Abstract:
This thesis revolves around satte of charge estimation in solar energy storage batteries. We present our findings after adapting multiple state of charge estimation models to our problem, analyzing the results for each and comparing them to deduce the better model. We do this for two different battery technologies which are used in photovoltaic energy production and storage: the lithium-ion and lead -acid batteries. We develop an equivalent circuit for our lithium-ion battery to which we add a state filter. We develop and apply different machine learning models estimate state of charge. We validate our findings with two standalone cycles
