Gabriele Pozzato received his Bachelor's degree in Information Engineering from Università di Padova and his Master of Science (cum laude) in Automation and Control Engineering from Politecnico di Milano. He was a visiting scholar at the Clemson University International Center for Automotive Research (CU-ICAR), South Carolina (USA), from January to November 2016. He received his Ph.D. in Information Technology from the Politecnico di Milano in 2020, defending a thesis on the optimization, modeling, and control of vehicles' powertrain. During his doctoral studies, he was an academic guest at the ETH Zürich and the Leibniz Universität Hannover. After the doctoral degree, he was junior Project Manager at Robert Bosch S.p.A., Sensortec division. He currently holds a post-doc position at the School of Earth, Energy & Environmental Sciences, Stanford University.
Phys Sci Res Assoc, Energy Resources Engineering
Doctor of Philosophy, Politecnico Di Milano (2020)
Bachelor of Science, Universita Degli Studi Di Padova (2013)
Master of Science, Politecnico Di Milano (2015)
- Mean-Value Exergy Modeling of Internal Combustion Engines: Characterization of Feasible Operating Regions JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2022; 144 (6)
- Core-Shell Enhanced Single Particle Model for lithium iron phosphate Batteries: Model Formulation and Analysis of Numerical Solutions JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2022; 169 (6)
Lithium-ion battery aging dataset based on electric vehicle real-driving profiles.
Data in brief
2022; 41: 107995
This paper describes the experimental dataset of lithium-ion battery cells subjected to a typical electric vehicle discharge profile and periodically characterized through diagnostic tests. Data were collected at the Stanford Energy Control Laboratory, at Stanford University. The INR21700-M50T battery cells with graphite/silicon anode and Nickel-Manganese-Cobalt cathode were tested over a period of 23 months according to the Urban Dynamometer Driving Schedule (UDDS) discharge driving profile and the Constant Current (CC)-Constant Voltage (CV) charging protocol designed at different charging rates - ranging from C/4 to 3C. Ten (10) cells are tested in a temperature-controlled environment (23 ∘ C). A periodic assessment of battery degradation during life testing is accomplished via Reference Performance Tests (RPTs) comprising of capacity, Hybrid Pulse Power Characterization (HPPC), and Electrochemical Impedance Spectroscopy (EIS) tests. The dataset allows for the characterization of battery aging under real-driving scenarios, enabling the development of models and management strategies in electric vehicle applications.
View details for DOI 10.1016/j.dib.2022.107995
View details for PubMedID 35252504
- Exergy-based modeling framework for hybrid and electric ground vehicles APPLIED ENERGY 2021; 300
- Modeling degradation of Lithium-ion batteries for second-life applications: preliminary results IEEE. 2021: 826-831