Titolo della tesi: Monitoring the State of Health (SoH) of green batteries
This doctoral research, conducted within the framework of the GreenBat project, aims to develop advanced methodologies for monitoring and predicting the health status of lithium-ion batteries, particularly those utilizing environmentally friendly formulations. As the demand for efficient and sustainable energy storage systems grows alongside the global shift towards renewable energy, improving the reliability, performance, and lifespan of lithium-ion batteries has become a critical objective. This research contributes to this goal by integrating advanced mathematical models with experimental electrochemical techniques, offering a more precise and holistic understanding of battery behavior over time.
A central aspect of this study is the coupling of predictive and exploratory chemometric models with electrochemical analysis methods. The mathematical models developed in this research are designed to predict the state of health (SOH) of batteries based on various input parameters, using statistical and machine learning techniques derived from chemometrics. These models help uncover complex relationships between different performance indicators and the underlying physical and chemical processes that drive battery degradation and efficiency. The chemometric approach allows for the analysis of large datasets, revealing patterns, correlations, and trends that might not be easily detected through traditional experimental methods.
On the experimental side, the research employs electrochemical techniques, such as galvanostatic cycling, to assess battery performance under different operational conditions. This method provides critical information on capacity, internal resistance, and overall health, helping to understand how batteries degrade over time and under varying charge-discharge cycles. In addition, hyperspectral imaging using Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopy is employed to analyze the material composition and internal dynamics of the batteries. These non-destructive imaging techniques offer detailed insights into chemical bonds, material distribution, and structural changes during operation, enabling the early detection of degradation or failure that may not be visible through electrochemical testing alone.
By combining these advanced techniques, the research aims to enhance the accuracy of battery health predictions and improve early detection of potential failures. The ultimate goal is to create a comprehensive framework for real-time monitoring and long-term prediction of battery health, with a particular focus on green lithium-ion batteries. This integrated approach represents a novel advancement in battery health monitoring, providing deeper insights into the factors that affect battery performance and contributing to the design of more durable and sustainable energy storage solutions.
The findings of this research are expected to have wide-ranging applications, from consumer electronics to electric vehicles and large-scale energy storage systems, where the demand for reliable, long-lasting, and environmentally responsible batteries is ever-growing. Ultimately, this project contributes to the development of next-generation batteries, supporting global efforts towards a more sustainable energy future.