The rapidly growing number of electrified vehicles requires development of the next generation of automotive battery cells. Due to properties such as high energy density, Lithium-ion (Li-ion) batteries are typically used for these automotive applications. In order to guarantee the safe, efficient and reliable operation of Lithium-ion batteries, the BMS (Battery Management System) is of vital importance in the automotive industry. The main objective for this project is to satisfy the aforementioned operating conditions by measuring and determining relevant battery cell parameters and states. Current battery state estimation algorithms have limited performance due to the fact that they do not take into account all battery processes when modelling the non-linear electrochemical behaviour of the battery. For example, SoC-estimation algorithms based on a Kalman-filter-like approach, do not take into account the change of model parameters due to the ageing process of the battery, temperature differences or SoC differences.
In this project, model-based battery management is proposed for estimating battery states and parameters. Two main research directions have been identified:
•Development of extensive (first principles) battery models, incorporating all relevant electrochemical battery processes.
•Given a certain application (e.g. SoC estimation), derivation of reduced-order battery models and development of model-based state estimation algorithms.
The topics in the project include State-of-Charge (SoC) and State-of-Health estimation (SoH), impedance-based temperature estimation, and charge control.