An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery
By Jinhao Meng, Mattia Ricco, Guangzhao Luo, Maciej Swierczynski, Daniel-Ioan Stroe, Ana-Irina Stroe, Remus Teodorescu. IEEE transactions on industry applications, 2018
335 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/6ef3679e4fb28ab21b02a45b711cdb3db361ad4a
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
- Moh Yamani Idna Idris
- Remus Teodorescu
- Guangzhao Luo
- Mattia Ricco
- Daniel-Ioan Stroe
- Saad Mekhilef
- Maciej Swierczynski
- Prashant Shrivastava
In Electrical and Electronic Engineering
- Moh Yamani Idna Idris
- Remus Teodorescu
- Guangzhao Luo
- Mattia Ricco
- Daniel-Ioan Stroe
- Saad Mekhilef
- Maciej Swierczynski
- Prashant Shrivastava
Citations in the corpus
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, background): “Currently, owing to its high accuracy and selfcorrecting features, KF family-based SOC estimation is one of the preferred approaches for online SOC estimation [8].”
- Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles (2018, methodology): “Considering the requirement in terms of robustness and accuracy for SOC estimation, model-based estimation has become popular for EV applications recently.”