Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

By Claudio Sbarufatti, Matteo Corbetta, Marco Giglio, Francesco Cadini. 2017

207 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8a09d81d2ac61e30c25da97d1b2b8deb6774e076

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. Kailong Liu
2. Yi Li
3. Widanalage Dhammika Widanage
4. Kang Li
5. Xiaosong Hu
6. Qiao Peng
7. Cheng Zhang
8. Mattin Lucu

In Control and Systems Engineering

1. Kailong Liu
2. Widanalage Dhammika Widanage
3. Yi Li
4. Kang Li
5. Xiaosong Hu
6. Qiao Peng
7. Cheng Zhang

In Electrical and Electronic Engineering

1. Kailong Liu
2. Yi Li
3. Kang Li
4. Widanalage Dhammika Widanage
5. Xiaosong Hu
6. Qiao Peng
7. Cheng Zhang
8. Mattin Lucu

Citations in the corpus

• Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, methodology): “vector regression [15], [16], Bayesian prediction [17], [18], and artificial neural network [19]–[21] have been successfully applied to build data-driven models for battery cyclic aging”
• A brief review on key technologies in the battery management system of electric vehicles (2018, methodology): “Besides, the adaptive data-driven approaches [21,22] can be used to achieve better battery modelling results.”