A Nernst-Based Approach for Modeling of Lithium-Ion Batteries with Non-Flat Voltage Characteristics

Athar Ahmad, Mario Iamarino and Antonio D’Angola ()
Additional contact information
Athar Ahmad: Dipartimento di Ingegneria, Università degli Studi della Basilicata, 85100 Potenza, Italy
Mario Iamarino: Dipartimento di Ingegneria, Università degli Studi della Basilicata, 85100 Potenza, Italy
Antonio D’Angola: Dipartimento di Ingegneria, Università degli Studi della Basilicata, 85100 Potenza, Italy

Energies, 2024, vol. 17, issue 16, 1-14

Abstract: This paper presents an easy-to-implement model to predict the voltage in a class of Li-ion batteries characterized by non-flat, gradually decreasing voltage versus capacity. The main application is for the accurate estimation of the battery state of the charge, as in the energy management systems of battery packs used in stationary and mobility applications. The model includes a limited number of parameters and is based on a simple equivalent circuit representation where an open circuit voltage source is connected in series with an equivalent resistance. The non-linear open circuit voltage is described using a Nernst-like term, and the model parameters are estimated based on the manufacturer discharge curves. The results show a good level of model accuracy in the case of three different commercial batteries considered by the study: Panasonic CGR18650AF, Panasonic NCR18650B and Tesla 4680. In particular, accurate description of the voltage curves versus the state of charge at different constant currents and during charging/discharging cycles is achieved. A possible model reduction is also addressed, and the effect of the equivalent internal resistance in improving the model predictions near fully depleted conditions is highlighted.

Keywords: Li-ion battery modeling; SoC prediction; Nernst equation; energy management system; CGR18650AF; NCR18650B; Tesla 4680 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3914/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3914/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:3914-:d:1452153

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().