On state-of-charge determination for lithium-ion batteries
By Zhe Li, Jun Huang, Bor Yann Liaw, Jianbo Zhang. 2017
284 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/6279b554449d6835f238400c8b78bc0224d9e08c
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
- Xiaosong Hu
- Saad Mekhilef
- Moh Yamani Idna Idris
- Prashant Shrivastava
- Tey Kok Soon
- Rui Xiong
- Lei Zhang
- Changfu Zou
In Electrical and Electronic Engineering
- Xiaosong Hu
- Tey Kok Soon
- Saad Mekhilef
- Moh Yamani Idna Idris
- Prashant Shrivastava
- Rui Xiong
- Changfu Zou
- Lei Zhang
Citations in the corpus
- Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018, methodology): “To date, the coulomb-counting technique is the most commonly used approach for SOC estimation in realistic BMSs, owing to its simplicity and ease of implementation [7].”
- Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles (2018, background): “Reference [2], [10], [16], [17] have presented a detailed SOC estimation in terms of overall research progress, future development trends and the origin of SOC estimation.”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “SOC estimation based on machine learning can be divided into two phases: data training and learning [27].”