Opportunities
Fuse Summer Internship 2022
Using Machine Learning Techniques to Discover Lithium- Sulfur Battery Models
Project Description
The need for high-capacity batteries is increasing rapidly due to our more electrified and mobilised world. Lithium-ion batteries have served use-cases such as phones and electric vehicles well, but the demand for greater performance requires the next generation of beyond-Lithium-ion batteries. One promising new battery technology is the Lithium-Sulfur battery. Such batteries have a theoretical specific energy capacity 3-5 times larger than traditional Lithium-ion batteries and are far more environmentally friendly. However, the current understanding of Li-S batteries is limited due to their highly complex internal electrochemical processes. The difficulty in understanding such batteries hinders the ability to enhance the technology enough for widespread adoption.
The project aims to incorporate techniques frequently used in machine learning to discover a correct model for Li-S batteries. These techniques will be applied to experimental data in order to create a model which both matches the data and is physically interpretable. This project will directly help to increase the applicability of Li-S models to a wider range of purposes, such as cell design for improved performance.
Supervisors:
Dr. Monica Marinescu, Dr. Michael Cornish
University:
Imperial College London
Location:
In-person, hybrid, or remote positions are available.
Start date:
The internship is a full-time role for 8 weeks during June – September 2022.
Eligibility:
- Be registered full-time undergraduate student from a UK university.
- Undertake the internship within the years of their undergraduate study (i.e., not in final year or during a subsequent Masters’ programme).
- Not have been a FUSE intern in a previous year
Funding:
A salary of £9.90 / hour across the UK or £11.05 / hour in London will be provided. This will be determined by the working address of the appointee, not the university's location. The funding is provided by the Faraday Institution.
Additional activities:
During the FUSE internship you will be able to attend Faraday Masterclasses and cohort events which will focus on a variety of topics to further develop your understanding of career opportunities in battery sector. At the end of the programme, you will be invited to share a poster about your work and prizes will be awarded.
Application:
In order to apply for a Faraday Undergraduate Summer Experience (FUSE) 2022 internship, you should be comfortable with Python and Multivariate Calculus. Familiarity with the Gradient Descent method and Ordinary Differential Equations (ODEs) will be beneficial. An understanding of how batteries work is desirable, but not essential.
You will be working with a leading research group to develop models in Python which can be cited by subsequent researchers. You will become more familiar with Python, ODEs, techniques used in Machine Learning, physical modelling, and battery technology. As part of The Faraday Institution’s 2022 intern cohort you will enter an end-of-project poster competition – the winners of which will be invited to present their poster at the Faraday Institution Conference in November 2022.
To express your interest, please fill out this form by April 8th, 2022. We will be in contact shortly thereafter.
Diversity:
The Faraday Institution is committed to creating a dynamic and diverse pool of talent for the fields of battery technology and energy storage. We at Imperial College are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender identity, sex, or sexual orientation. We are an Athena SWAN Silver Award winner, a Disability Confident Leader and a Stonewall Diversity Champion.
Fuse Summer Internship 2020
Lithium-Sulfur Battery model implementation in Python
Project Description
The need for high capacity batteries is increasing rapidly due to our more electrified and mobilised world. Lithium-ion batteries have served use-cases such as phones and electric vehicles well, but the demand for greater performance requires the next generation of beyond-Lithium-ion batteries. One promising new battery technology is the Lithium-Sulfur battery. Such batteries have a theoretical specific energy capacity 3-5 times larger than traditional Lithium-ion batteries and are far more environmentally friendly. However, the current understanding of Li-S batteries is limited due to their highly complex internal electrochemical processes. The difficulty in understanding such batteries hinders researcher’s ability to enhance the technology enough for widespread adoption.
Physics-based mathematical models of Li-S batteries can play an important role in the development of this technology. Models allow experimental-based hypotheses to be made precise and verified. Models which succeed in explaining experimental results can then be used to guide further experiments and cell designs. However, it is currently difficult to directly compare the array of models in the literature. The project aims to add several models to a flexible new python-based platform called PyBaMM which will allow researchers to directly compare these models between each other and with experimental data. This is part of a broader effort to facilitate cross-institutional collaboration, democratise research progress, and aid rapid model development. The student will be supervised by Dr. Monica Marinescu (Imperial College) and will work closely with Dr. Michael Cornish (Imperial College).
Due to the ongoing COVID-19 situation, the entire project will be running remotely, unless existing restrictions are removed.
Who we are looking for and what you can learn
You should be comfortable with Python and Ordinary Differential Equations (ODEs). An understanding of how batteries work is desirable, but not essential. An option to extend the work to models involving Partial Differential Equations is also available.
You will be working with a leading research group to develop models in Python which can be cited by subsequent researchers. You will become more familiar with Python, ODEs, and battery technology. As part of The Faraday Institution’s 2020 intern cohort you will enter an end-of-project poster competition – the winners of which will be invited to present their poster at the Faraday Institution Conference in November 2020.
Eligibility
Be a registered full-time undergraduate student at a UK university.
Undertake the internship within the years of undergraduate study (i.e. student must not be in final year)
Funding
A salary of £9.30/hour across the UK or £10.75/hour in London will be provided. This will be determined by the working address, not the university’s location. The internship is a full-time role for 8 weeks, beginning in early July. The funding is provided by The Faraday Institution.
Deadline
June 1st 2020, please send your CV and brief cover letter to m.cornish14@imperial.ac.uk