Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles

By Xiaosong Hu, Shengbo Eben Li, Yalian Yang. IEEE Transactions on Transportation Electrification, 2016

310 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f062cde1a66467ad593f9b5a6b8de8e82dcab43f

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. Maitane Berecibar
3. Lynn H. Kaack
4. Aoife M. Foley
5. Elise Nanini-Maury
6. J. Van Mierlo
7. Harry Ernst Hoster
8. Felix Creutzig

In Electrical and Electronic Engineering

1. Demis Hassabis
2. Kailong Liu
3. Alexandra Sasha Luccioni
4. Maitane Berecibar
5. Lynn H. Kaack
6. Aoife M. Foley
7. Elise Nanini-Maury
8. Yoshua Bengio

Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, methodology): “Work in this area has focused on predicting battery state, degradation, and remaining lifetime using supervised learning techniques, fuzzy logic, and clustering [228–235].”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “[129] proposed to use novel genetic algorithm-based fuzzy C-means clustering technique to partition the training data sampled of a lithium-ion battery during the driving cycle-based test, and the clustering result was then applied to automatically learn the topology and antecedent parameters of the ”