Johannes Rohwer

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.