An online state of health estimation method for lithium-ion batteries based on time partitioning and data-driven model identification

In recent years, the request for batteries to employ in emerging technologies like smart grids or electric vehicles shows constant growth. To maintain these systems over time, it is crucial to have a mechanism to monitor the battery State of Health (SoH), determine when it is not of use for the current application, and eventually reuse it in another context a.k.a. battery second life. However, standard techniques from the literature provide an accurate estimation of the State of Health mainly by performing offline tests or with a priori knowledge of hyperparameters. This paper proposes a novel algorithm, namely State of Health Estimator (SHE), that infers the battery model online, i.e., during its operational life, and uses this characterization to provide a reliable and accurate estimation of both actual battery capacity and internal resistance, considering both ohmic and polarization components. The experimental campaign, performed on real-world data, shows satisfactory performance, with an average error of 1.2 % and of 4 % in the estimate of the maximum battery capacity and internal resistance, respectively.