Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. (July 2022)

• Record Type: Journal Article
• Authors: Liu, Kailong; Gao, Yizhao; Zhu, Chong; Li, Kang; Fei, Minrui; Peng, Chen; Zhang, Xi; Han, Qing-Long
• Is Part Of: Control engineering practice. Volume 124(2022)
• Publication Date: 2022-07
• Subjects: Lithium-ion battery -- Control-oriented management -- Energy storage -- Electrochemical model -- Model reduction -- Parameter identification; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105176
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Battery management systems based on electrochemical models could achieve more accurate state estimations and efficient battery controls with access to cell unmeasurable physical variables. As battery electrochemical models are governed by first-principle partial differential equation sets, model complexity and multiple parameter determination are bottlenecks for their wider applications. This paper gives a systematical review of recent advancements in electrochemical model development and parameterization. Specifically, classic pseudo-two-dimensional model and related model order reduction methodologies are first summarized and analyzed. Given that the homogenization hypothesis of the pseudo-two-dimensional model could lead to significant model mismatch under some operational conditions, enhanced models considering cell internal inhomogeneity with multi-particles, multi-scales, aging and thermal dynamics are examined. To facilitate model portability, parameter identification techniques of these models are classified, and solutions for optimizing the parameterization procedure are explored. Finally, current research gaps in the literature and remaining challenges are discussed and highlighted with some suggestions. This review will therefore inform the engineers of battery management and control engineering, whilst boosting the research, design and operation of control-oriented electrochemical models for smarter battery management at different readiness levels. Highlights: A systematic review on control-oriented battery electrochemical models is proposed. P2D model and various order-reduction techniques are investigated. Enhanced multi-particle, multi-scale, aging, and thermal models are explored. Different parameterization methods for electrochemical models are summarized. Future trends, challenges and suggestions are discussed.