Research Studentship in Data-Driven Battery State of Health Diagnostics and Prognostics

Research Studentship in Data-Driven Battery State of Health Diagnostics and Prognostics (BHDP_DH_2017)

4-year D.Phil studentship, available from October 2017

Research Area

The crucial issue of battery degradation impacts the economics of energy storage and the roll out of renewable energy on the grid, as well as the viability of second life applications and vehicle-to-grid services. Despite much research, a real-world validated generally applicable algorithm for battery degradation modelling does not exist because degradation is non-uniform, path-dependent and multi-faceted, involving interacting chemical, mechanical and electrical factors . This is a particularly acute challenge at pack level where the internal configuration of the system may not even be known in detail. This project, in collaboration with Siemens, will take a completely new approach to degradation diagnostics and prognostics at pack level. By combining large volumes of real world data with validated physical model simulations we aim to apply sophisticated data-driven techniques (for example Bayesian non-parametrics) to diagnose and predict faults plus battery capacity and power fade as well as end of life behaviour. The project will bridge the gap between real world pack performance and lab cell performance and build new software tools and battery models to deal with data from a variety of settings in a structured way.

The student will be based at the University of Oxford as part of the Energy and Power Group and supervised by Professor David Howey, with co-supervision from Professor Michael Osborne (Machine Learning Group).  See and

 Award Value

The studentship covers University fees at the level set for UK students plus a stipend (tax-free maintenance grant) of £14,553 p.a. for the first year, and at least this amount for a further two years. The studentship does cover the payment of college fees..


To be eligible for a full award (stipend and fees) a student must have:

  • Settled status in the UK, meaning they have no restrictions on how long they can stay


  • Been ‘ordinarily resident’ in the UK for 3 years prior to the start of the grant. This means they must have been normally residing in the UK (apart from temporary or occasional absences)


  • Not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals)

 Candidate Requirements

Candidates are expected to meet the graduate admissions criteria available at

 Prospective candidates will be judged according to how well they meet the following criteria:

  1.  A high 2.1 or first class honours degree in Engineering, Physics or a related subject,
  2. Excellent English written and spoken communication skills.

An ability to program in Matlab, and some knowledge of machine learning, is desirable but not essential. 

Application Procedure

Informal enquiries are encouraged and should be addressed to Professor David Howey ( .

Candidates must also submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website at

Please quote BHDP_DH_2017 in all correspondence and in your graduate application.

Closing date for applications: 14 July 2017