BigInsight congratulates Clara Bertinelli Salucci from BigInsight and the the Department of Mathematics, who successfully defended her thesis "Advancing data-driven diagnostics and prognostics for lithium-ion batteries: A focus on model interpretability and accuracy" for the degree of Philosophiae Doctor on April 19, 2024.

Main research findings:
Machine Learning techniques are transforming industries, yet their opaque decision-making poses challenges, especially in safety-critical applications like lithium-ion (Li-ion) batteries. Li-ion batteries are pivotal in driving sustainable transportation but are also associated with hazards, as seen in incidents such as explosions leading to particularly intense fire. Traditional testing methods for battery health are costly and disruptive. My research addresses this issue by integrating data-driven methodologies within the Battery Management Systems (BMS). These algorithms, utilising sensor data, offer an efficient monitoring of battery health and remaining life, enabling both diagnostics and prognostics. By achieving a delicate balance between interpretability and prediction accuracy, they may enhance battery performance and durability while mitigating risks associated with degradation. Moreover, the methodologies developed in this research hold promise for broader applications beyond battery diagnostics, including fields like biostatistics. By bridging the gap between interpretability and prediction accuracy, these methods offer versatile solutions for addressing complex data challenges across various domains.

Adjudication committee:

• Professor Emeritus Bo Henry Lindquist, NTNU: Norwegian University of Science and Technology

• Professor Sören Ehlers, Hamburg University of Technology

• Associate Professor Thordis Thorarinsdottir, University of Oslo

 Supervisors:

• Associate Professor Riccardo De Bin, University of Oslo

• Professor Ingrid Kristine Glad, University of Oslo

• Azzeddine Bakdi

• Associate Professor Erik Vanem, University of Oslo