Hybrid physics-based and data-driven modeling with calibrated
uncertainty for lithium-ion battery degradation diagnosis and prognosis
- URL: http://arxiv.org/abs/2110.13661v1
- Date: Mon, 25 Oct 2021 11:14:12 GMT
- Title: Hybrid physics-based and data-driven modeling with calibrated
uncertainty for lithium-ion battery degradation diagnosis and prognosis
- Authors: Jing Lin, Yu Zhang, Edwin Khoo
- Abstract summary: Lithium-ion batteries (LIBs) are key to promoting electrification in the coming decades.
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.
- Score: 6.7143928677892335
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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