Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries

Lluís Trilla, Lluc Canals Casals, Jordi Jacas () and Pol Paradell
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Lluís Trilla: Power Systems Research Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain
Lluc Canals Casals: Energy Systems and Energy Harvesting Group, IREC, 08930 Barcelona, Spain
Jordi Jacas: Department of Engineering Projects and Construction, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
Pol Paradell: Power Systems Research Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain

Energies, 2022, vol. 15, issue 19, 1-14

Abstract: Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.

Keywords: lithium–sulfur battery; Battery Management System; cell model; SoC estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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