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Adaptive Energy Management System with a BESS through Experimental-Based Cell Modeling and Adaptive Model Predictive Control

Controlling the estimated state of charge (SOC) for battery energy storage systems (BESSs) is the main challenge behind increasing lithium-ion battery use in home energy management systems (HEMSs). This paper proposes a BESS EMS control strategy considering a dynamic battery model with second-order RC and a nonlinear adaptive unscented Kalman filter (AUKF) state estimation to control HEMSs. The battery model with second-order RC uses open-circuit-voltage over SOC and parameter identification so that the EMS meets a nonlinear model state estimation for model predictive control (MPC). The AUKF algorithm is used to improve reliable and accurate data-based SOC estimation. Adaptive MPC is utilized to achieve optimal power and voltage allocation control of home appliance energy usage via BESSs. The effective2ness of the proposed control strategy is verified through simulation to achieve optimal control of a BESS within a given SOC and to meet data of home power usage patterns.