Analysis of state of charge estimation methods for smart grid with VRLA batteries

Recent developments in fields such as portable devices, electric and hybrid vehicles and renewable energy harvesting are the main reasons why the energy storages/batteries are an object of intense research. Lead-acid batteries still play a key role, and it is old known technology. Other battery chemistries having higher power are still too much expensive for small and medium energy storages for smart grid. To achieve an effective exploitation of battery packs, it is important to have a robust and reliable battery management. Accurate state of charge estimation in battery management is a critical part. To achieve the economical and suitable energy performance including life-time extension battery nonlinearities must be considered. Analysis and comparison of widely used SOC estimation methods including Coulomb counting and Kalman filter with various battery models is the goal of the paper. Internal parameters dependencies on SOC were added to battery models used by Kalman filter to achieve high accuracy. SOC methods are verified by using two different discharge/charge tests.