Projects

 

Manifesto: Pioneering the Future of Li-ion Battery Ageing Modeling for High-Performance Applications

Welcome to a visionary research project that embarks on a transformative journey to model high-performance Li-ion battery cell ageing and Battery Management Systems (BMS) synthesis. In an era where energy storage technologies are paramount to sustainable transportation and power systems, our manifesto sets forth the ambitious goal of synthesizing innovative hybrid modeling techniques that fuse physical knowledge of electrochemical phenomena with the power of machine learning.

Li-ion cells, the heart of modern energy storage solutions, are subject to ageing due to inherent chemical reactions between the negative electrode and electrolyte. Even during periods of inactivity, the phenomenon of 'calendar ageing' subtly impacts the battery's performance. Understanding the intricate relationship between cell design, cell usage, and the ageing process is key to optimizing battery performance, extending lifespan, and enhancing overall safety.

Our research project is at the forefront of scientific innovation, striving to develop data-driven methodologies grounded in Explainable AI paradigms. By blending the insights from electrochemical physics with advanced machine learning algorithms, we aim to create a holistic and interpretable model of Li-ion battery ageing. The Explainable AI approach will not only predict ageing patterns but also shed light on the underlying mechanisms, empowering engineers and researchers with valuable insights to enhance battery design and management.

As collaborators with FERRARI S.p.A., we are committed to tackling real-world challenges in high-performance applications. Li-ion batteries play a pivotal role in electric vehicles, where maintaining optimum battery health is essential for driving range and acceleration. Additionally, in renewable energy systems, high-performance batteries ensure efficient energy storage and grid stability, contributing to a greener and sustainable future.

Through interdisciplinary collaboration and cutting-edge research, we unite experts in electrochemistry, machine learning, and battery technology. By fostering a culture of knowledge exchange and innovation, we pave the way for groundbreaking advancements that transcend traditional battery ageing models.

Our vision extends beyond the confines of research laboratories; we aspire to see our findings implemented in the design and management of Li-ion battery systems, revolutionizing energy storage solutions and propelling the electric vehicle industry forward.

Together, let us pioneer the future of Li-ion battery ageing modeling, where innovation and collaboration meet to shape a world powered by high-performance energy storage technologies. Embrace this transformative journey, and join us in driving progress towards a sustainable, electrified future in partnership with FERRARI S.p.A. The road ahead is illuminated with possibilities, and the destination is a future of cleaner, smarter, and more efficient energy systems.


The application and engineering component 

In the realm of engineering, the automotive industry stands at the forefront of technological advancements, with Electric Vehicles (EVs) becoming a focal point of innovation. The rise of EVs since the 1980s has captured widespread interest, driven by their eco-friendly and efficient attributes compared to traditional vehicles. However, engineering challenges arise, particularly concerning the complex electrochemical system of EVs' energy storage devices, namely, Li-ion batteries.

For the automotive sector to fully embrace Li-ion batteries, it is imperative to address the critical issues of battery ageing, prevention, and control. As these batteries power the heart of EVs, engineering solutions that ensure optimal battery performance and extended lifespan become paramount.

The research undertakes also the ambitious task of providing a comprehensive and updated research materials of battery ageing mechanisms affecting both electrodes. In this pursuit, the research takes a data-driven approach, emphasizing the use, for examples, of differential curves as a fruitful and lightweight diagnostic tool. Engineers find great value in these curves, as they offer powerful instruments for data analysis.

By leveraging cutting-edge AI and automatic procedures, specifically tailored to Battery Management Systems (BMS), engineering researchers can delve into the intricate intricacies of battery ageing mechanisms. This enables a deep understanding of the underlying processes, empowering engineers to design effective strategies for optimal battery usage and efficient maintenance.

Engineers can develop advanced algorithms that continuously monitor battery health in real-time, allowing for predictive maintenance and proactive fault detection. By integrating these engineering innovations, the industry can drive EV adoption to new heights, overcoming challenges and paving the way for a greener and more sustainable automotive landscape.

With engineering expertise at the helm, this research propels the automotive industry towards a future where EVs reign as the epitome of clean and efficient transportation. Engineers hold the key to unlocking the full potential of Li-ion batteries, enabling a paradigm shift in mobility and setting the course for a greener, smarter, and electrified future. As engineering principles and AI-driven diagnostics converge, the automotive landscape is poised to embrace a new era of reliable, efficient, and eco-friendly vehicles, shaping a brighter future for generations to come.