Kalman-variant estimators for state of charge in lithium-sulfur batteries

Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort

Comparative Evaluation of Investigation Methods for Estimating the Load-Dependent State of Charge and End of Discharge of a Multirotor UAV Battery

As the scope of multirotor unmanned aerial vehicle (UAV) applications increases, more attention is being paid to UAV energy requirements, which vary depending on the mission profile. To obtain accurate information about the UAV battery during flight, the idea of a digital twin including a battery state estimation model is promising. For battery state estimation, a Kalman filter combination is the preferred approach in the literature. Comparing different Kalman filters, the unscented Kalman filter has a more accurate estimation for nonlinear systems compared to the extended Kalman filter. In the application of UAV flight with load-dependent flight missions, the comparison of different Kalman filter estimation methods has not yet been researched. In order to evaluate the applicability of different state of charge estimation methods applied to different UAV flight missions, an extended Kalman filter, an unscented Kalman filter, and the Coulomb-counting method are implemented in this research and combined with an end of discharge estimation. To compare the estimation methods based on a delivery mission and a facade inspection mission, a parameter identification of the UAV battery is performed, and an equivalent circuit model is developed and combined with the estimation methods to estimate the battery state. The results of the investigation show that the unscented Kalman filter achieves more accurate state of charge estimation results than the extended Kalman filter, even in the field of UAV application. The results also show that the choice of estimation method is mainly influenced by the accuracy of the parameter identification process, while the dynamic load of a UAV mission has less impact. Contrarily, the end of discharge estimation does not correlate with the accuracy of the state of charge estimation, indicating that the end of discharge estimation is more dependent on the dynamic load