Modeling and study of lithium ion cell at N ano scale

1. Modeling and study of lithium ion cell at Nano scale

2. 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.

3. 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.

4. 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.

5. 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.

6. Phase diagram

7. 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.

8. 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.

9. 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.

10. 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 .

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

12. 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.

13. 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)

14. Branch Requirements • Materials Science/Mechanical • Computer Science