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Robust Real-Time SOH Estimation via Online Identification of Temperature and SOC Dependent Battery Resistance Model

Real-time and accurate health estimation of lithium-ion batteries is necessary to ensure the safe and continuous operation of critical systems such as microgrid energy storage systems and modern electric and hybrid vehicles. Estimation of a battery's state-of-health (SOH) requires online identification of battery model parameters such as internal resistance or battery capacity. Although many papers discuss either the estimation of these parameters in real-time or the identification of these parameter changes at different operating conditions, a unified framework for SOH estimation considering battery states and environmental conditions has not been widely accepted in the field. This paper focuses on the development of a novel methodology for online SOH estimation for lithium-ion batteries utilizing a newly proposed nonlinear model for electric circuit model (ECM) resistances with dependencies on temperature and state-of-charge (SOC).

Strategic Leader Selection and Cluster Formation in Hierarchical Networked Microgrids