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SS MLSP4EV 2020 : Special Session on Machine-Learning and Signal Processing Techniques for Electric Vehicles Interaction and Management


When Nov 18, 2020 - Nov 20, 2020
Where Turin, Italy
Submission Deadline Jul 31, 2020
Notification Due Sep 15, 2020
Final Version Due Sep 30, 2020
Categories    machine-learning   electric vehicle   smart grid   signal processing

Call For Papers

In the last decade, Machine-Learning and Signal Processing algorithms had a profound impact on several research fields and industrial applications. Such methods have been playing a significant role also in the electric vehicle (EV) sector, where they have been used, for example, for battery state estimation and management, smart charging, and fault diagnosis. Along with a positive impact on the environment and the society, the high penetration of EVs expected in the next years will also pose new challenges for our cities and the existing infrastructures. In this scenario, Machine-Learning and Signal Processing algorithms will play a fundamental role. Advanced algorithms are required for managing the EV internal operation and services, such as energy management, battery and driver state monitoring, but also for their integration within our cities (e.g., in pedestrian warning systems), and the interaction with the power grid (e.g., in Vehicle-to-Grid interaction, and in coordinated vehicle charging).

The aim of this session is thus to promote a forum where scientists and practitioners in the industry can discuss the most recent advancements in the application of Machine-Learning and Signal Processing techniques to the management of EVs and their interaction with the existing infrastructures.

- Machine-Learning for Smart EV Charging
- Vehicle Energy Management with Machine-Intelligence
- Non-Intrusive Monitoring of EVs Loads
- Deep Learning for Coordinated EV Charging
- Machine-Learning for Vehicle-to-Grid Integration and Battery Degradation
- Learning Algorithms for Vehicle-to-Grid (V2G), Vehicle-to-Vehicle, Vehicle-to-Home (V2H) interaction.
- Pedestrian Warning Systems with Signal Processing and Machine-Learning
- Vehicle Fault Detection and Diagnostics
- Machine-Learning for Battery Management and State Estimation
- Driver State Monitoring with Deep Learning
- Machine-Learning and Signal Processing in Vehicular Networks
- Machine Learning-based processing of measurement data within the EV

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