Model Order Reduction Techniques for Physics-Based Lithium-ion Battery Management: A Survey
Journal article, 2021

To unlock the promise of electrified transportation and smart grid, emerging advanced battery management systems (BMSs) shall play an important role in health-aware monitoring, diagnosis, and control of widely used lithium-ion (Li-ion) batteries. Sophisticated physics-based battery models incorporated in the advanced BMS can offer valuable battery internal information to achieve improved operational safety, reliability and efficiency, and to extend the lifetime of the batteries. However, developed from the fundamental electrochemical and thermodynamic principles, the rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. This article reviews prevailing order reduction techniques of physics-based Li-ion battery models to facilitate the development of next-generation BMSs. We analyze and comparatively characterize these techniques, mainly from perspectives of model fidelity, computational efficiency, and the scope of applications. By representing many effective and flexible reduced-order models as equivalent circuits, designers and practitioners, who do not have electrochemical expertise but with knowledge of circuit theory, can readily gain insights into multi-physical dynamics as well as their coupling effects inside the batteries. In addition, recommendations are made on how to select appropriate physics-based models for various model-based applications in battery management. Finally, the prospect of physical model-enabled BMSs is discussed, including the potential challenges and future research directions.

model order reduction

battericeller

electrochemical model

energy storage

battery management

lithium-ion batteries

Modelling

Energy systems modelling

Author

Yang Li

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Dulmini Karunathilake

Queensland University of Technology (QUT)

D. Mahinda Vilathgamuwa

Queensland University of Technology (QUT)

Yateendra Mishra

Queensland University of Technology (QUT)

Troy W. Farrell

Queensland University of Technology (QUT)

San Shing Choi

Queensland University of Technology (QUT)

Changfu Zou

Chalmers, Electrical Engineering, Systems and control, Automatic Control

IEEE Industrial Electronics Magazine

1932-4529 (ISSN)

Vol. In Press

Battery control via adaptive modeling and predictive control

Swedish Research Council (VR) (2019-04873), 2020-01-01 -- 2023-12-31.

Lithium-ion battery control for faster charging and longer life

European Commission (EC) (895337), 2020-11-01 -- 2022-10-31.

Driving Forces

Sustainable development

Subject Categories

Energy Engineering

Other Engineering and Technologies not elsewhere specified

Electrical Engineering, Electronic Engineering, Information Engineering

Areas of Advance

Energy

DOI

10.1109/MIE.2021.3100318

More information

Latest update

9/8/2021 5