Open Collections

Wang, Yixiu

University of British Columbia

Ensuring the safe and reliable usage of Lithium-ion batteries (LIBs) necessitates accurate degradation modeling. While data-driven methods offer promising prospects for modeling battery degradation, the intricate structures often make them specific to datasets. Furthermore, the black-box nature of data-driven models complicates the understanding of their decision-making process. In this thesis, we delve into data-driven modeling of battery degradation, focusing on capacity estimation and cycle life prediction, to address the challenges of generalizability and interpretability. To improve the generalizability of the model, we first propose adopting a simple and robust machine learning model, partial least squares regression (PLSR), for joint battery capacity estimation and remaining useful life (RUL) prediction. Experimental results on three battery cells cycled at varied conditions demonstrate superior generalizability of the suggested model over complex and sophisticated methods. Another approach we propose to improve the generalizability of the model is to use transfer learning. This approach presents excellent performance in handling the significant diversity in different types of batteries, as it can transfer the knowledge contained in well-studied batteries to a new battery. The key idea involves training a model in one type of battery with sufficient data. Then, the model can be applicable to a new type of battery by fine-tuning some parameters with limited data. Experimental results confirm that transfer learning can effectively enhance the generalizability of data-driven models in capacity estimation and cycle life prediction across different battery types. To build interpretable models, we advocate the use of decision trees for capacity estimation. We start with a classic regression tree with parallel splits for capacity estimation, but it requires a tree depth of 11 to achieve satisfactory performance. To address this challenge, we adopt optimal regression trees with hyperplane splits and propose a novel algorithm, DE-LR-ORTH, to train such a tree. DE-LR-ORTH initially conducts a one-step optimal hyperplane split for each branch node via differential evolution, followed by logistic regression-based fine-tuning to achieve overall optimality. Additionally, a GPU-accelerated implementation is proposed to significantly reduce the training time. Experimental results reveal a 1.0% capacity estimation error at depth 6 while maintaining high interpretability.

Text

2023-12-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/86983

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/