IFE Brage (Institute for Energy Technology)
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Instrumental broadening and the radial pair distribution function with 2D detectors
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A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things
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Revisiting Pulse-Based OCV Incremental Capacity Analysis for Diagnostics of Li-Ion Batteries
Full text linkThis paper presents the concept of applying incremental capacity analysis (ICA) on the OCV curve in the SoC space. The OCV curve can be obtained from any sequence of discharge or charge current or power pulse with a necessary rest period to allow the cell to reach a pseudo-OCV after each pulse. With a high resolution (>100 pulses) in the full SoC window, an OCV-vs.-SoC curve can be obtained with sufficient accuracy to perform an ICA on the obtained OCV curve. ICA as a diagnostic technique has commonly been applied on Li-ion cells with constant charge and discharge at slow currents. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to a battery in an application. Here, we revisit pulse-based ICA to supplement the conventional constant-current-based technique. Based on actual ageing data, we show that ICA performed on a selection of high-resolution OCV curves is comparable or better than conventional ICA with constant current. The main advantage of OCV-ICA is that it can be applied to most cells and systems without a significant interruption of normal cell operation. OCV-ICA can provide valuable insights into ageing mechanisms as well as, e.g., detailed information on changes in internal resistance.Revisiting Pulse-Based OCV Incremental Capacity Analysis for Diagnostics of Li-Ion BatteriespublishedVersio
A three-phase dispersion profile model for stratified pipe flow: Effects of gas bubbles on the distribution of oil and water droplets
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Identifying and analysing important model assumptions: Combining techno-economic and political feasibility of deep decarbonisation pathways in Norway
Full text linkUnderstanding the political feasibility of transition pathways is a key issue in energy transitions. Policy changes are a significant source of uncertainty in energy system optimisation modelling. Energy system models are nevertheless continuously being updated to reflect policy signals as realistically as possible. Using the concept of transition pathways as a starting point, this cross-disciplinary study combines energy system optimization modelling with political feasibility of different transition pathways. This combination generates insights into key political decision points in the ongoing energy transition. Resting on actor support structure and political feasibility of four main pathway categories (electrification, hydrogen, biomass, and energy efficiency), we identify critical model assumptions that are politically significant and impact model outcome. Then, by replacing the critical assumptions with technical limitations we model a scenario that is unrestrained by assumptions about policy, we identify areas where political choices are key to model outcomes. The combination of actor preferences and modelled energy system consequences enables the identification of future key decision points. We find that there is considerable support for electrification as the main pathway to net-zero. The implications of widespread electrification, in terms of energy production and grid capacity, lead us to identify challenging policy decisions with implications for the energy transition.Identifying and analysing important model assumptions: Combining techno-economic and political feasibility of deep decarbonisation pathways in NorwaypublishedVersio
