scholarly journals A Study of Model‐Based Protective Fast‐Charging and Associated Degradation in Commercial Smartphone Cells: Insights on Cathode Degradation as a Result of Lithium Depositions on the Anode

2021 ◽  
Vol 11 (12) ◽  
pp. 2003019
Author(s):  
Münir M. Besli ◽  
Anantharaman Subbaraman ◽  
Farshad R. Pour Safaei ◽  
Christina Johnston ◽  
Gerhard Schneider ◽  
...  
Keyword(s):  
Model Based ◽  
Cathode Degradation ◽  
Fast Charging

Assessment of fast-charging station locations—an integrated model based approach

2020 ◽  
pp. 595-611
Author(s):  
Michael Heilig ◽  
Patrick Plötz ◽  
Tamer Soylu ◽  
Lars Briem ◽  
Martin Kagerbauer ◽  
...  
Keyword(s):  
Integrated Model ◽  
Charging Station ◽  
Model Based ◽  
Fast Charging ◽  
Fast Charging Station

scholarly journals Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1412 ◽  
Author(s):  
Qiang Xing ◽  
Zhong Chen ◽  
Ziqi Zhang ◽  
Xiao Xu ◽  
Tian Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Electric Vehicles ◽  
Demand Forecasting ◽  
Model Based ◽  
Fast Charging ◽  
Human Decision ◽  
Data Driven Approach ◽  
Demand Forecasting Model ◽  
Decision Making Behavior ◽  
Charging Demand

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the ‘Electric Vehicles–Power Grid–Traffic Network’ fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners’ charging behavior.

Model Based Design of Composite Carbonaceous Anode for Li-Ion Battery for Fast Charging Applications

2019 ◽  
Vol 166 (6) ◽  
pp. A1185-A1196 ◽  
Author(s):  
Rajkumar S Patil ◽  
Ashish Khandelwal ◽  
Ki Young Kim ◽  
Krishnan S Hariharan ◽  
Subramanya Mayya Kolake
Keyword(s):  
Li Ion Battery ◽  
Model Based ◽  
Fast Charging ◽  
Li Ion

Model Based Investigation of Lithium Deposition Including an Optimization of Fast Charging Lithium Ion Cells

2021 ◽  
Author(s):  
Lars Hovestadt ◽  
Simon Lux ◽  
Nicolas Koellner ◽  
Anton Schloesser ◽  
Richard Hanke-Rauschenbach
Keyword(s):  
Lithium Ion ◽  
Model Based ◽  
Lithium Ion Cells ◽  
Fast Charging ◽  
Lithium Deposition

scholarly journals Electrochemical Model-Based Fast Charging: Physical Constraint-Triggered PI Control

2021 ◽  
pp. 1-1
Author(s):  
Yang Li ◽  
Mahinda Vilathgamuwa ◽  
Evelina Wikner ◽  
Zhongbao Wei ◽  
Xinan Zhang ◽  
...  
Keyword(s):  
Pi Control ◽  
Physical Constraint ◽  
Model Based ◽  
Fast Charging ◽  
Electrochemical Model

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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