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The recent pandemic caused by COVID-19 has globally affected air transport mobility as well as the airline industry in general. Numerous restrictions have been implemented in airline transport which is potentially leading towards severe long term impacts on the global airline industry. In this paper, air transport mobility was analyzed regarding Europe (EU) based on the available data from the relevant sources associated with the airline industry. Data were analyzed in specific periods from January to April of 2020, which corresponded with the initialization of the pandemic in the EU and later in its full development. Specifically, two airports were selected in Croatia as case studies to analyze the impact of COVID-19 more thoroughly on mobility together with the estimation of carbon footprint during the pandemic and the year before the pandemic state. The results revealed that COVID-19 gradually affected air transport mobility in the EU where a peak was reached in April with a reduction in the number of flights in the EU region reaching more than 89%. Cargo traffic was not significantly affected by the pandemic, and was even increased in some cases due to the supply of medical equipment in the fight against the disease. The analyzed case study revealed the reduction in air transport mobility for selected airports to be more than 96%, which directly affected the reduction of CO2 emissions to factor 1.81 for the commercial airport of Zagreb and 3.49 for the seasonal airport of Split. A normalization of air transport mobility is expected to be reached through a one-year period with a continued reduction in the number of flights ranging from 15% to 25%, based on the projections and expectations from relevant EU associations.

Lithium-ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium-ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two-step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two-step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low-current discharges, while the second group contains kinetic parameters, which are estimated using a well-designed highly-dynamic pulse (dis-)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium-ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.