Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation

By Yongzhi Zhang, Rui Xiong, Hongwen He, Weixiang Shen. IEEE transactions on power electronics, 2017

206 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c29a14fd4763d55b26aaf387d12ccd21be1c2300

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. Moh Yamani Idna Idris
2. Saad Mekhilef
3. Prashant Shrivastava
4. Tey Kok Soon

In Control and Systems Engineering

1. Saad Mekhilef

In Electrical and Electronic Engineering

1. Moh Yamani Idna Idris
2. Saad Mekhilef
3. Prashant Shrivastava
4. Tey Kok Soon
5. Yousef A. Mahmoud
6. Ehab F. El-Saadany
7. Mohamed A. Abdelwahed
8. Zengshi Chen

Citations in the corpus

• Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, unknown): “lifetime [1], [2].”
• Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “For online parameter identification, the weighted RLS [130] is the most appealing method, and it requires only the battery current and voltage for parameter identification.”