State of Health Prediction of Lithium-ion Batteries

• Published in: 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) pp. 12 - 17
• Main Authors: Barcellona, S., Cristaldi, L., Faifer, M., Petkovski, E., Piegari, L., Toscani, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.06.2021
• Subjects: Aging; Data models; Lithium-ion batteries; Predictive models; Temperature; Temperature dependence; Training data
• DOI: 10.1109/MetroInd4.0IoT51437.2021.9488542

The presence of lithium-ion batteries has been steadily growing in stationary and mobile applications and their development continues to play a key role in the wide spread adoption of electric vehicles. They are characterized by high energy density and long life; however, they are not impervious to aging effects. It is necessary to accurately predict this process in order to make sound technical and commercial decisions. Unfortunately, battery aging is a complex mechanism depending on several factors such as temperature, state of charge, voltage levels and current rates. Aging effect has resulted in many different model-based and data-driven methods attempting to predict the aging process under certain working conditions. In this paper, two functions are considered to model the battery aging behavior. Their coefficients are calculated following the leastsquares method, using data collected under controlled conditions. Additionally, it is shown that one of the two functions allows one to forecast the aging behavior. Finally, the prediction capability of the aging trend of two other batteries being discharged at different currents is analyzed.