Modeling and study of lithium ion cell at Nano scale.

Slides:



Advertisements
Similar presentations
Introduction to Neural Networks
Advertisements

How to power your smartphone for a week!. 2 Presentation name What is a battery? A device that stores chemical energy in its active materials and converts.
Neural Networks in Financial Analysis
PH 0101 Unit-5 Lecture-61 Introduction A fuel cell configuration Types of fuel cell Principle, construction and working Advantage, disadvantage and application.
Supercapacitor Energy Storage System for PV Power Generation
Have you ever held a wire that has current flowing through it? If so what did you notice about it? The wire gets hot. The increase in temperature causes.
A New Design Tool for Nanoplasmonic Solar Cells using 3D Full Wave Optical Simulation with 1D Device Transport Models Liming Ji* and Vasundara V. Varadan.
Chemical Equilibrium The applications of fuel cells and secondary cells. Cho Sin Lui F.6A (2)
Secondary Cells and Fuel Cells To Wing Yin F.6B (11)
The Significance of Carbon Nanotubes and Graphene in Batteries and Supercapacitors Elena Ream and Solomon Astley.
INTRODUCTION COMPUTATIONAL MODELS. 2 What is Computer Science Sciences deal with building and studying models of real world objects /systems. What is.
COGNITIVE NEUROSCIENCE
Introduction to Neural Networks Simon Durrant Quantitative Methods December 15th.
Spreadsheet Modeling & Decision Analysis:
Hydrogen Fuel Cells Maddie Droher. What is a fuel cell? An energy conversion device set to replace combustion engines and additional batteries in a number.
6A Luk Pui Lam (7). Lead-acid accumulator Lithium battery charged A secondary cell is any kind of electrolytic cell in which the electrochemical reaction.
PH0101 UNIT-5 LECTURE 7 Introduction Types of battery Lithium battery
INTEGRATION OF ARTIFICIAL INTELLIGENCE [AI] SYSTEMS FOR NUCLEAR POWER PLANT SURVEILLANCE & DIAGNOSTICS.
Brian Kim 5/16/13.  Introduction  What are batteries?  Objective?  Materials and Method  Results and Discussion  Data and Evidence of the Data 
Density Functional Theory HΨ = EΨ Density Functional Theory HΨ = EΨ E-V curve E 0 V 0 B B’ E-V curve E 0 V 0 B B’ International Travel What we do Why computational?
Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.
 The most intelligent device - “Human Brain”.  The machine that revolutionized the whole world – “computer”.  Inefficiencies of the computer has lead.
National Science Foundation Dynamic Phenomena in Complex Oxides for Electrochemical Energy Storage Ying S. Meng, University of California-San Diego, DMR.
Lateef Taiwo, Biju Shrestha (PhD Candidate), P. Novak (PhD Candidate), and David Wetz, Ph.D Department of Electrical Engineering, The University of Texas.
Introduction to machine learning and data mining 1 iCSC2014, Juan López González, University of Oviedo Introduction to machine learning Juan López González.
Lithium-Ion Battery By QingjieBao. A lithium-ion battery (sometimes Li-ion battery or LIB) is a family of rechargeable battery types in which lithium.
National Science Foundation Dynamic Phenomena in Complex Oxides for Electrochemical Energy Storage Ying S. Meng, University of California-San Diego, DMR.
Lithium-Ion Battery Anodes Juchuan Li, Fuqian Yang, and Yang-Tse Cheng Department of Chemical & Materials Engineering, University of Kentucky Artificial.
Calderglen High School Calderglen High School 1 Electricity is a flow of …..? answer electrons.
Statistical Tools for Solar Resource Forecasting Vivek Vijay IIT Jodhpur Date: 16/12/2013.
NANO BATTERIES By Soumya Yadala UNIVERSITY OF TULSA.
Laboratory of Molecular Simulations of Nano- and Bio-Materials Venkat Ganesan “Where molecules and models meet applications” Computations Fluid Mechanics.
THE UNIVERSITY OF AT AUSTIN Department of Chemical Engineering Institute for Computational Engineering & Sciences Texas Materials Institute Institute for.
Cells and Batteries A cell is a unit which includes two electrodes and one electrolyte.
QCAdesigner – CUDA HPPS project
LITHIUM POLYMER BATTERIES DAVID AUSLENDER NICHOLAS FORTENBERRY.
Cells and Batteries An electrical battery is one or more electrochemical cells that convert stored chemical energy into electrical energy Cells are portable.
Neural Networks Presented by M. Abbasi Course lecturer: Dr.Tohidkhah.
Modeling of the device SEYED AHMAD SHAHAHMADI Principal supervisor: Prof. Dr. Nowshad Amin.
Molecular Simulations of Nano- and Bio-Materials Venkat Ganesan Computations Fluid Mechanics Biology Statistical Mechanics Venkat Ganesan: CPE 3.414,
Example of prediction quality for the first (10 min) and last (3 h) prediction horizon element for the same sequence of WF Danilo power production events.
Introduction Introduction Background Background Objectives Objectives Design Specifications Design Specifications Risk Analysis Risk Analysis Budget Budget.
Unit D Review Electricity. How can you explain two charged objects “sticking” to one another? Opposite charges, movement of electrons!
Modelling and Simulation of Passive Optical Devices João Geraldo P. T. dos Reis and Henrique J. A. da Silva Introduction Integrated Optics is a field of.
Electrochemical Reactions. Anode: Electrons are lost due to oxidation. (negative electrode) Cathode: Electrons are gained due to reduction. (positive.
Electrochemical Cells
8.1 ELECTRIC POTENTIAL ENERGY AND VOLTAGE BC Science 9: p
Seth Kulman Faculty Sponsor: Professor Gordon H. Dash.
Artificial Neural Networks and Their Applications Prof. Les Sztandera.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 6: Artificial Neural Networks for Data Mining.
Electrochemical Reactions. Anode: Electrons are lost due to oxidation. (negative electrode) Cathode: Electrons are gained due to reduction. (positive.
Circuit Electricity. Electric Circuits The continuous flow of electrons in a circuit is called current electricity. Circuits involve… –Energy source,
Project Overview  Introduction  Frame Build  Motion  Power  Control  Sensors  Advanced Sensors  Open design challenges  Project evaluation.
MICRO-LEVEL ENERGY HARVESTING Prakash Hiremath. M 1DA06EC061.
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 6: Artificial Neural Networks for Data Mining.
Dynamical Systems Modeling
Secondary Cell Nickel Cadmium (NiCd) Cells and Batteries
Outline Of Today’s Discussion
Date of download: 10/9/2017 Copyright © ASME. All rights reserved.
Electric Circuits Science 9.
How can minerals effect batteries to create a stronger and longer-lasting charge By: Sam G and Sathvik R.
How Can Minerals Effect Batteries to Create A Stronger And Longer-lasting Charge By: Sam G and Sathvik R.
Artificial Intelligence ppt
Chemistry AS – Redox reactions
Electricity 2 objectives.
Optimization of PHEV/EV Battery Charging
OVERVIEW OF BIOLOGICAL NEURONS
8.1 Electric Potential Energy & Voltage
V.P. Nagorny, V.N. Khudik Plasma Dynamics Corporation, USA
EV Battery A Perspective from the OEM
Presentation transcript:

Modeling and study of lithium ion cell at Nano scale

Li ion batteries Lithium-ion batteries are common in consumer electronics. They are one of the most popular types of rechargeable battery for portable electronics, with one of the best energy densities, no memory effect, and only a slow loss of charge when not in use. Beyond consumer electronics, LIBs are also growing in popularity for military, electric vehicle and aerospace applications.

What we aspire to do In our project we wish to simulate a Li-ion cell functional behaviour and estimate the electronic and mechanical properties in a Nano scale. The electronic, mechanical processes will be different in the one dimensional Nano structures from their bulk counterparts. By Simulating in a Nano scale, from the results we hope to achieve might find their application in fabricating novel active devices with improved functionalities with potential applications in various fields.

How we plan do (Bulk scale) To compute and construct phase diagrams, to compare the relative thermodynamic stability of phases using python. Approximating the electrochemical windows for the electrolytes and estimating the electronic parameters and stability of electrodes. Compute electrochemical, mechanical and thermodynamic properties and save a database.

How we plan to do (Nano scale) Understanding the substrata phenomena we will build a similar database for all our materials in a nano scale. Predict the better possibilities and combinations using Artificial neural networks. We will build a similar electrochemical, mechanical and thermodynamic database for elements in nano scale.

Phase diagram

Present day Challenges To design and develop new materials for lithium ion batteries, experimentalists have focused on mapping the synthesis–structure– property relations in different materials’ families. This approach is time/labor consuming and not very efficient due to the numerous possible chemistries.

How will we overcome? We will be using Artificial neural networks for predicting possible combinations. For the predictions from ANN, we will be computing the electrochemical, mechanical and thermodynamic feasibility by using PYTHON.

Neural networks An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. ANNs are considered nonlinear statistical data modeling tools where the complex relationships between inputs and outputs are modeled or patterns are found.

Using Python We will be manipulating the data obtained from both the Bulk and Nano scale database to predict the best outcome. From this data we will be creating a structure and manipulate the structures under various conditions. Performing high through put transformations to this data.

Requirements A computer with a GPU for good computational capabilites. Thermodynamic and electronic databases. Annual license for Mathematica or Matlab

Outcomes of the project We will be having a database for future material design in both bulk and nano scale. The possible outcomes from our project might find their use in some novel applications.

Deadlines For building bulk scale database – October(one month). For computing and building nano scale database – November and December(two months). For predicting possible combinations and approximating their parameters – January(one month)

Branch Requirements Materials Science/Mechanical Computer Science