Fractional-order modeling and parameter identification for lithium-ion batteries

By Baojin Wang, Shengbo Eben Li, Huei Peng, Zhiyuan Liu. 2015

218 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/208d166568669e2f83b1034c581a6f033edf8207

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. Xiaosong Hu
2. Lei Zhang
3. Zhenpo Wang
4. Michael G. Pecht
5. Changfu Zou
6. Torsten Wik
7. Moh Yamani Idna Idris
8. Saad Mekhilef

In Control and Systems Engineering

1. Xiaosong Hu
2. Michael G. Pecht
3. Saad Mekhilef

Citations in the corpus

• Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “The ECIM developed from the Randles model depicts higher accuracy compared with the commonly used 1RC model under a wide range of uncertainties [146,147].”
• Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018, methodology): “More information about the PSO and its application to battery systems can be found in [29], [31] and references therein.”
• A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (2018, methodology): “[59] presented an FOM by adding a Warburg element (W) in series with the charge-transfer resistor (R1) (s e Fig.”