Critical comparison of equivalent circuit and physics-based models for lithium-ion batteries: A graphite/lithium-iron-phosphate case study

Lagnoni, Marco; Scarpelli, Claudio; Lutzemberger, Giovanni; Bertei, Antonio

doi:10.1016/j.est.2024.112326

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.112326

|View full text |Cite

Critical comparison of equivalent circuit and physics-based models for lithium-ion batteries: A graphite/lithium-iron-phosphate case study

Marco Lagnoni,

Claudio Scarpelli,

Giovanni Lutzemberger

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Jiang,

Zhai,

Long

et al. 2025

Journal of Power Sources

View full text Add to dashboard Cite

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Jiang,

Zhai,

Long

et al. 2025

Journal of Power Sources

View full text Add to dashboard Cite

Advances in Sustainable Battery Technologies: Enhancing Longevity, Recycling, and Alternative Components-- A Review

Hailemariam,

Birkneh

2024

AJAC

View full text Add to dashboard Cite

The field of sustainable battery technologies is rapidly evolving, with significant progress in enhancing battery longevity, recycling efficiency, and the adoption of alternative components. This review highlights recent advancements in electrode materials, focusing on silicon anodes and sulfur cathodes. Silicon anodes improve capacity through lithiation and delithiation processes, while sulfur cathodes offer high energy density, despite inherent challenges. Recycling technologies are also advancing, with mechanical methods achieving 60% efficiency, hydrometallurgical processes reaching 75%, and pyrometallurgical methods achieving 85% efficiency. These improvements in recycling contribute to a more sustainable lifecycle for batteries. Moreover, the shift towards alternative components, such as organic batteries, sodium-ion batteries, and solid-state batteries, is gaining momentum, representing 10%, 20%, and 15% of the market, respectively. These alternatives address environmental concerns and enhance battery performance and reliability. These developments underscore the importance of ongoing innovation in electrode materials and recycling technologies to overcome current challenges. As the industry continues to evolve, these advancements pave the way for more efficient and environmentally friendly energy storage solutions, promising a sustainable future for battery technologies.

show abstract

Lithium Battery SOC Estimation Based on Type-2 Fuzzy Cerebellar Model Neural Network

Zhao,

Ge,

Huang

et al. 2024

Electronics

View full text Add to dashboard Cite

Accurate estimation of the state of charge (SOC) of lithium batteries is critical for the safe and optimal operation of battery management systems (BMSs). Traditional SOC estimation methods are often limited by model inaccuracy and noise interference. In this study, a novel type-2 fuzzy cerebellar model neural network (Type-2 FCMNN) is proposed for accurately estimating the state of charge of lithium batteries. Based on the traditional fuzzy cerebellar model neural network (FCMNN), type-2 fuzzy rules are innovatively introduced to enhance the robustness of the model against uncertainties and noise disturbances. This enables the model to better cope with nonlinear complexity and external disturbances when dealing with SOC estimation of lithium batteries and significantly improves the accuracy of prediction. On this basis, by analyzing the working principle of lithium batteries, a battery equivalent circuit model is successfully established and simulated and tested by Simulink R2022b, which provides a theoretical basis for the selection of the size of the input parameters of the subsequent neural network. Then, this paper designs and implements the SOC estimation model based on Type-2 FCMNN and tests it in Matlab. Finally, this paper carries out simulation comparison experiments between Type-2 FCMNN and various classical neural network algorithms in Matlab R2022b including traditional FCMNN, a backpropagation neural network, radial basis function neural network, and Kalman filtering algorithm. The simulation results show that Type-2 FCMNN exhibits a significant advantage in SOC estimation accuracy, with mean absolute error and root mean square error values of only 43.1% and 36.0% of FCMNN’s, respectively, while FCMNN achieves the best results among the compared methods.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Critical comparison of equivalent circuit and physics-based models for lithium-ion batteries: A graphite/lithium-iron-phosphate case study

Cited by 5 publications

References 85 publications

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Multi-scale analysis of voltage curves for accurate and adaptable lifecycle prediction of lithium-ion batteries

Advances in Sustainable Battery Technologies: Enhancing Longevity, Recycling, and Alternative Components-- A Review

Lithium Battery SOC Estimation Based on Type-2 Fuzzy Cerebellar Model Neural Network

Contact Info

Product

Resources

About