Modelling
Research focusses on developing cell-level and module-level (electrical/thermal) mathematical models and on developing better understanding of battery degradation mechanisms. These models include empirical (data-driven) models, as well as first-principles (physics-based) models, and focusses on current Lithium-ion chemistries, as well as advanced all-solid-state and Sodium-based chemistries. To have representative models, also experimental procedures need to be developed to enable reliable estimation of the model parameters.
State Indication
Reliably estimating the internal states of the battery allows monitoring available energy, the remaining capacity, and the available power. This leads to better utilization of the battery (e.g., discharging the battery further because the available energy is known more precisely). This requires algorithms that have high reliability, low computational footprint and are easy to calibrate. In some cases, such internal states can be estimated/inferred using online electrochemical impedance spectroscopy.
Control and Prognostics
Models and estimates of current internal battery state allows for development of advanced control and prognostics. For instance, a closed-loop charging strategy can de designed that optimizes the trade-off between charging time and ageing, or balancing can be done while charging (instead of alternating between charging and balancing). Furthermore, the group works methods that allow to predict and optimize the remaining runtime and useful life by optimizing the safe operating window of the battery.