State-of-Charge Estimation for Li-ion Batteries: A More Accurate Hybrid Approach

George Misyris, Dimitrios I. Doukas*, Theofilos A. Papadopoulos, Dimitris P. Labridis, Vassilios G. Agelidis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Modeling of battery energy storage systems (BESS) used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method.
Original languageEnglish
JournalI E E E Transactions on Energy Conversion
Issue number1
Pages (from-to)109 - 119
Publication statusPublished - 2018


  • Battery energy storage systems (BESS)
  • Capacity estimation
  • Coulomb Counting
  • Equivalent circuit model (ECM)
  • Model-based methods
  • Parameter identification
  • State estimation algorithms
  • State-of-charge (SOC) estimation

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