Machine Learning-1

  • Create efficient battery models using machine learning, integrating thermal aspects.

  • Using ML capture complex battery behaviors and integrate them into vehicle system models

  • Boost accuracy, cut time, and optimize systems by integrating machine learning into battery modeling processes

Abstract

In this webinar, we will discuss the use of machine learning techniques to develop efficient and comprehensive models for batteries, including thermal aspects. We will showcase how machine learning algorithms are employed to quickly capture complex battery behaviors and integrate them into vehicle system models. Undoubtedly, fast and accurate battery models are crucial for various applications such as electric vehicles, renewable energy storage, and portable electronics. Additionally, we 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: 

• Machine learning techniques are used to develop efficient battery models, including thermal aspects
• Machine learning algorithms can capture complex battery behaviors quickly and integrate them into vehicle system models
• Fast and accurate battery models are crucial for electric vehicles, renewable energy storage, and portable electronics applications
• Integrating machine learning into battery modeling processes can lead to improved prediction accuracy, reduced computational time, and enhanced system optimization capabilities

 
Massimiliano Mastrogiorgio 2-1
Massimiliano Mastrogiorgio | Host

Senior Application Engineer, Electrical Systems | Battery Systems at Gamma Technologies