Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis
This addresses the bottleneck of inadequate understanding of LIB degradation, which limits battery durability and safety for applications like electrification to mitigate climate change, and is incremental as it combines existing physics and statistical learning techniques.
The paper tackled the problem of lithium-ion battery degradation by proposing a hybrid physics-based and data-driven model for online diagnosis and prognosis, resulting in improved generalizability, interpretability, and well-calibrated uncertainty predictions.
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.