Upcoming Webinars: Power

Machine Learning for Multi-Physics Automotive Simulation


Undoubtedly, fast and accurate battery models are crucial for various applications such as electric vehicles, renewable energy storage, and portable electronics. Machine learning techniques offer the ability to develop efficient and comprehensive models for automotive simulation including batteries and their thermal aspects.

This 45-minute webinar will explore how to use machine learning to develop these more efficient models as well as showcase how machine learning algorithms are employed to quickly capture complex behaviors and integrate them into vehicle system models. Additionally, the program will highlight the potential benefits of integrating machine learning into battery modeling processes such as improved prediction accuracy, reduced computational time, and enhanced system optimization capabilities.

Topics include:

  • How machine learning techniques are used to develop efficient, optimized vehicles
  • How machine learning algorithms can capture complex battery behaviors quickly and integrate them into vehicle system models
  • How integrating machine learning into engineering processes can lead to improved prediction accuracy, reduced computational time, and enhanced system optimization capabilities


Massimiliano Mastrogiorgio, Senior Battery Application Engineer, Gamma Technologies

Massimiliano Mastrogiorgio serves as Gamma Technologies’ Senior Battery Application Engineer. He holds a master’s degree in energy and nuclear engineering from Politecnico di Torino, with specialization in energy production and management, as well as dual master’s degrees in industrial engineering and mechanical engineering from the University of Illinois at Chicago (UIC), with a concentration in electrochemical storage systems. During his academic journey, Massimiliano conducted research in the Nano Engineering Laboratory at UIC, where he contributed to the synthesis of electrolytes tailored for 3D printing applications. Subsequently, he dedicated his efforts to the development of physics-based aging models for batteries, with a focus on their practical implementation in real-world scenarios. Massimiliano’s career underscores his unwavering commitment to advancing sustainable energy solutions for a more environmentally conscious future.


Amanda Hosey, Editor, SAE Media Group

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