Accurately predicting battery behavior, while using low input data, is highly desirable in embedded simulation architectures like grid or integrated energy system analysis. Currently, the available vanadium redox flow battery (VRFB) models achieve highly accurate predictions of electrochemical behavior or control algorithms, while the optimization of the required input data scope is neglected. In this study, a parametrization tool for a DC grey box simulation model is developed using measurements with a 10 kW/100 kWh VRFB. An objective function is applied to optimize the required input data scope while analyzing simulation accuracy. The model is based on a differential-algebraic system, and an optimization process allows model parameter estimation and verification while reducing the input data scope. Current losses, theoretical storage capacity, open circuit voltage, and ohmic cell resistance are used as fitting parameters. Internal electrochemical phenomena are represented by a self-discharge current while material related losses are represented by a changing ohmic resistance. Upon reducing input data the deviation between the model and measurements shows an insignificant increase of 2% even for a 60% input data reduction. The developed grey box model is easily adaptable to other VRFB and is highly integrable into an existing energy architecture.

The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m² and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.

For increased penetration of energy production from renewable energy sources at a utility scale, battery storage systems (BSSs) are a must. Their levelized cost of electricity (LCOE) has drastically decreased over the last decade. Residential battery storage, mostly combined with photovoltaic (PV) panels, also follow this falling prices trend. The combined effect of the COVID-19 pandemic and the war in Ukraine has caused such a dramatic increase in electricity prices that many consumers have adjusted their strategies to become prosumers and self-sufficient as feed-in subsidies continue to drop. In this study, an investigation is conducted to determine how profitable it is to install BSSs in homes with regards to battery health and the levelized cost of total managed energy. This is performed using mixed-integer linear programming (MILP) in MATLAB, along with its embedded solver Intlinprog. The results show that a reasonable optimized yearly cycling rate of the BSS can be reached by simply considering a non-zero cost for energy cycling through the batteries. This cost is simply added to the electricity cost equation of standard optimization problems and ensures a very good usage rate of the batteries. The proposed control does not overreact to small electricity price variations until it is financially worth it. The trio composed of feed-in tariffs (FITs), electricity costs, and the LCOE of BSSs represents the most significant factors. Ancillary grid service provision can represent a substantial source of revenue for BSSs, besides FITs and avoided costs.

This research concerns the design and prototyping of an artificial middle finger, using Shape Memory Alloys (SMAs), PolyLactic Acid (PLA), and other technologies. The design is a biomimicry of the human biological anatomical and muscular systems. After briefly describing the operational features and functioning of natural striated muscles, the document reviews the features, advantages and disadvantages of SMAs in the perspective of their use as an actuator of a prosthetic finger. Using different design parameters, such as the lightness of the device, actuation complexity, and resilience, a working prototype is proposed meeting the established criteria.

Off-highway vehicle electrification requires traction motors combining high torque density with reliable performance across demanding duty cycles, yet finite-element-based optimization remains computationally demanding for broad design-space exploration. This study addresses the gap with a sensitivity-analysis-based, surrogate-assisted multi-objective optimization framework for a 12-pole/72-slot, 120 kW Interior Permanent-Magnet Synchronous Motor (IPMSM) for a compact wheel-loader drivetrain, coupling Ansys Motor-CAD with Ansys OptiSLang. A Latin Hypercube sensitivity study of thirteen geometric parameters identifies the dominant design drivers, and an evolutionary algorithm operating on the validated surrogate produces a Pareto-optimal set, from which the final design is selected using the CRITIC–TOPSIS method applied to finite-element-validated feasible designs. Relative to the baseline, the validated performance shows a 7.4% increase in continuous torque, a 4.0% increase in peak torque, a 4.0% increase in efficiency, and a 53.5% reduction in torque ripple, with mass essentially unchanged, while also revealing that surrogate predictions were markedly optimistic relative to the finite-element results. These findings demonstrate an efficient, reliable route to high-performance IPMSM design for off-highway applications.