Online State of Health Estimation of Lithium-Ion Battery for Electric Vehicle

Nowadays, lithium-ion batteries (LiBs) are present in many applications and are increasingly becoming of interest in the electric vehicle (EV) sector. State of charge and state of health estimations are of fundamental importance to predict and quantify the remaining EV range and battery degradation level. The latter is usually related to the capacity fade or internal resistance increase. In the present work, the focus was on the capacity fade estimation to evaluate the actual battery capacity considering the battery aging. To do this, it is possible to use model-based methods or data-driven methods. Even if, the former can be used online, they can require high performance of the battery management system and high computational effort. The latter, conversely, are simpler to be implemented, but they require collecting a lot of data offline. In most cases, they need the knowledge of the whole open circuit voltage (OCV) curve resulting not suitable for EV applications. In this work, the possibility to estimate the actual battery capacity, for EV applications, starting from the knowledge of just two experimental OCV points was proposed and analyzed. Different aging tests were performed on a LiB to validate the proposed method for different aging levels.