• Energy Research
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AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Spinel LiNi0.5Mn1.5O4 as one of the high-energy positive electrode materials for next generation Li-ion batteries has attracted significant interest due to its economic and environmental advantages. However, the sensitivity of this type of material upon short to long term ambient storage conditions and the impact on the electrochemical performances remains poorly explored. Nevertheless, this remains an important aspect for practical large-scale synthesis, storage and utilization. Herein, we study and compare the evolution of surface chemistry, bulk crystal structure and elemental content evolution and distribution of LiNi0.5Mn1.5O4 using a variety of characterization techniques including XPS and STEM-EDS-EELS, as well as electrochemical analysis. We show that Mn species dominate the outer surface (0–5 nm), while Ni and Li are preferentially located further away and in the bulk. The studied LiNi0.5Mn1.5O4 material is found to be stable, with minor changes in surface or bulk characteristics detected, even after 12 months of storage under ambient air conditions. The low surface reactivity to air also accounts for the minor changes to the electrochemical performance of the air-exposed LiNi0.5Mn1.5O4, compared to the pristine material. This study provides guidance for the appropriate storage, handling and processing of this high-performance cathode material.

Spinel LiNi0.5Mn1.5O4 as one of the high-energy positive electrode materials for next generation Li-ion batteries has attracted significant interest due to its economic and environmental advantages. However, the sensitivity of this type of material upon short to long term ambient storage conditions and the impact on the electrochemical performances remains poorly explored. Nevertheless, this remains an important aspect for practical large-scale synthesis, storage and utilization. Herein, we study and compare the evolution of surface chemistry, bulk crystal structure and elemental content evolution and distribution of LiNi0.5Mn1.5O4 using a variety of characterization techniques including XPS and STEM-EDS-EELS, as well as electrochemical analysis. We show that Mn species dominate the outer surface (0–5 nm), while Ni and Li are preferentially located further away and in the bulk. The studied LiNi0.5Mn1.5O4 material is found to be stable, with minor changes in surface or bulk characteristics detected, even after 12 months of storage under ambient air conditions. The low surface reactivity to air also accounts for the minor changes to the electrochemical performance of the air-exposed LiNi0.5Mn1.5O4, compared to the pristine material. This study provides guidance for the appropriate storage, handling and processing of this high-performance cathode material.