
Ph.D. in Energetica , 35th cycle (2019-2022)
Ph.D. obtained in 2023
Dissertation:
Development of Innovative Methodologies to Support the Design of Connected and Electrified Vehicles (Abstract)
Tutors:
Luciano Rolando Federico MilloProfile
Research topic
Exploiting V2X connections and advanced energy management strategies to achieve maximum CO2 reductio
Research interests
Biography
His Ph.D. is focused on hybrid electric vehicles to assess the CO2 emissions reduction that can be achieved by synergically exploiting V2X connections and advanced energy management strategies.
From November 1st, 2019 to August 31st, 2022, he was a visiting student researcher at Stanford University invited by Prof. Onori. His research activity, sponsored by Volkswagen Group of America (VWoA), was focused on the health estimation of battery packs combining electrochemical modelling and data-inspired algorithms.
Awards and Honors
- First Prize for the Student Awards at the FISITA World Conference 2021, with the paper entitled: “Energy Management System Optimization Based on V2X Connectivity”. The Student Awards are awarded for the best 3 papers written and presented at Congress by an author under the age of 35. (2021)
- U.S. Patent Application: Battery management system for determining a health of a power source based on driving events. For confidentiality reasons, only the citation has been uploaded. (2023)
- U.S. Patent Application: Battery management system for determining a health of a power source based on an impedance indicator. For confidentiality reasons, only the citation has been uploaded. (2023)
- U.S. Patent Application: Battery management system for determining a health of a power source based on charging events. For confidentiality reasons, only the citation has been uploaded. (2023)
Teaching
Teachings
Master of Science
- Propulsori termici. A.A. 2022/23, INGEGNERIA MECCANICA. Collaboratore del corso
Publications
Works published during the Ph.D. View all publications in Porto@Iris
- Pulvirenti, L.; Rolando, L.; Millo, F. (2023)
Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction. In: TRANSPORTATION ENGINEERING, vol. 11. ISSN 2666-691X
Contributo su Rivista - Pulvirenti, Luca (2023)
Development of Innovative Methodologies to Support the Design of Connected and Electrified Vehicles. relatore: ROLANDO, LUCIANO; MILLO, Federico; , 35. XXXV Ciclo, P.: 153
Doctoral Thesis - Pulvirenti, Luca; Tresca, Luigi; Rolando, Luciano; Millo, Federico (2023)
Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario. In: ENERGIES, vol. 16, pp. 4121-4139. ISSN 1996-1073
Contributo su Rivista - Millo, Federico; Rolando, Luciano; Tresca, Luigi; Pulvirenti, Luca (2023)
Development of a neural network-based energy management system for a plug-in hybrid electric vehicle. In: TRANSPORTATION ENGINEERING, vol. 11. ISSN 2666-691X
Contributo su Rivista - Millo, F; Rolando, L; Pulvirenti, L; Di Pierro, G (2022)
A Methodology for the Reverse Engineering of the Energy Management Strategy of a Plug -In Hybrid Electric Vehicle for Virtual Test Rig Development. In: SAE INTERNATIONAL JOURNAL OF ELECTRIFIED VEHICLES, vol. 11, pp. 113-132. ISSN 2691-3747
Contributo su Rivista