Battery Energy Storage Systems Modeling for Online Applications

Georgios Misyris, Tomas Tengner, Antonis G. Marinopoulos, Dimitrios I. Doukas, Dimitris P. Labridis

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Over the last decade the use of battery energy storage systems (BESS) on different applications, such as smart grid and electric vehicles, has been increasing rapidly. Therefore, the development of an electrical model of a battery, capable to estimate the states and the parameters of a battery during lifetime is of critical importance. To increase the lifetime, safety and energy usage, appropriate algorithms are used to estimate, with the lowest estimation error, the state of charge of the battery, the battery impedance, as well as its remaining capacity. This paper focuses on the development of model-based online condition monitoring algorithms for Li-ion battery cells, which can be extended to battery modules and systems. The condition monitoring algorithms were implemented after considering an optimal trade-off between their accuracy and overall complexity.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE Manchester PowerTech
Number of pages6
Publication date2017
Publication statusPublished - 2017
Externally publishedYes
Event12th IEEE Power and Energy Society PowerTech Conference: Towards and Beyond Sustainable Energy Systems - University Place, University of Manchester., Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017


Conference12th IEEE Power and Energy Society PowerTech Conference
LocationUniversity Place, University of Manchester.
CountryUnited Kingdom


  • Battery energy storage systems (BESS)
  • Capacity
  • Equivalent circuit model (ECM)
  • Internal resistance
  • Model-based method
  • Parameter identification
  • State of charge (SOC)
  • State of health (SOH)

Cite this

Misyris, G., Tengner, T., Marinopoulos, A. G., Doukas, D. I., & Labridis, D. P. (2017). Battery Energy Storage Systems Modeling for Online Applications. In Proceedings of 2017 IEEE Manchester PowerTech IEEE.