In this webinar, presented by Gamma Technologies’ own Massimiliano Mastrogiorgio (Senior Application Engineer, Electrical Systems • Battery Systems), we discuss machine learning techniques to develop efficient and comprehensive battery models, including thermal aspects. We will showcase how machine learning algorithms are employed to capture complex battery behaviors quickly and integrate them into vehicle system models. Yet undoubtedly fast and accurate battery models are important for various applications such as electric vehicles, renewable energy storage, and portable electronics. Additionally, it may 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.
Key Aspects are:
- Accuracy: Highlighting how machine learning techniques can enhance the accuracy of battery models by capturing complex behaviors and dynamics
- Flexibility: Discussing the flexibility offered by machine learning approaches, which can adapt to different battery chemistries, operating conditions, and applications
- Scalability: Addressing the scalability of machine learning-based battery models, enabling them to handle large datasets and complex systems efficiently
- Real-time Capability: Emphasizing the ability of machine learning models to provide real-time predictions and insights, enabling rapid decision-making and control in dynamic operating environments
In a nutshell: Machine learning enables fast, accurate, and adaptable battery modeling solutions that are well-suited for real-world applications across various industries.