Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

By Zhongwei Deng, Lin Yang, Yishan Cai, Hao Deng, Liu-Yi Sun. 2016

142 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c400e7a598afe12bdaf01cee3a45c8c12f09b1fc

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. P. Kollmeyer
2. Ali Emadi
3. Ryan M. Ahmed
4. Matthias Preindl
5. Ephrem Chemali
6. Carlos Vidal
7. Pawel Malysz

In Electrical and Electronic Engineering

1. P. Kollmeyer
2. Ali Emadi
3. Ryan M. Ahmed
4. Matthias Preindl
5. Ephrem Chemali
6. Carlos Vidal
7. Pawel Malysz

Citations in the corpus

• Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, background): “like artificial neural network (ANN) [21], extreme learning machines [22], and SVMs [23], [24].”