A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

By Changfu Zou, Lei Zhang, Xiaosong Hu, Zhenpo Wang, Torsten Wik, Michael G. Pecht. Journal of Power Sources, 2018

452 citations recorded by the source corpus.

Abstract Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced battery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.

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

View Paper: https://www.semanticscholar.org/paper/e5c84a9c98daf5e227306c06487ea1c87bcd8c5d

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. Xuning Feng
2. Minggao Ouyang
3. Languang Lu
4. Xiaosong Hu
5. Xiangming He
6. Dongsheng Ren
7. Xuebing Han
8. Lei Zhang

In Control and Systems Engineering

1. Xiaosong Hu

In Electrical and Electronic Engineering

1. Xuning Feng
2. Minggao Ouyang
3. Languang Lu
4. Xiaosong Hu
5. Xiangming He
6. Dongsheng Ren
7. Xuebing Han
8. Caihao Weng

Citations in the corpus

• Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus (2019, methodology): “An equivalent internal resistance model is employed to simulate the battery circuit, in which the open circuit voltage and equivalent internal resistance are parametric functions of the battery SOC [52].”
• Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019, background): “2927120 electric vehicles greatly drive the revolution of vehicle automation and electrification [2], [3].”
• Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, unknown): “[145] developed the ROM with fraction element.”
• Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018, methodology): “By using CPEs, nonuniform boundary and distributed intercalation/de-intercalation processes within porous electrodes can potentially be described [25], [41].”