Battery Management Using Estimated State of Health, State Of Power, And State Of Safety

Prof. Diego Iannuzzi, University of Naples Federico II, Italy, diego.iannuzzi@unina.it.
Dr. Simone Barcellona, Politecnico of Milano, Italy, simone.barcellona@polimi.it

Expanding manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs, Railway Vehicles, Ships, and Aircrafts). However, battery diagnostics and prognostics related to capacity degradation or resistance increase (referred as state of health, SOH), ability of charging/discharging (referred as state of power, SOP), and safety issues (referred as state of safety, SOS) in real-world applications are still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for “real EVs” is very challenging. For instance, the state of health prediction is critical in battery management systems to ensure the reliability and safety of battery operation for further cycle running. On the other hand, electric vehicle applications require an accurate SOH estimation at a low computational burden. Therefore, developing simple yet accurate cell or pack battery models and the estimation of their parameters becomes a very important challenge for battery management systems, state of health prediction, diagnosis and prognosis to improve the battery efficiency and increase the battery lifecycle. System identification approaches have been successful in precise battery health prognostic. Most of the recent studies introduces also hybrid structures for accurate state estimation. The scope of this special session is to give the opportunity to the presenters to share and propose their own contributions on battery modelling, battery parameter estimation methods, state of health, state of power, and state of safety prediction techniques, and improved battery management strategies for EVs, Railway Vehicles, Ships, and Aircrafts. The conference covers the following topics:

• Battery models (e.g. integrated electrical, thermal and aging)
• Parameters estimation methods – State of health estimation and prediction techniques
• State of power estimation and prediction techniques – State of safety estimation and prediction techniques
• Battery management strategies for EVs, Railway Vehicles, Ships, and Aircrafts
• Battery diagnostic and prognostic