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Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview

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  • Panagiotis Eleftheriadis

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Spyridon Giazitzis

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower.

Suggested Citation

  • Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:3:p:32-599:d:1239485
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    References listed on IDEAS

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    2. Singh, S. & Budarapu, P.R., 2024. "Deep machine learning approaches for battery health monitoring," Energy, Elsevier, vol. 300(C).

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