Thesis title: Monitoring the State of Health (SoH) of green batteries
This doctoral research, conducted as part of the GreenBat project, aims to develop advanced methodologies for monitoring and predicting the health status of lithium-ion batteries, particularly those based on environmentally friendly formulations. As the global transition to renewable energy accelerates, the demand for efficient and sustainable energy storage systems is rapidly increasing. Enhancing the reliability, performance, and lifespan of lithium-ion batteries has therefore become a critical objective. This research contributes to that goal by integrating advanced mathematical modeling with experimental electrochemical techniques, offering a more accurate and comprehensive understanding of battery behavior over time.
A key focus of this study is the integration of exploratory and predictive chemometric models with electrochemical analysis methods. The mathematical models developed here are designed to estimate the state of health (SOH) of batteries based on various input parameters, using statistical and machine learning techniques rooted in chemometrics. These models reveal complex relationships between performance indicators and the underlying physical and chemical processes that govern battery degradation and efficiency. The chemometric approach enables the analysis of large datasets, uncovering patterns, correlations, and trends that may not be readily identified through traditional experimental methods.
On the experimental front, the research utilizes electrochemical techniques—such as galvanostatic cycling—to evaluate battery performance under different operating conditions. This approach provides essential data on capacity, internal resistance, and overall health, thereby shedding light on how batteries degrade over time and across varying charge-discharge cycles. Additionally, Near-Infrared (NIR) spectroscopy and hyperspectral imaging in the NIR and the Mid-infrared (MIR) ranges is employed to analyze material composition and internal battery dynamics. These non-destructive imaging techniques offer detailed insights into chemical bonding, material distribution, and structural changes during operation, allowing for the early detection of degradation or failure not readily captured by electrochemical methods alone.
By combining these advanced techniques, the research aims to improve the accuracy of battery health diagnostics and enable the early identification of potential failures. The ultimate objective is to establish a comprehensive framework for real-time monitoring and long-term prediction of battery health, with a particular emphasis on green lithium-ion batteries. This integrated approach marks a significant advancement in battery health monitoring, offering deeper insights into the factors influencing performance and contributing to the development of more durable and sustainable energy storage systems.