Development of electrolyte descriptors for predicting cycling performance of electrochemical cells
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2023
Författare
Haugaard, Victor
Silverstam, Gustav
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This master’s thesis presents the development of structural electrolyte descrip tors utilized for predicting the cycling performance of electrochemical cells such
as lithium-ion batteries (LIBs). The research is conducted in collaboration with
Compular, a startup company focusing on materials development and battery tech nology improvement. Through molecular dynamics (MD) simulations and trajectory
analysis facilitated by Compular’s software, electrolyte descriptors are developed,
integrating structural and electrochemical molecular properties. The simulations
applied are based on data from battery performance tests, collected and annotated
in this project. We propose to use machine learning (ML) to model the relationship
between the chemical structure of electrolytes and their performance characteristics.
The developed descriptors function as input to a graph neural network (GNN) and
thereby offer a novel and efficient method for evaluating electrolyte performance
and optimizing electrochemical cells. The findings of this thesis confirm that the
descriptors successfully extract necessary information from electrolytes using Com pular’s analysis software, CHAMPION, and demonstrate their compatibility with
the GNN. Moreover, the discussion highlights the importance of annotated data,
the complexity of electrolyte descriptors and their predictive abilities. Limitations,
challenges and potential enhancements are also addressed, underscoring the need
for a larger dataset and exploring possible actions to enhance the performance of
the model. In conclusion, this research bridges the gap between empirical experi ments and theoretical understanding of battery cycling performance while reducing
the need for extensive manual testing. It provides a foundation for further inves tigations into electrolyte performance prediction and represents a significant step
towards more efficient and sustainable battery technologies.
Beskrivning
Ämne/nyckelord
lithium-ion batteries, electrolytes, descriptors, machine learning, graph neural network, molecular dynamics