We design systems and develop diagnostics and control algorithms for electrochemical energy devices such as batteries and supercapacitors, in applications from electric cars to grid power systems.

Find out more

The group is led by Professor David Howey at the Department of Engineering Science in the University of Oxford.

Our aim is to improve performance and cost by predicting dynamics and lifetime, estimating temperatures and faults, and measuring how and why devices perform in the real world. This requires us to address fundamental issues in modelling, instrumentation and data processing.


Autumn update 2022

A warm welcome to new group members Emmanuelle Hagopian and Joe Ross who arrived in October and are working on voltage hysteresis and power prediction, respectively. We were sad to say goodbye to Antti Aitio recently who has moved to take up a position in industry. You can read about his awesome PhD work on battery life diagnostics and machine learning here.

Conference season

It’s been a busy few months as the world opens up to travelling again. Professor Howey gave keynote presentations at ModVal in Germany, the ISE Topical Meeting in Sweden, and the Benelux Meeting on Systems and Control; Nicola Courtier and Ross Drummond spent time working with Luis Cuoto at ULB in Belgium on battery fast charging, and Gosia Wojtala gave a talk at the 241st ECS Meeting on how battery ageing impacts entropy measurements.

Congrats to Sam Greenbank on passing his viva!

Huge congratulations to Sam Greenbank who today successfully defended his doctoral work on battery aging using machine learning to predict lifetime. Read more about this topic in Sam’s excellent IEEE paper.

New paper alert

We start the year excited to share the publication of recent work on battery health diagnostics using machine learning. Our long-term collaboration with BBOXX has resulted in a new Joule paper “Predicting battery end of life from solar off-grid system field data using machine learning” where we crunched 620 million rows of field data to show that battery end of life can be diagnosed directly from the data without additional sensors or the need to take systems offline. Get the paper here and open access preprint here.

Autumn update 2021

A warm welcome to new group members Becky Perriment, Zihao Zhou and Masaki Adachi who arrived in October and will respectively be working on solar-battery off-grid systems, lifetime modelling, and machine learning. We were sad to say goodbye to Dr Jie Lin in September who has moved to take up a new position at UCL. You can read about Jie’s exciting research on coupled thermal-electrochemical modelling here.