Precise online lithium-ion battery state of health estimation is critical for the correct operation and management of battery-based energy storage systems such as microgrids and electric vehicles. However, in such applications it is necessary to maintain standard operation and therefore difficult to experimentally determine. Advancements in machine learning techniques and capabilities allow for precise and efficient data-driven predictions. In this paper we propose a simple, yet effective state of health estimation model based on the extraction of features observed from patterns in the voltage, current and temperature profiles during the charging process, which then through artificial neural networks allow for per cycle estimations. We then apply this model to two groups of batteries from the NASA Ames PCoE Battery data set. Results show that the proposed model is capable of estimating the state of health of batteries discharged under varied conditions with resulting coefficients of determination between 0.896 and 0.992 while also employing significantly less input data than other works.
