Predicting the state of charge and health of batteries using data-driven machine learning

By Man-Fai Ng, Jin Zhao, Qingyu Yan, Gareth John Conduit, Z. Seh. Nature Machine Intelligence, 2020

633 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/86788a38467e1f2f1df713f5d5694bfee9f8ae29

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Frede Blaabjerg
2. Xinhua Liu
3. Shichun Yang
4. Widanalage Dhammika Widanage
5. Huai Wang
6. Billy Wu

In Electrical and Electronic Engineering

1. Huai Wang
2. Frede Blaabjerg
3. Xinhua Liu
4. Shichun Yang
5. Widanalage Dhammika Widanage
6. Shuai Zhao
7. Billy Wu
8. Xiongfei Wang

Citations in the corpus

• An Overview of Artificial Intelligence Applications for Power Electronics (2020, methodology): “AI methods in RUL prediction is typically dealing with a nonlinear regression between the degradation information and the corresponding RUL based on the training dataset [167].”
• Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “Various works in applying techniques such as ANNs, support vector machines (SVM), Gaussian/Bayesian regression, random forest and KF based approaches have been reported for predicting states such as SOC, SOH and RUL which are well summarised by Ng et al. [52] and Li et al. [53] .”