Advanced State Estimation Techniques for Battery Packs in Electric Vehicles and Energy Storage Systems

About the Project

Accurate estimation of battery pack states, such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP), is essential for the safe and efficient operation of electric vehicles (EVs) and energy storage systems. Reliable state estimation ensures optimal performance, extends battery life, and enhances safety by preventing overcharging and deep discharging. However, the complexity of battery packs—composed of multiple cells with varying characteristics over time—makes accurate estimation challenging. This PhD project aims to develop and validate advanced techniques for real-time state estimation in battery packs, leveraging data-driven approaches, modelling, and machine learning.

This project is open-ended making it suitable for MSc by Research and PhD level

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in electrical engineering, control engineering, computer science, Mechanical, or a related subject. Previous modelling experience in Python, MATLAB/Simulink, or relevant software packages is essential.

How to Apply:

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.