Empirical and physics-based approaches to estimate states of lithium-ion battery

Lithium-ion batteries are integral parts of our life due to the rapid increase of applications which require batteries for their exploitation. Thus, there is a market demand to produce lithium-ion batteries for a huge number of applications from electric vehicles to energy storages. Battery Management System (BMS) is developed to maintain safe battery exploitation conditions. Most BMSs are embedded systems that have physical memory limits. Therefore, battery model should be easy to simulate to be integrated into BMS for states estimation. In the present paper we intend to compare empirical and physics-based approaches to estimate lithium-ion battery states with respect to their possibility of implementation in the embedded system. We will use Kalman filter to estimate battery states by means of the mentioned models.