Search

Purpose The paper aims to develop a particle matter (PM2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM2.5 require either time-consuming or expensive equipment, thus making PM2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices. Design/methodology/approach This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources. Findings The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data. Originality/value This work demonstrates that by combining low-cost sensors and ML, indoor PM2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

The capacitor–inductor–inductor–capacitor ( CLLC ) converter is a promising topology for bidirectional power conversion applications, such as hydrogen or battery energy storage systems and bidirectional pulsing current charging or heating for electric vehicle (EV) batteries. For these applications, high dynamic constant current control is required. In this article, a novel constant current control method with improved dynamic performance over the conventional proportional–integral (PI) method is proposed for the CLLC converter. In constant current control, the charging and discharging process of the output capacitor determines the dynamic performance. A state trajectory model is proposed for the CLLC converter and is employed to analyze the transient process in the resonant tank. Based on the analysis, a novel dead-band-based control method for the constant current control is proposed. The proposed method can directly and effectively control the charging and the discharging process of the output capacitor, which the conventional PI control method cannot do. As a result, the dynamic performance is improved. The correctness of the proposed state trajectory model, the state trajectory analysis, and the effectiveness of the proposed constant current control method are verified by experiments. Experimental results show that the response time of the proposed method is reduced by more than 50% compared with that of the conventional PI method, with no overshoot.

While it is challenging to simultaneously achieve both high mechanical performance and self-healing ability within one polymer hydrogel network, we, herein, synthesized a novel class of hydrogels based on a combination of chemical and dual non-covalent crosslinks

This study aimed to construct an endogenous competition network for cervical squamous intraepithelial lesions using differential gene screening.GSE149763 was used to screen differentially expressed long non-coding RNAs (lncRNAs) and mRNAs to predict correlated microRNAs (miRNAs). The correlated miRNAs and GSE105409 were used to screen differentially expressed miRNAs for differential co-expression analysis, and the co-expressed differentially expressed miRNAs were used to predict correlated mRNAs. Differentially expressed mRNAs, miRNAs, and lncRNAs were visualized, and differential gene screening, enrichment, and pathway analysis were performed.The ceRNA network of cervical squamous intraepithelial was successfully established and a potential differentially expressed network was identified. The key genes were VEGFA and FOS, and the key pathway was the MAPK signaling pathway.The differential expression and potential effects of the lncRNA BACH1-IT1/miR-140-5p/VEGFA axis, key genes, VEGFA and FOS, and MAPK signaling in CIN were clarified, and the occurrence and potential effects of CIN were further clarified. The underlying molecular mechanism provides a certain degree of reference for subsequent treatments and experimental research.

This article presents a multilevel energy management framework for dc microgrids with multiple energy storage systems (ESSs) to ensure reliable power dispatch, stable voltage regulation, and daily economic operation. In the primary layer, an advanced virtual-battery drooped control is constructed for the ESSs, which can realize adaptive load sharing and state of charge (SOC) balancing. Decentralized bus-signaling control is proposed to prevent single point of failure, where the mode-switching voltages of the utility grid and photovoltaic system are adjustable based on the VirtualOCV curve of the virtual-battery model. A current feedforward control strategy is scheduled as the secondary control to reduce bus voltage deviation, which is verified through simulation. A small-signal model is developed to investigate the effects of the virtual-battery parameters, SOC value, and constant power load on system stability through root locus analysis. In the energy management level, an improved particle swarm optimization algorithm is applied to obtain the optimal setting of the virtual-battery model parameters for minimum daily operating costs. A discrete model is derived from calculating the capacity loss of the batteries. Finally, the proposed control hierarchy is verified in the MATLAB/Simulink environment and the simulation results prove the feasibility and priority of the proposed multilevel energy management.

Introducing double physical crosslinking reagents (i.e., a hydrophobic monomer micelle and the LAPONITE® XLG nano-clay) into the copolymerization reaction of hydrophilic monomers of N,N-dimethylacrylamide (DMAA) and acrylamide (AM) is reported here by a thermally induced free-radical polymerization method, resulting in a highly tough and rapid self-healing dual-physical crosslinking poly(DMAA-co-AM) hydrogel. The mechanical and self-healing properties can be finely tuned by varying the weight ratio of nanoclay to DMAA. The tensile strength and elongation at break of the resulting nanocomposite hydrogel can be modulated in the range of 7.5–60 kPa and 1630–3000%, respectively. Notably, such a tough hydrogel also exhibits fast self-healing properties, e.g., its self-healing rate reaches 48% and 80% within 2 and 24 h, respectively.

Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM2.5 was 1.73 μg/m3 for the LSTM model and in the range of 2.20-4.71 μg/m3 for the other models when regulatory ambient PM2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM2.5 information, the developed models still demonstrated relatively high skill in predicting the PM2.5 levels in well-mixed indoor air.

In this paper, a novel constant current control method, with improved dynamic performance over the conventional PI method, is proposed for the CLLC converter. We proposed a state trajectory model for the CLLC converter. Based on the proposed the state trajectory analysis, a novel dead-band based control method with improved dynamic performance is proposed. The effectiveness of the proposed method was verified by experimental results. The response time was hugely reduced (less than 50% of the PI method) and no overshoot was detected.

This paper focuses on the selection of matching areas in the gravity-aided inertial navigation system. Firstly, the Sobel operator was used in convolution of the gravity anomaly map to obtain the feature map. The convolution slope parameters were constructed by combining the feature map and the gravity anomaly map. The characteristic parameters, such as the difference between convolution rows and columns, convolution variance of the feature map, the pooling difference, and range of the gravity anomaly map, were combined. Based on the support vector machine algorithm, the convolution slope parameter-support vector machine combined method is proposed. Second, we selected the appropriate training sample set and set parameters to verify. The results show that compared with the pre-calibration results, the classification accuracy of the test set is more than 92%, which proves that the convolution slope parameter-support vector machine combined method can effectively distinguish between the suitable and the unsuitable area. Thirdly, we applied this method to another region. The navigation experiment was performed in the split-matching area. The average positioning error was better than 100 m, and the correct rate was more than 90%. The results show that sailing in the selected area can accurately match the trajectory and reduce the positioning error.

The health assessment for energy storage batteries matters in the context of carbon neutrality. Dual closed-loops capacity framework integrated with a reduced-order electrochemical model including triple side reactions is put forward, realizing parameter correction for health evaluation. Simplified microgrid aging experiment is formulated to test the closed-loop matching between the aging mechanism and electrochemical model relying on incremental capacity analysis. In addition, taking into account the future degradation prediction for energy storage system, the reliable capacity output afterwards acts as references for closed-loop parameter updating in empirical model to predict degradation evolution. The framework proposed implements the closed-loop dynamic updating for aging parameters with ideal error within 2%, making up for the lack of aging mechanism interpretation of accustomed empirical or data-driven black box model in the field of energy storage batteries.

Introducing double physical crosslinking reagents (

This paper presents a DC microgrid semi-decentralized control strategy for electric vehicle fast charging and hydrogen production. The equivalent circuit models of the photovoltaics (PV), battery energy storage system and the proton exchange membrane water electrolyzer (PEMWE) are built and integrated into an equivalent bus capacity model of the microgrid, where both model accuracy and computation efficiency are promised. The microgrid, excepted for the PEMWE, is always maintained under the decentralized mode, which is based on the Virtual battery model and Bus-signaling control. The control of the PEMWE is divided into the decentralized mode and power-based mode, the mode choice of which is determined by the PEMWE controller according to time period when the price of the grid is the lowest of the day. The PEMWE could utilize the extra energy generated by the PV array to stabilize the bus voltage, the daily production target could also be satisfied. The proposed semi- empirical approach is verified in the MATLAB/Simulink environment and the feasibility and priority of which is proved.

Optimizing the size of photovoltaic (PV) and energy storage system (ESS) is a key issue to ensure reliable, resilient and economic operation of the microgrid (MG). However, multiplicity of electric vehicle (EV) load profiles and computational complexity of the optimizing models limit the application of existing methods upon restricted scenarios. In this paper, a novel framework combining physical-economical model (PEM) and data-driven model (DDM) is established. First, a data augmentation approach based on reversed Monte Carlo (RMC) is proposed to generate comprehensive synthetic EV load datasets for big data training. Subsequently, a PEM is given to determine the capacity configuration of PV and ESS under each load condition, with the objective to obtain the best financial profitability. The model takes into consideration battery degradation and optimal power dispatch. Finally, a DDM is trained with machine learning techniques to relate optimal sizes with input of EV profiles and MG features. Testing results illustrate that in the test sets, the neural network could predict the optimal configuration within 15% and 20% relative error, respectively, enormously narrowing design space of PEM, and thus produces over 88% runtime savings while maintaining high fidelity and optimality.

Accurate battery energy storage system (BESS) state of health estimation is important to microgrid optimization and energy management. This paper presents a coupled optimization-oriented reduced-order aging model for BESS under microgrid operating conditions. Three dominant physical degradation reactions, namely solid electrolyte interface growth, loss of active material and lithium plating are derived from the single particle model, where the three side reactions are coupled with each other. Five accelerated aging experiments are designed and conducted with various charging-rate and state of charge range to approximate the microgrid conditions. The proposed aging model is calibrated against the experimental data while an excellent fitting result is realized with most of relative errors less than 1%, indicating the accuracy of the reduced-order aging model. The contribution of the three side reactions on total capacity loss is consistent with the incremental capacity analysis, which proves the physical validity of the proposed model.

Although the exposure to PM

This paper presents a novel dual-layer framework to find the most economic photovoltaic and battery capacity combination of a grid-connected DC microgrid. The system cost during the whole lifetime is addressed in the optimization framework through the net present cost based objective function, where the investment cost, battery replacement cost and operating and management cost are taken into account. The proposed CAPN model, which combines all of the battery ageing stressing factors, is incorporated to estimate the replacement number of the battery. A particle swarm optimization algorithm, which is initialized with a rule-based power dispatch strategy, is put forward in the energy management level to achieve the optimal power dispatch. In order to adapt to variant operating conditions, the ambient and electric vehicle charging profiles throughout a whole year are employed as the input conditions of the optimization problem. A 61.8k RMB reduction is realized with the optimal configuration under the proposed method compared with the rule-based energy management strategy, indicating the superior of the proposed optimization methodology. Besides, the optimal power dispatch is also implemented with the extra power fed back to the utility grid.

A case study was undertaken on one floor of a multi-floor office building in Seattle, WA. Its aim was to offer a straight-forward example for facilities managers, administrators, and researchers alike wishing to perform systematic, naturalistic, mixed-methods research in office spaces that have recently been retrofitted. Changes were made to the floor’s layout, and to the size of employees’ workspaces. New sound-making technology and a modern lighting framework were added. Objective measurements of lighting, acoustics, and indoor air quality were taken and an online questionnaire was distributed to staff to afford subjective measurements of their perceptions about the previous and new open-plan settings. Items concerning satisfaction with workspace layout, size, lighting, acoustics, air quality, and level of input into the retrofit process were asked. After the new space had been used for 1.5 months, occupants reported being more satisfied, in general, than they recalled being in the original setting. The size of personal workspaces and a sense of privacy were especially important to employees. Despite overhead lighting illuminance levels being below recommended industry standards, occupants were not dissatisfied with light levels. The sound masking system was iteratively commissioned based on negative occupant feedback, resulting in purposely setting some areas to exceed or fall short of acoustical performance guidelines; indoor air quality remained unchanged. Differences in quantitative and qualitative findings highlight the importance of gathering self-reported information from occupants in several ways and exploring them carefully to better understand why environmental satisfaction (or dissatisfaction) exists. Employees’ sense of environmental control remained a prominent theme in the data, supporting existing studies in the field of environmental psychology. While perceptions of control did not improve after the retrofit, occupants’ responses about the level of input they had into the retrofit process correlated significantly and positively with their perceptions of environmental satisfaction after its completion. The nuanced findings from this case study’s customized approach to measuring objective environmental stimuli, along with occupants’ environmental perceptions, add to a growing body of literature merging social scientific methodologies with technical environmental assessments for practical use by decision-makers working to satisfy employee preferences.

The installation of rooftop photovoltaic (PV) arrays is increasing throughout the US. Until recently, pedestal type PV framing systems for rooftops were basically designed using procedures from the ASCE7-10 Components and Cladding Standard for rooftop equipment. The 2011 Japanese Standard Load design guide on structures for photovoltaic arrays was useful in characterizing the pressure coefficients on rooftops, but the Standard employs different wind speed and importance factors, making its use in the US quite limited. Even the updated 2017 version is written for a different audience. Because rooftop pressure loadings are high due to flow separation, SEAOC and other organizations contracted boundary layer wind tunnel tests of panels attached to rooftops to ascertain if the ASCE7-10 equipment loadings were appropriate. The investigations resulted in new standards for pedestal-style arrays that appear in Chapter 29 of ASCE7-16. However, the new standards are limited to simple geometries and orientations, and the dynamics of the simply-supported thin PV plates do not appear to be considered. Questions regarding the ability of the boundary tunnels to simulate accurately the turbulence at the scale required for the attached panels have been raised. In response, very limited full-scale investigations in large-scale tunnels and in situ have been undertaken to calibrate the tunnel results. The results of this paper represent one of these calibration investigations. Specifically, in situ full-scale net wind pressure loadings on a rooftop PV array in a pedestal-style framing system located on the three story Hogue Technology Building of Central Washington University (CWU) in Ellensburg, Washington were measured. The CWU campus has a rural setting in a region with steady winds: Ellensburg is located in the Kittitas Breezeway portion of the Northwest wind power region. Indeed, the Wild Horse Wind and Solar Farm is located on the outskirts of town. The data described here were collected from April through August 2014. The measured net pressure coefficient time series were similar to those for rooftop pressure loadings for low-rise buildings described in the literature such as the Wind Engineering Research Field Laboratory at Texas Tech University (Ham and Bienkiewicz, 1998 [1] ; Levitan and Mehta, 1992 [2] ). The analysis of the net pressure time series data included an examination of the minimum, maximum, mean, and RMS values. Preliminary results suggest that the range of the values is larger than assumed in the ASCE7 Standard, and that the magnitude of the loadings vary considerably spatially over the multiple panel array. The pressure loading measurements are ongoing.

With the progress of renewable energy technologies, distributed energy system (DES) has become attractive due to its flexibility and interaction with power systems. Battery energy storage system (BESS) is an indispensable part of DESs, the control strategies of which have a great influence on system performance. In this paper, we present a novel adaptive droop control (ADC) for energy storage batteries. The state and model parameters of energy storage batteries are estimated simultaneously online by dual extended Kalman filter (DEKF) algorithm. Then the droop controller is designed based on the battery parameters by the “virtual battery” algorithm, benefit from which power can be distributed among battery packs adaptively for their power characteristics, meanwhile SOC balancing can be achieved automatically. The proposed control strategy is validated in MATLAB/Simulink environment with a direct-current (DC) microgrid model where battery packs are connected to the DC bus through dual active bridge (DAB) converters. Compared with conventional droop controls, the proposed ADC method considers the battery characteristics and battery aging, and it is more suitable for DES with multiple sets of batteries.

Plug load monitoring and associated occupant behavior interventions can play a critical role in reducing commercial building energy consumption. This study investigates whether the reduction in building energy consumption justify the added cost of plug load monitoring and occupant energy saving interventions. The objective of this study is to conduct deterministic and probabilistic return-on-investment (ROI) analysis of instrumenting workspaces, monitoring plug load usage, and applying interventions to promote building energy reduction. The study uses the findings of actual occupant energy saving intervention investigations conducted with city and federal government offices in which the association between occupant energy savings interventions and energy use risk was evaluated. While the deterministic approach led to a positive net present value, the interventions failed to recapture the initial investment, and operational expenses given the uncertainties in the estimate of costs and energy use. The mean ten-year net present value was −$3914 at a 6% discount rate considering all U.S. states. From the project manager’s perspective, other non-energy benefits can justify the additional resources.

Healthy building design guides are cogent and necessary. While elements that contribute to healthy buildings are multifactorial, the perception of sound versus noise is subjective and difficult to operationalize. To inform the commissioning process, the acoustics in an open office was examined following the first international building certification system that focuses on the well-being of occupants. Results highlight the role facility managers play in ensuring acoustical quality and offer suggestions to optimize healthy building rating systems. Mixed empirical evidence concerning the advantages of open office designs exists, as does evidence that noise, and a lack of privacy, affects workers&rsquo, levels of distraction and dissatisfaction. Sound masking systems can lower stress levels and augment performance. However, the sound produced by these systems can also be disruptive, conflicting information exists for facility managers to use when making decisions. The results suggest that, although objective measurements and healthy building guidelines for designing satisfactory indoor acoustic environments are important, changes to the physical environment, and acoustical systems, in particular, require iterative subjective assessments within the retrofit process to bolster occupant satisfaction. Mixed-methodologies used in this study may aid facilities managers in capturing and interpreting occupant data about physical stimuli in the workplace and improving the commissioning process.

In moderate climates, the operation of windows is the most common way to control for thermal comfort. Window-opening behavior (WOB) is a complex process influenced by multiple factors, yet only simple bi-variate analyses between variables obtained from longitudinal datasets have been examined. The goal of this study is to investigate the effects of indoor and outdoor environmental parameters on WOB using a statistical modeling approach called &ldquo, structural equation modeling.&rdquo, The results show that the indoor environmental parameters, such as operative temperature and air velocity, mediated the relationship between the outdoor environmental parameters, such as outdoor air temperature and wind gust, and the WOB. The indoor wet-bulb globe temperature rose as the solar radiation increased, and subsequently, both parameters affected the WOB. Also, an increase in outdoor wind gust led to higher indoor air velocity, which in turn resulted in a lower chance of occupants opening the window. By enhancing our understanding of the relationship between these theoretical parameters, improved design strategies on the mediating parameters can be prioritized and communicated early in the building design phase leading to more informed design decisions.

Thermal management system (TMS) plays an essential part in improving the safety and durability of the battery pack. Prior studies mainly focused on controlling the maximum temperature and temperature difference of the battery pack. Little attention has been paid to the influence of the TMS on thermal runaway (TR) prevention of battery packs. In this paper, a heat pipe-based thermal management system (HPTMS) is designed and investigated to illustrate both the capabilities of temperature controlling and TR propagation preventing. Good thermal performance could be achieved under discharge and charge cycles of both 2 C rate and 3 C rate while the equivalent heat dissipation coefficient of the HPTMS is calculated above 70 W/(m2·K). In the TR propagation test triggered by overcharge, the surface temperature of the battery adjacent to the overcharged cell can be controlled below 215 °C, the onset temperature of TR obtained by the adiabatic TR test of a single cell. Therefore, TR propagation is prevented due to the high heat dissipation of the HPTMS. To conclude, the proposed HPTMS is an effective solution for the battery pack to maintain the operating temperature and improve the safety level under abuse conditions.

Microgrid-based electric vehicle (EV) fast charging station is believed to be a promising solution to lessen the enormous charging burden of large-scale EVs on the main grid, while the optimal charging protocol in microgrids is still missing. Thus, this paper presents a model-based continuous differentiable charging (CDC) approach for EV fasting charging in microgrids. The priority of the proposed method on bus voltage regulation over the conventional multi-stage constant current (MCC) strategy is first validated. The parameters of the CDC profile are obtained through the model-based particle swarm optimization framework. An electrothermal-coupled equivalent circuit model is adopted as the performance model while a physical-based semi-empirical battery degradation model is built to measure the capacity loss and lithium plating rate. The objective function of the optimization is to reduce the charging time, capacity fading, and bus voltage disturbance, with the constraints of preventing lithium deposition and limiting the maximum voltage and temperature. The optimization results of the CDC and MCC methods confirm that CDC protocol can reduce the charging time by about 33.5% without scarifying battery health. The sensitivity analyses of initial state of charge, ambient temperature, and heat dissipation coefficient further suggest the universality of the proposed CDC strategy.

Fragility functions are used to represent the probability of failure of a structure or lifeline system conditional upon a hazard or set of hazards and are essential in the performance-based design process. Continuous lognormal damage fragilities are traditional, but recent formulations have implemented logit transformations from the family of generalized linear models for categorical data with a binary outcome (e.g., failure, no failure). In wind engineering, single hazard parameters derived from correlated variables (e.g., integrated kinetic energy, IKE) have been employed to indirectly include the effect of more than one hazard variable; however, even with more general hazard metrics, the lognormal formulation is still unduly restrictive for realistic fragility modeling. Using a statistical approach based on a logit formulation, a shift towards more robust fragility functions can be achieved. Because of its simplicity and ability to represent multiple predictor variables to improve the fitted model, this paper proposes use of the logit formulation of the fragility function at the system level for two or more simultaneous weather hazards. Successful applications of the model to characterize lifeline system-level fragility functions for electric power delivery during Hurricane Isaac in Louisiana and Hurricane Sandy in New York City using in-situ damage and hazard data are shown. While the results here are empirically derived, the modeling approach may be expanded for other structural systems subject to multiple loadings or demand variables.

Battery degradation cost is one of the major concerns when designing energy management strategies of DC microgrids. However, many battery degradation models used in the previous works are over-simplified and the effectiveness of which has not been verified. As a result, this paper presents a comparative study of the impact of battery aging models on energy management of the microgrid. Four popular single factor-based semi-empirical models are investigated while a combined factor-based Combined Arrhenius-Peukert-NREL (CAPN) model is proposed with the best fitting performance compared with the experimental data. The five degradation models are considered as part of the objective function in the particle swarm optimization-based energy management structure of a grid-connect microgrid. The optimized power scheduling and state of charge trajectory of the battery under different single factor-based models exhibit enormous deviations, so as the calculated total costs, which have the maximum error of 63.9% compared with the CAPN model. The application of the studied single factor-based models will also result in 3.5%–12.5% additional actual operating cost under non-optimal conditions. This paper first reveals the significant and unneglectable influence of the simplified degradation models on the microgrid energy management, the abandon of the single factor-based models is also recommended.

DC microgrid is supposed to be a feasible solution to reduce the negative impact of electric vehicle (EV) fast charging on the electric grid and improve the penetration of photovoltaics (PV) generation. In this paper, an improved decentralized Virtual-battery based droop control with the capability of bus voltage maintenance, load power dispatch and SOC balance of the energy storage system (ESS) is proposed to ensure the autonomous and stable operation of the DC microgrid. The reference output voltage and virtual resistance in the droop control loop are altered dynamically based on the Virtual-battery model of the ESS. The coordinated control among the PV-ESS-Grid integrated system is realized through the primary Bus-Signaling control, where the reference voltages at which the control modes of the PV array and the grid are switched are designed based on the VirtualOCV of the ESS. The effectiveness of the proposed control strategy is validated in MATLAB/Simulink environment with an equivalent bus capacitance-based model where the EV charging profile is obtained from real-world charging data of a fast charging station. The merits of the control strategy including higher PV utilization, less frequent connection of the grid and more precise voltage tracking are highlighted in comparison with the conventional droop control strategy. Finally, the sizing of the ESSs is optimized based on the total cost of the DC microgrid, including the daily electricity cost purchased from the grid and the depreciation cost of the ESSs based on the expanded capacity degradation model of Li-ion batteries.

Today, new lighting technologies are rapidly changing energy savings goals and the ways in which those savings are identified and captured. As occupant expectations concerning indoor comfort and well-being are transforming lighting design and engineering solutions, research concerning systems that afford more interaction between occupants, overhead lighting, and local (task) lighting is prudent. This study builds a value proposition by determining risk-based energy savings potential of interactive lighting system while evaluating programmatic success through the measurement of occupants’ psychosocial variables. Post-retrofit data supplied by an overhead lighting control system were combined with empirical energy consumption data from individual tunable desk lamps for a probabilistic return on investment (ROI) analysis. Descriptive and correlational analyses were done to link employees’ environmental perceptions with energy consumption. Even for a modestly energy efficient office building that underwent a lighting retrofit in 2011, it is possible to recover the cost of an interactive lighting investment in nine years. Probabilistically, the rebate appears to be the most important factor in increasing ROI. Occupants perceived overall improvement to the lighting environment after desk lamps had been installed, and that a ‘blending’ had been achieved between task and overhead lighting. The paper proposes a generalizable mixed-methodology to evaluate potential savings. Interactive lighting systems are representative of future building systems that require more human interaction and are prone to irregular operational loads . This research may help facility managers find practical solutions based on existing and emerging technologies to leverage readily available data to aid decision-making processes.

Recent disruptions of communities due to natural hazard events such as hurricanes and earthquakes have led to increased calls for improved resiliency of the built environment. The “built environment” denotes constructed facilities such as buildings and bridges, as well as infrastructure systems such as power delivery, transportation roadways, and water utilities. “Resiliency” is defined here as the “recovery and adaptability” during and after events which disrupt civil infrastructure services. In the context of this paper, the critically important service is energy delivery, on which many other services such as communications and transportation networks depend. The robustness of the building energy supply can be significantly enhanced through on-site renewable sources such as photovoltaic panels coupled with storage batteries. The degree to which the energy demand is met by the on-site capacity in the future will be determined largely upon advances in renewable energy generation and storage as well as in efficiency gains for commonly used equipment and appliances such as lighting fixtures and cooling systems. In this paper, we propose an improved design approach for the energy capacity of existing and new buildings as part of a greater regional community in which the total energy capacity requirements are met through increasingly enhanced on-site permanent power links, as opposed to increased reliance on the existing power grid. The metrics for characterizing resiliency will be “robustness,” “redundancy,” “resourcefulness,” and “rapidity,” with the associated metrics for sustainability being self-reliance and intergenerational equity enhancement.

According to NOAA, eleven billion-dollar weather disasters occurred in the United States in 2012. One of the predominant reasons that storms become disasters is the failure of civil infrastructure systems, also known as “lifelines,” on which communities rely. The approach to modeling resilience herein is through the use of systems-based models of performance. The models are based on empirical evidence of the ability of the underlying structural delivery system to provide essential infrastructure services to the community. For example, electric power delivery performance, subject to extreme storms, is sensitive to the combined system of mounted electrical and mechanical equipment, substations, and poles and towers that comprise the distribution and transmission systems. Interdependency is assessed first through previously derived input-output models of recovery to storm hazards and then significantly expanded in this paper. Further, the research results, based upon examination of extensive geocoded...

Most battery packs are required to comply with IP67 standard to guarantee waterproof before commercialization. However, passing the IP67 standard cannot 100% guarantee that the battery pack do not leak when immersed in fluid like dirty water or sea water during the whole life cycle, especially under some extremely conditions. It is reported that, in 2012 several Fisker Karma electric vehicles were burned after sea water intrusion caused by Hurricane Sandy. In 2016, two electric buses were burned into fire after overnight water immersion due to a heavy rain in Nanjing, China. Obviously, it can be very dangerous when water seeps through the cover of battery packs and cause electrical short circuit inside the battery. Therefore, it is critical to study on the phenomena on and the mechanism of water immersion upon battery pack. It is known that the whole battery pack is a complex system. And it is hard to monitor all the phenomena and confirm the repeatability of the experiment if the whole battery pack is thrown into water directly during investigation. Moreover, ruining a whole battery pack cost so much. For these reasons, a substitute experiment was proposed. The two special batteries/modules, which at the highest potential or lowest potential, were considered as the most probably dangerous ones in a battery pack(Figure a). The voltage of the whole battery pack was simulated by a high voltage Digatron battery charger. A single cell was connected with the charger in series. When anode of the single cell is connected to the positive electrode of the charger, it represents that the cell is in the highest potential. Meanwhile the negative electrode of charger is connected with a copper sheet to simulate a full circuit of the battery pack, as shown in Figure b1. Similarly, to simulate the single cell being in the lowest potential, cathode of the single cell was connected to the negative electrode of charger, while the positive electrode of charger was connected with an aluminum sheet as shown in Figure b2. During the experiment, only the tested battery and metal sheet were soaked in salt water with a 3.5 wt% concentration of NaCl to simulate the environment of sea water. In addition, experiments with both electrodes of the charger connected with two metal sheets(Figure b3) or two single cells(Figure b4) were also conducted for references. Pouch cells and prismatic cells were used in the study. During the experiment, we found that when a single cell was immersed into water, electric arc can be observed near the poles of the cell. The poles of prismatic cell fused near the area where the electric arc was triggered. A ball of flames appeared obviously and some other small flames can be seen floated on the water surface. After experiment, the special odor of electrolyte can be smelt above the water, indicating that the electrolyte has leaked from the shell of prismatic battery(Figure c). The cell material and separator were found intact after disassembled. The test of the pouch cell showed similar phenomena of the electric arc, however, no obvious flame appeared on the water surface. For further analysis, dummy cells with only empty shells were used. Electrolyte was injected into the empty battery shells, then similar tests as Figure b2 were conducted. The same experimental phenomena for dummy cells were observed, and the electrolyte was considered the main cause for the flame on water. In conclusion, electric arc containing enormous energy will be released due to high voltage when the battery pack is immersed under water. And the arc hazard is only caused by high voltage, with limited relationship with the cell. The electric arc with large amount of energy will make the poles or the battery shell fuse then lead to battery failure, including electrolyte leakage. The leaked electrolyte can be ignited by electric arc which is one of the most dangerous factors in battery pack water immersion. Acknowledgement: This work was supported by Project U1564205 supported by National Natural Science Foundation of China. Figure 1

During winter months many neighborhoods in the Seattle metropolitan area are heavily affected by particulate matter from residential wood burning. A study was conducted to investigate the relationship between fine particulate matter and pulmonary function in young children. The subjects were 326 elementary school children, including 24 asthmatics, who lived in an area with high particulate concentrations predominantly from residential wood burning. FEV1 and FVC were measured before, during and after the 1988-1989 and 1989-1990 winter heating seasons. Fine particulate matter was assessed using a light-scattering instrument. Analysis of the relationship between light scattering and lung function indicated that an increase in particulate air pollution was associated with a decline in asthmatic children's pulmonary function. FEV1 and FVC in the asthmatic children dropped an average of 34 and 37 ml respectively for each 10(-4) m-1 increase in sigma sp. This sigma sp increase corresponds to an increase in PM2.5 of 20 micrograms/m3. It is concluded that fine particulate matter from wood burning is significantly associated with acute respiratory irritation in young asthmatic children.