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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Athar Ahmad & Mario Iamarino & Antonio D’Angola, 2024. "A Nernst-Based Approach for Modeling of Lithium-Ion Batteries with Non-Flat Voltage Characteristics," Energies, MDPI, vol. 17(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3914-:d:1452153
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    References listed on IDEAS

    as
    1. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
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