Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

<p>Fast charging is crucial to the proliferation of electric vehicles. Fast charging is limited by lithium plating, which is the deposition of lithium metal on the anode surface instead of intercalation of lithium into the anode. Lithium plating causes capacity fade, increases cell resistance, and presents safety issues. A fast charging strategy was implemented using a battery management system (BMS) that avoided lithium plating by predicting the anode impedance. Commercial pouch cells modified with a reference electrode were cycled with and without the BMS. Cells cycled with the BMS avoided lithium plating but experienced significant degradation at the cathode. Cells cycled without the BMS underwent extensive lithium plating at the anode. Capacity loss was differentiated into irreversible and irretrievable capacity to understand electrode-based degradation mechanisms. Post-mortem analysis on harvested electrodes showed that the BMS cycled cells exhibited minimal anode degradation and had a two-times higher capacity loss on the cathode. The cells cycled without the BMS had extensive anode degradation caused by lithium plating and a seven-times higher capacity loss on the anode. </p> <p> </p> <p>Understanding and preventing the aging mechanisms of lithium-ion batteries is necessary to prolong battery life. Traditional full cell measurements are limited because they cannot differentiate between degradation processes that occur separately on anode and cathode. A reference electrode was inserted into commercial cylindrical lithium-ion cells to deconvolute the anode and cathode performance from the overall cell performance. Two configurations of the reference electrode placement inside the cell were tested to find a location that was stable and had minimal interference on the full cell performance. The reference electrode inside the mandrel of the cylindrical cell had stable potential measurements for 80 cycles and at different C-rates and had minimal impact on the full cell performance.<b></b></p>

Increase in the awareness environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed and SOC trajectory, as 50 element time variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for given drive cycle. Observations of the trade-off is discussed in the case of Multi-Objective Optimizations.

<p>Recent developments in semiconductor device technology have seen the advent of wide-bandgap (WBG) based devices that enable operation at high switching frequencies. These devices, such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs), are becoming a favored choice in inverters for electric drive systems because of their lower switching losses and higher allowable operating temperature. However, the fast switching of such devices implies increased voltage edge rates (high <i>dv/dt</i>) that give rise to various undesirable effects including large common-mode currents, electromagnetic interference, transient overvoltages, insulation failure due to the overvoltages, and bearing failures due to</p> <p>microarcs. With increased use of these devices in transportation and industrial applications, it is imperative that accurate models and efficient simulation tools, which can predict these high-frequency effects and accompanying system losses, be established. This research initially focuses on establishing an accurate wideband model of a surface-mount permanent-magnet</p> <p>ac machine supplied by a WBG-based inverter. A new multirate simulation framework for predicting the transient behavior and estimating the power losses is then set forth. In this approach,</p> <p>the wideband model is separated into high- and low-frequency models implemented using two different computer programs that are best suited for the respective time scales. Repetitive execution of the high-frequency model yields look-up tables for the switching losses in the semiconductors, electric machine, and interconnecting cable. These look-up tables are then incorporated into the low-frequency model that establishes the conduction</p> <p>losses. This method is applied to a WBG-based electric drive comprised of a SiC inverter and permanent-magnet ac machine. Comparisons of measured and simulated transients are provided.</p>

<p>CO₂ concentration in the atmosphere poses a great threat to life on earth as we know it. The reduction of CO₂ concentration is key to avoid the critical turning point of 1.5^oC temperature increase highlighted by Intergovernmental Panel on Climate Change (IPCC). Gasification using CO₂ as reacting agent can potentially reduce the CO₂ concentration in the atmosphere. Naturally, biomass such as corn, uses great amounts of CO₂ for photosynthesis and produces O₂; hence, energy and fuel production using biomass can potentially be classified as carbon neutral. Moreover, if CO₂ is used as the gasifying agent, gasification can effectively be carbon-negative and collaborate to the reduction of CO2 in the atmosphere.</p> <p>The major setback of using CO₂ biomass gasification is the energy-intensive reaction between C + CO₂ -> 2CO. This reaction at atmospheric pressure and room temperature is heavily tilted towards producing char and CO2. The current investigation describes efforts to favor the right hand side of the reaction by using simple impregnation techniques and cost-effective catalysts to reduce the energy requirements of the reaction. Also, parameters such as pressure are explored to tilt the balance towards the production of CO. Corn stover is selected as the biomass for the present research due to its wide use and availability in the US.</p> <p>The results show that by using catalysts such as iron nitrate and sodium aluminate, the temperature required to achieve substantial char conversion is reduced. Also, increasing the pressure of the reactor, the temperature can be substantially decreased by 100 K and 150 K. The structure and chemical composition of the chars is studied to explain the differences in the reaction rate between chars. Further, chemical kinetics are calculated to compare the present work with previous work in the literature. Finally, data-driven analysis of the gasification data is explored. The appendices provide supplementary information on the application of deep learning to CO₂ recycling using turbulent flames and efforts to reduce the flame spread rate over a pool of Jet A by using Multi Walled Carbon Nanotubes (MWCNTS).</p>

In this research, an attempt is made to evaluate alternative model-predictive microgrid control approaches and to understand the trade-offs that emerge between model complexity and the ability to achieve real-time optimized system performance. Three alternative controllers are considered and their computational and optimization performance compared. In the first, nonlinearities of the generators are included within the optimization. Subsequently, an approach is considered wherein alternative (non-traditional) states and inputs of generators are used which enables one to leverage linear models with the model predictive control (MPC). Nonlinearities are represented outside the control in maps between MPC inputs and the physical inputs. Third, a recently proposed linearized trajectory (LTMPC) is considered. Finally, the performance of the controllers is examined utilizing alternative models of the synchronous machine that have been proposed for power system analysis.

<p>As energy demands for the world increase, the necessity for alternate sources of energy are critical. Just in the United States alone, 92 quadrillion British thermal units (Btu) were used in 2020. As political and geographical pressures surrounding oil increase, along with the growing concern for climate, the drive to explore alternative and renewable means for harvesting energy is on the rise. Solar cells, also known as photovoltaics (PVs), are an attractive renewable source and have been developed as an alternative energy means for over 60 years. When considering losses due to atmospheric absorption and scattering, the Earth’s surface gets about 1000 W/m2 of energy from the sun, which is why there are research efforts around the world trying to maximize the efficiency of solar cells.</p> <p>Organic-inorganic halide perovskites provide for ideal absorbing layers that feature long carrier lifetime and diffusion lengths, strong photoluminescence, and promising tunability. Furthermore, the solution-processing methods used to make these perovskites ensure that the solar cells will remain low-cost and have easy scale-up possibilities. The main problem perovskites is that they degrade in the presence of water, thus leading to decreased device performance.</p> <p>In this work two approaches are investigated to increase moisture stability. The first investigates incorporation of thiols as pseudohalides into the 2D perovskite structure. Instead of the theorized perovskite, two novel 2D compounds were created, Pb₂X(S-C₆H₅)₃ (X= I, Br, Cl) and PbI_1.524(S-C₆H₅)_0.476. While not perovskites, this study gives insight into the effect that the thiol may have on determining structure when comparing –S-C₆H₅ with –SCN groups. Future work will explore more electronegative thiols that will be used to make moisture resistant, tunable 2D perovskites.</p> <p>The second approach is to incorporate longer organic ammonium cations into the perovskite structure to produce quasi-2D perovskite films fabricate them into devices. Adding in electronically insulating ligands leads to a stricter requirement for vertically aligned 2D films and special care must be taken to have efficient charge collection. The current field has successfully incorporated short ligands such as butylammonium (BA) into PVs, however the extension to larger and more beneficially hydrophobic ligands has been very scarce. In this work, a novel solvent engineering system is developed to create vertically aligned quasi-2D perovskite absorbing layers based off of a bithiophene ligand (2T). These absorbing layers are then characterized and incorporated into efficient PV devices. Generalizations to solvent conditions related to ligand choice is discussed herein, creating deep insights into incorporating more conjugated ligands into devices.</p>

<p>The purpose of this study is to develop a flexible computer tool to predict the power produced by a photovoltaic (PV) panel. The performance of the PV panel is dependent on the incident solar radiation and the cell temperature. The computer tool predicts voltage-current curves, power-voltage curves, and maximum power point values. Five different models are implemented to predict the temperature of the panel, and comparison between the different thermal models is good. A thermal capacitance approach that uses a simple relationship for the forced convection heat transfer coefficient is used to predict the cell temperature. Both the electrical and temperature models are verified through comparisons using PVWatts and validated by comparisons to measured values. The model is flexible in the sense that it can be applied to PV arrays of any size, at any location, and of different cell types. After being verified and validated, the model is used to investigate the effects of cooling on the photovoltaic panel to improve the panel efficiency and increase its power output. Typical results show that for every degree Celsius rise in temperature, the efficiency of the solar panel is reduced by 0.5%. The effect of cooling and the resulting increase in energy production in two different climatic zones are studied and discussed. </p>

The pandemic scenario caused by COVID-19 is an event with no precedent. Therefore, it<br>is a phenomenon that can be studied to observe how electricity loads have changed during the stayat-home order weeks. The data collection process was done through online surveys and using<br>publicly available data. This study is focusing on analyzing household energy units such as<br>appliances, HVAC, lighting systems. However, collecting this data is expensive and timeconsuming since dwellings would have to be studied individually. As a solution, previous studies<br>have shown success in characterizing residential electricity using surveys with stochastic models.<br>This characterized electricity consumption data allows the researchers to generate a predictive<br>model, make a regression and understand the data. In that way, the data collection process will not<br>be as costly as installing measuring instruments or smart meters. The input data will be the<br>behavioral characteristics of each participant; meanwhile, the output of the analysis will be the<br>estimated electricity consumption "kWh." After generating the "kWh" target, a sensitivity analysis<br>will be done to observe the electricity consumption through time and examine how people evolved<br>their load during and after the stay-at-home order.<br>This research can help understand the change in electricity consumption of people who<br>worked at home during the pandemic and generate energy indicators and costs such as home office<br>electricity cost kWh/year. In addition to utilities and energy, managers can benefit from having a<br>clear understanding of domestic consumers during emergency scenarios as pandemics. <br>

<div>In this paper various applications of axial tensile load, bending load, and rolling loading has been applied to a Copper Indium Gallium Diselenide (CIGS) Solar Cell to lean how it would affect the solar cell parameters of: Open circuit voltage (Voc), Short circuit current, (Isc), Maximum power (Pmax), and Efficiency (EFF), and Fill Factor (FF). These Relationships were found for with three different experiments. The first experiment the applies axial tensile stress is to a CIGS solar cell ranging from 0 to 200 psi with various strain rates: 0.0001, 0.001, 0.01, and 0.1 in/sec as well as various relaxation time: 1min, 5min, and 10 min while the performance of solar cell is measured. The results of this gave several trends couple pertaining the Voc . The first is that open circuit voltage increases slightly with increasing stress. The second is the rate of increase (the slope) increases with longer relaxation times. The second set of trend pertains to the Isc. The first is that short circuit current generally is larger with larger stress. The second is there seems to be a general increase in the Isc up to a given threshold of stress. After that threshold the Isc seems to decrease. The threshold stress varies depending on strain rate and relaxation time. The second set of experiments consisted of holding a CIGS solar cell in a fixed curved position while it was in operational use. The radii of the curved cells were: 0.41, 0.20, 0.16, 0.13, 0.11, 0.094, and 0.082 m. The radii were performed for both concave and convex cell curvature. The trends for this show a slight decrease in all cell parameters with decreasing radii, the exception being Voc which is not effecting, the convex curvature causing a slightly faster decrease than the concave. This set of experiments were also processed to find the trends of the single diode model parameters of series resistance (Rs), shunt resistance (Rsh), dark current (I0), and saturation current (IL), which agreed with the experimental results. The second experiment consisted of rolling a CIGS solar cell in tensile (cells towards dowel.) and compression (cells away from dowel) around a dowel to create internal damage. The diameter of the dowels decreased. The dowel diameters were: 2. 1.75, 1.25, 1, 0.75, 0.5, and 0.25 inches. This experiment showed similar trends as the bending one but also had a critical diameter of 1.75 in where beyond that damage much greater. Finally a parametric study was done in COMSOL Multiphysics® to examine how changes in the CIGS material properties of electron mobility (EM), electron life time, (EL), hole mobility 15 (HM), and Hole life time (HL) effect the cell parameters. The trends are of an exponential manner that converges to a given value as the material properties increase. When EL, EM, HL are very small, on the order of 10-4 times smaller than their accepted values, a transient like responses occurs.<br></div>