Real-time model-based estimation of SOC and SOH for energy storage systems

Abstract

Accurate modeling of electrochemical batteries is of major concern in designing the control system of Energy Storage Systems (ESS). In particular, a precise estimation of State of Charge (SOC) and State of Health (SOH) parameters strongly affects the full exploitation of battery energy potential in real applications. In this paper a novel real-time estimation method is presented representing a good tradeoff between model accuracy and algorithm complexity. In the proposed approach, SOC and SOH values are determined by a suitable algorithm that continuously performs a comparison between the ESS voltage value, calculated by an adaptive run-time circuital model, and its real value measured at the ESS terminals. The result of such comparison is used to suitably tune two parameters of the ESS circuital model, the no-load voltage and resistive voltage drop, in order to compensate the inaccuracy of the model response due to parameter variations. Initially, to set the parameter of ESS electrical model, the proposed approach requires to carry out short preliminary tests that can be easily implemented in a low cost control units. Experimental results and comparisons with other estimation methods highlight the consistency of the proposed algorithm.