As battery technology advances and battery system capacity expands, its penetration in buildings enables building consumers to be energy prosumers, interact with the grid to provide services, and facilitate grid-interactive efficient buildings (GEB). As such, these Battery Energy Storage Systems (BESS) are expected to operate for a prolonged time without significant degradation, be efficient and reliable, meet the changing needs of the building and grid’s requirements, enhance grid resilience, and be fault tolerant. These novel features can be met with the application of Reconfigurable Battery Systems (RBS) technology. However, research and development of RBS integration with the built environment is limited and is the focus of this ongoing research project. This research focuses on developing versatile scalable multi-port switching topology, proposing novel sensors to improve the life span of each battery cell in the RBS system, and applying Reinforcement Learning (RL) to operate the building and its RBS as a dispatchable resource. A literature review, background information, preliminary results, and future work are presented.

Accurate assessment of the state of charge (SoC) and state of health (SoH) of batteries provides critical information for effective operation in various battery applications. Such information optimizes charging, discharging, fault isolation, and maintenance decisions. Conventional techniques have shortcomings, including accuracy, cost, speed, and invasiveness. Recent research has introduced novel techniques, many exploiting magnetic field changes that occur during battery operation. This literature review summarizes conventional and novel measurement methodologies, highlighting any limitations of each. While magnetic field and X-ray approaches can overcome many of the shortcomings of conventional methods, further research is necessary to reduce costs, enhance safety, and enable real-time monitoring. The development of accurate, quick, and cost-effective measurement techniques will accelerate the integration of batteries into a rapidly increasing range of applications, including battery energy storage systems for behind the meter in buildings, facilitating electrification goals and the expansion of renewable energy.

Using data from Omaha, Nebraska, this conference paper examines the aggregate impacts and predictability of residential electric vehicle (EV) charging behavior in a local area. The study highlights patterns in collective charging behavior by examining temporal patterns in total energy demand and simultaneous connections to charging infrastructure. This analysis informs decision-making on energy infrastructure, load management, charge scheduling, and urban planning, contributing valuable knowledge to integrating EVs into existing energy frameworks. The dataset tracks a fixed number of regularly charging users over a year to identify trends scalable to future penetration levels. Both the average demand and variability are analyzed across various timeframes to assess the stability and predictability of residential charging.

Industrial Internet-of-Things (IIoT) technology has been rigorously developed in recent years, moving towards the ambitious goal of industry 4.0, Network Automation. However, there are a few critical challenges regarding the implementation of a reliable IIoT ecosystem for different applications; security, battery life, and bandwidth are controversial challenges. All the challenges regarding IIoT are mainly struggling within the edge computing smaller box of the big picture in which a cloud is an upstream object while sensors and actuators act as downstream devices. Therefore, having a reliable IIoT ecosystem necessitates focusing on the whole IIoT’s challenges in the edge computing smaller box; first realizing the vital, practical demands of a specific IIoT application, and then defining the compatible protocols to form the effective edge computing configuration. This paper reviews four IIoT case study applications with their specific requirements and their counterpart sensor/actuator properties to find the appropriate edge computing protocols satisfying their demands.

Energy optimization in the built environment is receiving more attention in the last decade. This leads to remarkable technological advancements in energy monitoring, data communication, energy storage, control, and data analysis applications. Energy monitoring can be aggregate (Non-Intrusive load monitoring -NILM) or disaggregate (Intrusive load monitoring -ILM) based on the location in which the monitoring takes place. Sensors communicate via wired or wireless means, and several protocols are developed to support communications in smart homes. This real-time data can be sent to remote controllers for further analysis, training and prediction applications using Internet of Things (IoT) that provides a cost-effective solution for energy management systems. This manuscript presents an overview of current innovation in smart homes for the advancement of building technology.

This paper presents a novel machine learning-based algorithm to estimate the frequency offset in wireless communication systems from the received signal's IQ constellations. The algorithm focuses on dividing the received signal symbols into clusters and finding the centroid of each cluster using a modified k-means algorithm. The constellation rotation angle, corresponding to the frequency offset, is then found from the angel between the obtained centroids' coordinates and the coordinates of the corresponding modulation format. The simulation results have shown 100% estimation accuracy for constellation rotation angles within the (-45, 45) degrees range. The proposed algorithm provides a low-complexity scheme that eliminates the overhead required for training preamble (TP) based techniques, thus improving the communication system's efficiency. The algorithm can be applied to wide range of communication systems especially those used in high speed train and vehicular communications.

In the last decade, the significant development of smart building technologies has led to the formation of various energy sensing and monitoring applications. Energy monitoring of appliances relies on techniques such as Intrusive Load Monitoring (ILM) and Non-Intrusive Load Monitoring (NILM). ILM is referred to a technique that a sensor installed for each load. In NILM method, disaggregation of measured energy of all appliances at utility service entry is the main goal to provide a simple and cost-effective method of monitoring the appliances like sequence time domain reflectometry (STDR). This manuscript provides an overview of developments in energy consumption sensing and monitoring in three key areas of Internet of things (IoT), WSN and STDR for the advancement of smart building technology. This paper also provides some research directions for smart home of the future.

Recent trends in the applications of batteries in the built environment are improving the efficiency of batteries and lowering costs. This paper introduces various types of battery technologies such as sodium sulfur, lithium ion, flow and lead acid batteries and discusses their models. Various applications of batteries such as adaptive battery systems, Battery Electrical Vehicles (BEVs), Battery Energy Storage Systems (BESS), the Internet of Things (IoT), and Smart Grid and Smart Environment applications are also discussed. In their selection and use of batteries, scholars are ultimately looking to maximize occupant comfort whilst keeping costs low and optimizing the energy efficiency of buildings. This paper will provide a review of current trends in this field.

Intelligent sensing and actuation in building applications has been considered by many Heating, ventilation and air conditioning (HVAC) experts. Wireless Sensor Network (WSN) performs as an efficient communication tool for human comfort (e.g. indoor thermal control). In this paper, an analysis of Packet Error Rate (PER) in WSN for HVAC system was implemented. Based on the collected PER information, a WSN network was designed and built for achieving low PER. The network provided guidance for large-scale WSN design in HVAC systems.

In this paper, we propose an end-to-end smart electric vehicle charging infrastructure system architecture with an online scheduling scheme. The online scheduler is an important component of the architecture to manage resources at the charging stations. The scheduler operates in two steps. The first step is decentralized and is executed by each vehicle requiring battery recharge. The other step is performed by the central controller, a component of the architecture, to schedule vehicles for recharge based on users' requirements. We provision the scheduler with two plans, namely, plan A and plan B. In plan A, the scheduler tries to admit the requests for an immediate charge. If this plan fails, the scheduler applies plan B where it admits requests for delayed charging within a look-ahead time. The simulation was conducted to test the performance of the scheduler and its algorithm with and without look-ahead scheduling.

The Internet of Things (IOT) continues to transform the world, and in many countries is now an integral part of our everyday lives-influencing everything from the way that we intercommunicate to how we conduct business. Innovators continue to find ways to integrate IOT into uses as far flung as fashion to medicine. This survey looks at how IOT is currently being integrated into the built environment for the purpose of saving energy and improving occupants' livelihoods. In particular, it reviews three technologies that have received a lot of attention in the literature as the future of an IOT mediated built environment. Based on this literature, predictions are made of the likely trends in regards to the future scholarship on these technologies.

A new twelve level three-phase inverter topology based on a computing model that can pre-calculate switching angles is proposed. The main advantage of this topology is that the number of input DC voltage sources, power electronic switches, pre-calculated switching angles, and the total harmonics distortion in the output voltage are all reduced. This means that the topology can be connected to the network with small size inductance. Applications that could use this new topology include the dynamic hybrid battery/inverter and photovoltaic/inverter systems to improve overall system operation and performance and at the same time improve output voltage and power generation quality. The proposed topology has been modeled and validated using Matlab software.

The recent trends in adaptive renewable energy and storage systems for buildings is aiding in improved energy efficiency and costs. Various novel adaptable systems and devices including photovoltaics, batteries and electric vehicles have been proposed in the prior art for the purpose of supporting highly energy sustainable buildings. To aid these systems and to provide additional features, building automation, a trusted technology, is also used to ensure user comfort. Today the challenge is not only to provide comfort, but to do it while having a close eye on the energy performance of the building. Distributed, renewable energy sources, advances in IT and tighter legislative constraints offer and demand changes in the way we operate buildings. This paper will provide a review of current trends in this filed.

Building automation is a trusted technology to ensure user comfort in buildings. Today the challenge is not only to provide comfort, but to do it while having a close eye on the energy performance of the building. Distributed, renewable energy sources, advances in IT and tighter legislative constraints offer and demand changes in the way we operate buildings. We look at the core fields that enable building operation: monitoring of operation data, controls, automation and the management organization to show that innovation can be achieved by integrating cross-domain knowledge and getting inspirations from other domains, where existing approaches and technologies can be used and transferred to the domain of building automation.

The smart grid will increase building energy efficiency and conservation via combining information and communication technologies, advanced instrumentations, system intelligence, and information on the end user. Specifically Demand Side Management (DSM) programs serve as an aid in energy conservation and management strategies as well as in the collection of real-time consumption information data. As proposed in this paper, real-time, fine-grain consumption data at the point of use in buildings can be used by building energy managers, utilities, and the end user for planning, load forecasting, and feedback for providing information that may lead to end user behavior change. This paper focuses on the development of a platform detecting every active node in the building remotely and using a combination of tools to monitor and locate these active nodes. The paper will discuss the main component of this platform, the energy node locator and how it works based on the principles of Time Domain Reflectometry (TDR). Preliminary results will be presented and used to evaluate the node locator principles. Summary and future work will be presented to discuss the framework moving forward.

Engineers are increasingly considering the social dimensions of energy use in the design of energy conservation technologies. This has led to design informed by ethnographic and theoretical inquiry on everyday energy practices and user-technology interactions that will in turn shape future practices and interactions. This paper contributes to the small but growing literature on real-time residential energy feedback displays that more closely examines how users experience such technologies. Survey responses from participants involved in a large-scale residential energy feedback monitoring case study are analyzed and contextualized into a set of positive and negative user “experiences.” The qualitative data derived from these responses add new insight on the social variables of user interaction with real-time feedback monitors that are of use to both the study of energy feedback monitoring and the design of future smart communities. Finally, an example is given of how these participant “experiences” are being put to the test in the design of a new energy feedback technology with implications for future ways of conceptualizing energy.

Although current research focused on sensor deployment to balance the energy consumption among the sensor nodes for prolonging the lifetime of wireless sensor networks, sensor nodes with larger communication radius have to consume more energy to overcome wireless channel fading and path loss. Cooperative transmission allows nodes with single-antenna to cooperate on information transmission and/or reception to achieve a lower average error probability than its non-cooperative counterpart under the same transmit energy budge. In this work, we introduce the concept of cooperative transmission into sensor deployment to balance the energy consumption among sensor nodes. The key idea is to deploy an optimal number of cooperative nodes into a given area. In this way, batteries of all sensor nodes are discharged at the same rate. Extensive simulations have been conducted to evaluate the performance of the proposed scheme. Compared with non-cooperative cases, the proposed scheme with cooperative transmission improves the battery energy efficiency by 52.1%, and prolongs the lifetime of wireless sensor network by three times.

One of the design challenges of wireless sensor networks is the tradeoff between network operation time and network coverage. Recent studies reveal that the useable battery capacity drops faster at a higher discharge current in a nonlinear fashion. To take advantage of this battery current effect, in this paper we explore a new sensor node deployment scheme to prolong the entire sensor network lifetime as well as each individual sensor node. The key idea of the proposed scheme is to assign a sensor node having higher traffic load to adopt lower transmission power level. In this way, batteries of all nodes in a given area are discharged at the same current, thus they are depleted at the same time. Extensive simulations have conducted to evaluate the performance of the proposed sensor node deployment scheme. Compared with peer work on heterogeneous deployment, the useable battery capacity by using the proposed scheme can be improved by 26.67%, and the operating time per sensor node can be enhanced by 20.95%. Furthermore, the proposed deployment scheme can reduce the number of sensor nodes required to cover the given area, leading to a significant reduction of deployment cost.

Battery-powered embedded networked sensing systems become more and more pervasive with the fast-paced deployment of various remote sensing applications. How to prolong the battery operating time is one of the most challenging areas in the design and development of these sensing systems due to the fact that the battery operation is much more dynamic and complex than considered, which is derived from its application and its internal structure (multiple hardwired series/parallel cells) to produce a specific voltage and capacity. Current research on prolonging the battery operating time is mainly focusing on the low-power hardware and energy-efficient network protocol designs, and simply treats the battery as a passive two-terminal energy source. In this paper, we will propose a novel adaptive, proactive, and reconfigurable multicell battery design for supporting power-aware hardware and energy-efficient network protocols for embedded networked systems, which provides a whole new perspective to look at the energy problems of battery- powered embedded networked sensing systems. A theoretical modeling of the proposed design is provided, and simulation results show that the proposed design can significantly enhance the energy performance, especially for low voltage and low discharge current scenarios.