• Johannes Rohwer

  • Theme:Propulsion Electrification
  • Project:Lithium-ion battery state of health estimation
  • Supervisor: Chris Vagg ,Frank Marken
  • Industry Partner: AVL
  • The Gorgon's Head - Bath University Logo


Johannes recently completed a pre-doctoral programme at Argonne National Laboratory in the USA. During this time, he was involved in experimental research on advanced, high-efficiency internal combustion engines, with specific focus on pre-chamber systems and co-optimization of fuels and engines. His other industry experience includes working for an industrial manufacturer of heat exchangers in Germany as well as a robotics start-up in London. Johannes obtained his BEng and MEng at the University of Stellenbosch, South Africa. His research interests at the AAPS are in the field of digital systems, control systems, hybrid vehicles, EVs and battery SOH.


  • I played the trumpet in an 'Oktoberfest band' during my two and a half years stay in Germany.
  • I was the treasurer of a beach sailing club during my MEng.
  • I have lived in three different continents so far and hope to travel to a couple more during my PhD.
  • When I was a child, I would make my siblings waffles at least once a month.

Lithium-ion battery state of health estimation

Electric vehicles (EVs) play a key role in decreasing the carbon footprint of the mobility sector. Their high upfront cost, limited range and slow charging speed are however a barrier to increased EV uptake. Reducing the cost and improving the EV Lithium-ion (Li-ion) battery could reduce these barriers. There is however limited knowledge in the safe operation and degradation rate of Li-ion batteries. This is largely due to the complex electrochemical mechanisms not being well understood. Furthermore, the large operating envelope (temperature, charging speed etc.) over its lifetime require resource intensive testing to parameterize semi-imperical models. The battery is therefore operated very conservatively, resulting in oversizing the battery and sub-optimal operating conditions resulting in inefficiencies and higher costs. Johannes' PhD aims to provide optimal testing strategies and accurate modelling of Li-ion batteries in order to provide information to facilitate more efficient operating strategies (e.g. fast charging). This will be achieved by a combination of advanced design of experiments (DOE), modelling and machine learning. The initial part of the PhD will focus on building a model structure which is based on a semi-physical neural network. The accuracy of this model will then be assessed using existing battery data in literature and data provided by the industrial partner. An experimental test campaign will then be designed and implemented, in an attempt to efficiently parameterize the battery models. The resultant battery models would then provide important information to improve the safe operation range of the battery.

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