@ARTICLE{Hallmann_Marcel_Methods_2022,
 author={Hallmann, Marcel and Wenge, Christoph and Komarnicki, Przemyslaw and Balischewski, Stephan},
 volume={vol. 71},
 number={No 1},
 journal={Archives of Electrical Engineering},
 pages={139-157},
 howpublished={online},
 year={2022},
 publisher={Polish Academy of Sciences},
 abstract={The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10–120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed.},
 type={Article},
 title={Methods for lithium-based battery energy storage SOC estimation. Part I: Overview},
 URL={http://journals.pan.pl/Content/122621/PDF/art09_corr.pdf},
 doi={10.24425/aee.2022.140202},
 keywords={battery modeling, equivalent circuit, estimation algorithm, lithium-ion battery energy storage, simulation, state of charge (SOC)},
}