In recent years, the number of batteries used for smart grids and electric vehicles has been steadily increasing. To properly maintain these systems over time, it is essential to monitor the battery’s state of health (SoH), determine when it is no longer useful for the current application, and potentially repurpose it in another context, a concept known as second-life battery. However, standard techniques in the literature provide an accurate estimation of the SoH primarily through offline testing or with prior knowledge of model parameters.

This paper proposes a new algorithm, the State of Health Estimator (SHE), which deduces the battery model online, i.e., during its operational life, and uses this characterization to provide a reliable and accurate estimate of both the battery’s actual capacity and internal resistance, considering both the ohmic and polarization components. The experimental campaign, conducted on real data, shows satisfactory performance, with an average error of 1.2% and 4% in estimating the battery’s maximum capacity and internal resistance, respectively.