidation of Capacity Degradation for Graphite in Sulfide-Based All-Solid-State Lithium Batteries: A Void Formation Mechanism

2020 ◽  
Vol 3 (6) ◽  
pp. 5472-5478
Author(s):  
Kentaro Kuratani ◽  
Atsushi Sakuda ◽  
Tomonari Takeuchi ◽  
Hironori Kobayashi
Keyword(s):  
Solid State ◽  
Formation Mechanism ◽  
Lithium Batteries ◽  
Void Formation ◽  
Capacity Degradation

State of Health Estimation of Lithium-Ion Batteries Based on Combination of Gaussian Distribution Data and Least Squares Support Vector Machines Regression

2018 ◽  
Vol 929 ◽  
pp. 93-102
Author(s):  
Didik Djoko Susilo ◽  
Achmad Widodo ◽  
Toni Prahasto ◽  
Muhammad Nizam
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Support Vector ◽  
Degradation Data ◽  
Starting Point ◽  
Capacity Degradation ◽  
Battery Capacity ◽  
Useful Life

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

Progress and perspectives on halide lithium conductors for all-solid-state lithium batteries

2020 ◽  
Vol 13 (5) ◽  
pp. 1429-1461 ◽  
Author(s):  
Xiaona Li ◽  
Jianwen Liang ◽  
Xiaofei Yang ◽  
Keegan R. Adair ◽  
Changhong Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Solid State ◽  
Lithium Batteries ◽  
Electrochemical Stability ◽  
Superionic Conductors ◽  
Fundamental Understanding ◽  
Potential Applications ◽  
Synthesis Routes

This review focuses on fundamental understanding, various synthesis routes, chemical/electrochemical stability of halide-based lithium superionic conductors, and their potential applications in energy storage as well as related challenges.

Sign in / Sign up

Export Citation Format

Share Document