Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
By Yunwei Zhang, Qiaochu Tang, Yao Zhang, Jiabin Wang, U. Stimming, Alpha Albert Lee. Nature Communications, 2020
764 citations recorded by the source corpus.
Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)—a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis—with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures—the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems. Forecasting the state of health and remaining useful life of batteries is a challenge that limits technologies such as electric vehicles. Here, the authors build an accurate battery performance forecasting system using machine learning.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ca94a1d2da80e813b707f40f38a31e9e2720c879
How researchers see this paper
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In Automotive Engineering
- Widanalage Dhammika Widanage
- Xinhua Liu
- Shichun Yang
- Billy Wu
- Valentin Sulzer
- Anna G. Stefanopoulou
- David A. Howey
In Electrical and Electronic Engineering
- Widanalage Dhammika Widanage
- Xinhua Liu
- Shichun Yang
- Billy Wu
- Valentin Sulzer
- Anna G. Stefanopoulou
- David A. Howey
Citations in the corpus
- The challenge and opportunity of battery lifetime prediction from field data (2021, background): “[33], who use raw electrochemical impedance spectroscopy data to predict remaining useful life (RUL).”
- Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “Zhang et al. [59] for instance showed that by taking the entire EIS spectrum and combining this with Gaussian process regression and an automatic relevance determination algorithm, that accurate RUL estimations can be achieved.”