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A Digital Twin for EV Battery Monitoring

Parole chiave BATTERIA, CLOUD COMPUTING, DEEP LEARNING, DIGITAL TWIN, EDGE COMPUTING, NEURAL NETWORKS, NEURAL NETWORKS, EMBEDDED SYSTEMS, C++ PROGRAMMING, VEICOLI ELETTRICI ED IBRIDI

Riferimenti MASSIMO PONCINO, SARA VINCO

Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA

Tipo tesi RICERCA, RICERCA CON AZIENDA

Descrizione The current growth in the Electric Vehicle (EV) market, with 10 million EV sold worldwide in 2022. Sales are expected to grow by another 35% in 2023, making it clear that advancement in battery technology can not be underestimated, as battery-based propulsion still faces many challenges: low energy and power densities compared to liquid fuels, long charging times, and quick degradation over time.
To address these challenges, Battery Management System (BMS) are commonly employed in EV to ensure safe battery operation and optimize efficiency. Furthermore, digital technologies, such as the battery Digital Twin (DT), have become increasingly utilized to support various stages of the battery’s lifespan and enhance BMS functionality. A battery BMS is a digital representation that emulates battery dynamics, enabling smart management, what-if analysis, predictive maintenance, and more.
Many open activities fall in this scenario, and may become the focus of a thesis:
• Battery modeling with Deep Learning and Neural Network techniques, including Physically Informed techniques
• Exploration of the deployment of battery models on edge devices
• Design of edge-cloud architecture to allow the execution of battery models on-board and around the EV

Vedi anche  battery dt thesis proposal.pdf 

Conoscenze richieste Programmazione

Note Possibile collaborazione con azienda innovativa piemontese


Scadenza validita proposta 05/12/2024      PROPONI LA TUA CANDIDATURA