In recent years, lithium-ion batteries have been broadly employed in many different applications that require high energy and power density. In both static and mobile applications, the onboard estimation of internal parameters such as the capacity and internal resistance is of paramount importance. In particular, the internal resistance of a battery limits the power it can deliver and affects the overall efficiency. The battery resistance changes under different conditions, such as temperature, state of charge, and aging. In addition, a hysteresis resistance phenomenon was discovered and analyzed for different temperatures in a previous work. Indeed, the internal resistance of a battery changes based on the charging or discharging phase for a fixed state of charge and temperature. In the present work, the hysteresis phenomenon was deeply analyzed, even considering the cycle aging of the battery. To do this, several tests were performed using the electrochemical impedance spectroscopy method. Finally, a mathematical model capable of predicting how the hysteresis changes as a function of aging, for a fixed state of charge and temperature, was proposed.

In recent years, lithium-ion batteries (LiBs) have gained a lot of importance due to the increasing use of renewable energy sources and electric vehicles. To ensure that batteries work properly and limit their degradation, the battery management system needs accurate battery models capable of precisely predicting their parameters. Among them, the state of charge (SOC) estimation is one of the most important, as it enables the prediction of the battery’s available energy and prevents it from operating beyond its safety limits. A common method for SOC estimation involves utilizing the relationship between the state of charge and the open circuit voltage (OCV). On the other hand, the latter changes with battery aging. In a previous work, the authors studied a simple function to model the OCV curve, which was expressed as a function of the absolute state of discharge, q, instead of SOC. They also analyzed how the parameters of such a curve changed with the cycle aging. In the present work, a similar analysis was carried out considering the calendar aging effect. Three different LiB cells were stored at three different SOC levels (low, medium, and high levels) for around 1000 days, and an analysis of the change in the OCV-q curve model parameters with the calendar aging was performed.