Performance evaluation of algorithms for the State of Charge estimation of storage devices in microgrid operation

Description

This paper analyzes different Kalman filtering algorithms for the real-time State of Charge (SoC) estimation of Battery Energy Storage System (BESS). Accurate SoC estimation is a key issue for microgrid real-time operation involving optimal model-based control. A BESS composed of Li-ion battery equipped with a Battery Management System (BMS) is characterized by fitting the parameters of a dynamic model, validated through experimental tests. Particular attention is devoted to the identification and representation of model nonlinearities in order to design robust Kalman filtering SoC estimation methods. Performance evaluation of the proposed algorithms are carried out by statistical simulations and experimental real-time tests. The analysis also takes in consideration the computational performances of the different methods in order to match the requirements of real-time control routines.

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