  Advancing lithium-ion batteries (LIBs) in both design and usage is key to
promoting electrification in the coming decades to mitigate human-caused
climate change. Inadequate understanding of LIB degradation is an important
bottleneck that limits battery durability and safety. Here, we propose hybrid
physics-based and data-driven modeling for online diagnosis and prognosis of
battery degradation. Compared to existing battery modeling efforts, we aim to
build a model with physics as its backbone and statistical learning techniques
as enhancements. Such a hybrid model has better generalizability and
interpretability together with a well-calibrated uncertainty associated with
its prediction, rendering it more valuable and relevant to safety-critical
applications under realistic usage scenarios.
