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Economic, environmental and social pressures have increased the need for business organisations to control and manage their energy performance on a continual basis. Responding to these pressures follows a learning curve that is influenced by changing drivers and barriers. Consequently, different energy management factors have different development priorities over time. This paper explores the development priority of one factor, namely, energy performance measurement, in the energy-intensive industrial sector, which is the most advanced industrial sector in its energy management learning curve. In addition, the paper identifies the research and development needs of energy performance measurement that are required to further improve energy performance. The results are based on interviews carried out with managers and operators in three energy-intensive industrial sectors in Finland. Energy performance measurement is found to be the third development priority in energy management, behind resource and commitment issues. This represents a paradox as resources and commitment are prerequisites for energy performance measurement to be developed, whereas energy performance measurement influences the very same issues by enforcing changed behaviour. Several deficiencies are identified in energy performance measurement in the temporal, systemic and organisational dimensions. Research should be continued towards the implementation of energy performance measurement as a process, the integration of energy performance metrics into overall management and the development of metrics for different industrial sectors, companies and operating cultures

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.

Reducing the rates of nuclear power generation for all electric power sources has been seriously discussed in Japan since the accident at the Fukushima No. 1 nuclear power plant in March 2011. Thus, the distributed power supply is expected to expand. The local production of safe and clean energy for local consumption is greatly needed in Japan. In this paper, the Saroma Lake green microgrid (SLMG), a fuel cell microgrid using tidal power generation and photovoltaics, has been planned. Energy balance equations were used to investigate this system’s method of operation. Further simulated analysis of the facility, its operation cost, and the electric power quality of the network were conducted using MATLAB/Simulink, and the relationships among the capacity of a facility, the cost, and supply rate of green energy and the electric power quality (interphase voltage, frequency, and higher harmonic wave) of the power network were clarified. The total projected cost of the equipment and operation for introducing the proposed SLMG is \1,500,000,000/10 years. Increasing the supply rate of green energy and reducing the facility cost will require the introduction of biofuels and the reduction of the facility costs of solid oxide fuel cells in the SLMG.