Lithium-ion battery digitalization: Combining physics-based models and machine learning

Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Renewable & sustainable energy reviews Jg. 200; S. 114577
Hauptverfasser: Amiri, Mahshid N., Håkansson, Anne, Burheim, Odne S., Lamb, Jacob J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2024
Schlagworte:
Battery control
Battery design
Computational cost
Digital twin
Digitalization
Electrochemical models
Hybrid models
Lithium-ion battery
Machine learning
Physics-based models
Lithium-ion battery
Digitalization
Digital twin
Battery design
Hybrid models
Electrochemical models
Machine learning
Battery control
Physics-based models
Computational cost
ISSN:1364-0321
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at the cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field. [Display omitted] •Physics-based battery models are computationally expensive.•Machine learning models facilitate fast computation of battery states.•Machine learning models do not provide insight about the physical mechanisms.•Hybrid models allow battery monitoring, control and design.
ISSN:1364-0321
DOI:10.1016/j.rser.2024.114577