Real-Time Prediction of Anode Potential in Li-Ion Batteries Using Long Short-Term Neural Networks for Lithium Plating Prevention

2019 ◽  
Vol 166 (10) ◽  
pp. A1893-A1904 ◽  
Author(s):  
Xianke Lin
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Anode Potential ◽  
Li Ion Batteries ◽  
Short Term ◽  
Time Prediction ◽  
Li Ion

Real-time monitoring of stress development during electrochemical cycling of electrode materials for Li-ion batteries: overview and perspectives

2019 ◽  
Vol 7 (41) ◽  
pp. 23679-23726 ◽  
Author(s):  
Manoj K. Jangid ◽  
Amartya Mukhopadhyay
Keyword(s):  
Real Time ◽  
Electrode Materials ◽  
Electrochemical Potential ◽  
Li Ion Batteries ◽  
Stress Development ◽  
Electrochemical Cycling ◽  
Li Ion ◽  
Real Time Information ◽  
Time Information

Monitoring stress development in electrodes in-situ provides a host of real-time information on electro-chemo-mechanical aspects as functions of SOC and electrochemical potential.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

scholarly journals Li-Ion Batteries Parameter Estimation With Tiny Neural Networks Embedded on Intelligent IoT Microcontrollers

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 122135-122146
Author(s):  
Giulia Crocioni ◽  
Danilo Pau ◽  
Jean-Michel Delorme ◽  
Giambattista Gruosso
Keyword(s):  
Neural Networks ◽  
Parameter Estimation ◽  
Li Ion Batteries ◽  
Li Ion
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