Energy Efficiency Monitoring Research Articles

Optimization strategy of property energy management based on artificial intelligence

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.

Sustainable manufacturing process thermal energy environment detection and green economy development based on image classification

With a growing global focus on sustainability, manufacturing needs to ensure productivity while reducing resource consumption and environmental impact. This paper discusses the thermal energy environment detection method based on image classification in order to improve the energy efficiency monitoring in the manufacturing process and promote the development of green economy. In this study, deep learning technology was used to train convolutional neural network (CNN) to classify thermal environment data, collect thermal data images from multiple manufacturing processes, and label them. The pretreatment technology is used to improve the data quality, and the classification effect is optimized through multi-level network structure. The training and verification process of the model ensures its reliability in practical application scenarios. The experimental results show that the proposed image classification method achieves high accuracy in thermal environment detection, which is obviously superior to the traditional method. Through real-time monitoring of thermal energy status, timely identification of abnormal conditions, so as to provide effective data support for the manufacturing process, and promote the realization of energy conservation and emission reduction goals. Therefore, the thermal environment detection based on image classification provides a new technical path for the sustainable development of the manufacturing industry, and helps enterprises realize the transformation and upgrading of the green economy.

Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems

The energy efficiency identification of machining process plays an indispensable part in achieving energy-efficient manufacturing and improving energy utilization as well as productivity and surface quality. However, there is a great difficulty to track energy efficiency in real-time based on one kind of traditional power signal. Because energy consumption is affected by many factors such as machine tool current performance, tool wear conditions and cutting parameters selection. This paper puts forward an energy efficiency recognition method as well as surface roughness prediction model based on the cutting force signals. The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is employed to decompose the cutting force signal into multiple IMF (intrinsic mode function) components; and characterization of energy efficiency of machining process is recognized through proportion of components based on PCA—Fast ICA algorithm. Then, a surface roughness prediction model is proposed using support vector regression (SVR) based on specific cutting energy consumption (SCEC). The orthogonal test is designed considering spindle speed, feed rate, depth of cutting and width of cutting in 3 levels to obtain the influence degree of cutting parameters on cutting force, specific energy consumption, and the surface roughness. The energy efficiency of 27 group experiments is classified into high, medium and low levels according to energy efficiency value. Finally, using the data of orthogonal test, energy efficiency state was identified. The result show that time–frequency of cutting force signals for high, medium and low energy efficiency could be extracted, and the average absolute error of surface roughness predict is 0.058. That illustrated that the proposed method could meet the industry requirement for energy efficiency monitoring and surface roughness prediction to achieve sustainable manufacturing.

Development of a prototype to evaluate and monitor energy efficiency aiming at power quality 4.0

In research and development projects, the need to implement prototypes to speed up the development and delivery process is paramount, offering tools to validate scenarios and anticipate challenges. The aim of this research is to experimentally develop a prototype for monitoring electrical power systems using a gateway that converts MODBUS-RTU communication protocols to TCP, providing useful information for technicians and managers of a company in the Manaus Industrial Estate (PIM) in the field of reflective tapes. To achieve this goal, it was necessary to specify the technical requirements for panel assembly and communication via MODBUS, acquire and research materials in accordance with the technical specifications and technological requirements, implement the monitoring system with an IMS Controller for reactive power compensation, and configure and parameterise the MODBUS protocol via the RTU to TCP converter. The prototype is expected to be able to take accurate measurements and, in the data acquisition process, obtain readings of electrical quantities such as: voltage per phase; current per phase; power factor per phase; active power per phase; reactive power per phase; apparent power per phase; frequency; voltage THD and voltage harmonics. The prototype must act intelligently to drive the capacitor bank to control and monitor reactive power. The development of the prototype included crucial stages such as the specification of technical requirements, the acquisition of suitable materials, and the implementation and configuration of the system. The specification ensured that the components were aligned with the project objectives, while the procurement of materials ensured the quality and efficiency of the system. The implementation of the Automatic Reactive Power Compensation Controller was vital for reactive power compensation and the configuration of the MODBUS protocol ensured precise communication between the components. To summarise, this project demonstrates the importance of prototypes in technological advancement, offering practical and efficient solutions for industry. The prototype developed to monitor electrical power systems through a MODBUS-RTU to TCP protocol converter gateway represents a significant advance, providing essential data and contributing to informed decision-making, improving the performance and management of the company's energy resources.

SimulatorBridger: System for Monitoring Energy Efficiency of Electric Vehicles in Real-World Traffic Simulations

SimulatorBridger: System for Monitoring Energy Efficiency of Electric Vehicles in Real-World Traffic Simulations

Energy Efficiency Measurement of Mechanical Crushing Based on Non-Contact Identification Method

The efficiency of mechanical crushing is a key metric for evaluating machinery performance. However, traditional contact-based methods for measuring this efficiency are unable to provide real-time data monitoring and can potentially disrupt the production process. In this paper, we introduce a non-contact measurement technique for mechanical crushing efficiency based on deep learning algorithms. This technique utilizes close-range imaging equipment to capture images of crushed particles and employs deeply trained algorithmic programs rooted in symmetrical logical structures to extract statistical data on particle size. Additionally, we establish a relationship between particle size and crushing energy through experimental analysis, enabling the calculation of crushing efficiency data. Taking cement crushing equipment as an example, we apply this non-contact measurement technique to inspect cement particles of different sizes. Using deep learning algorithms, we automatically categorize and summarize the particle size ranges of cement particles. The results demonstrate that the crushing efficiencies of ore crushing particles, raw material crushing particles, and cement crushing particles can respectively reach 80.7%, 70.15%, and 80.27%, which exhibit a high degree of consistency with the rated value of the samples. The method proposed in this paper holds significant importance for energy efficiency monitoring in industries that require mechanical crushing.

A high-fidelity digital twin predictive modeling of air-source heat pump using FCPM-SBLS algorithm

This paper proposes an innovative Digital Twin (DT) model to predict air-source heat pumps' (ASHP) coefficient of performance (COP) using a combination of fuzzy (c + p) means clustering (FCPM) and the stacked broad learning system (SBLS) algorithm (FCPM-SBLS). The SBLS algorithm is adopted to establish the structure of the DT model, considering the imperative of tracking real-time performance changes in ASHP applications. To enhance the fitting and generalization ability of the DT model, FCPM is employed to partition the dataset into subclasses, allowing for the creation of sub-models specifically tailored to each subclass. Moreover, an incremental-full evolution strategy of the DT model is proposed to track changes in ASHP performance. The DT model's validation is conducted using data collected at a railway passenger station. Results demonstrate that the FCPM-SBLS method has commendable predictive performance, with respective mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of 0.045, 0.012, and 0.252. Additionally, the proposed evolution strategy ensures timely updates for tracking ASHP performance changes. Through analysis of the evolution performance of DT model straddling over years, the updated model shows a 63.1% decrease in MAPE compared to the non-updated model. This study is not only conducive to advancing DT modeling in the field of HVAC systems, but also has practical significance for improving the energy efficiency and performance monitoring of ASHP.

Physics-Based Modeling and Parameter Tracing for Industrial Demand-Side Management Applications: A Novel Approach

The transition to sustainable energy sources presents significant challenges for energy distribution and consumption systems. Specifically, the intermittent availability of renewable energy sources and the decreasing usage of fossil fuels pose challenges to energy flexibility and efficiency. An approach to tackle these challenges is demand-side management, aiming to adapt energy consumption and demand. A key requirement for demand-side management is the traceability of the energy flow among individual energy consumers. In recent years, advancements in industrial information and communication technology have provided additional potential for data acquisition. Complementary to acquired data, a physics-based modeling and analysis approach is proposed, which describes the energy consumption with physical parameters. This results in comprehensive options for monitoring actual energy consumption and planning future energy demand supporting energy efficiency and demand-response goals. To validate the proposed approach, a case study with a 3D printer covering approximately 110 h of active printing time is conducted. The medium-term study results indicate a consistent parameter trend over time, suggesting its conceptual suitability for industrial application. The approach helps to monitor energy efficiency among manufacturing assets by identifying peak loads and consumption hotspots, and provides parameters to estimate energy consumption of manufacturing processes. Results indicate up to 50% energy savings when switching the printing material and indicate further potentials.

Enhancing Energy Efficiency and Real-time Monitoring in Industrial Environments through an Integrated Software Solution: NIGHTWATCH

Globally, the demand for energy continues to escalate due to factors such as heating, lighting, transportation, and fuel supply for various devices. In a context where energy has garnered such paramount importance, achieving energy efficiency has necessitated a comprehensive approach encompassing activities related to energy generation, transmission, and consumption. Any positive or negative developments in the realm of energy significantly impact both human and environmental factors, which constitute the focal points of sustainable development. The presented study, operates within industrial settings and interfaces with energy analyzers, collecting and analyzing data such as energy consumption, instantaneous current, and voltage. This initiative seeks to provide real-time visibility into energy-related operations on factory premises. Additionally, the software's capabilities extend to retrospective data analysis, enabling informed insights for future extrapolations. Furthermore, the integration of energy consumption data from the analyzers into the Manufacturing Execution System (MES) facilitates energy tracking on a per-job basis. The software's dashboard component empowers users to establish customized threshold values for monitored energy parameters. When these thresholds are exceeded or when values deviate from expected levels, the software triggers alerts and notifications via email and other communication channels, ensuring timely dissemination of pertinent information. Consequently, the software allows businesses to evaluate unit costs associated with specific job orders. Additionally, the study aims to establish a correlation between energy consumption data at the job-order level and machine-level energy consumption. This correlation could foster the augmentation of efficient energy utilization, thereby enhancing competitiveness and overall efficacy.

Monitoring energy usage of heavy-haul iron ore trains with on-board energy meter for improving energy efficiency

PurposeFor billing purposes, heavy-haul locomotives in Sweden are equipped with on-board energy meters, which can record several parameters, e.g., used energy, regenerated energy, speed and position. Since there is a strong demand for improving energy efficiency in Sweden, data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approachTo monitor energy efficiency, the present study, therefore, develops key performance indicators (KPIs), which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation. Energy meter data of IORE class locomotives, hauling highly uniform 30-tonne axle load trains with 68 wagons, together with additional data sources, are analysed to identify significant parameters for describing driver influence on energy usage.FindingsResults show that driver behaviour varies significantly and has the single largest influence on energy usage. Furthermore, parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions, e.g., axle loads and number of wagons, on energy usage.Originality/valueBased on the parametric studies, some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived. In the end, some possible measures for improving energy performance in heavy-haul operations are given.

Chemiresistive gas sensors: From novel gas-sensing materials to electrode structure

Gas sensors based on chemiresistive technology are attractive for their small size, low-cost fabrication, predictable electrical properties, and compatibility with electronic circuits. They have various applications from health and safety to energy efficiency and emissions monitoring. Despite exploring many gas-sensing materials to detect different gases for the above-mentioned applications, these sensors have limitations such as poor selectivity, high limit of detection, poor reversibility, high operating temperature, and poor stability that restrict their implementation in real-time applications. To address these limitations and improve the sensing performance toward target gases, various approaches have been developed. In this regard, an important aspect to improve the gas-sensing performance is to optimize the device architecture by selecting the appropriate gas-sensing material, electrode material, and electrode structure design. This review discusses the advancements in the novel gas-sensing materials, such as metal-organic frameworks (MOFs), MXenes, graphitic carbon nitride (g-C3N4), hexagonal boron nitride (h-BN), group III–VI semiconductors, phosphorene, black phosphorus, metal ferrites, and high entropy oxides. In addition, this review discusses the impact of various electrode materials, including platinum (Pt), gold (Au), silver (Ag), chromium (Cr), indium tin oxide (ITO), and aluminum (Al), and its electrode structures and design parameters on the gas-sensing performance. The electrode structures covered in this review are head-to-head, interdigitated, fractal, and laser-induced graphene. Finally, this review highlights the summary, challenges, and future perspectives of novel gas-sensing materials, electrode materials, and their structures to improve the gas-sensing performance of chemiresistive sensors.

The Emergence of Smart Sensor Homes is Revolutionising Residential Living

Smart sensor homes, also known as smart homes or connected homes, are residential properties equipped with advanced sensor technologies, connectivity, and automation systems that enable homeowners to control and monitor various aspects of their homes remotely. These homes offer a wide range of benefits, including convenience, energy efficiency, enhanced security, personalized comfort, and remote monitoring capabilities. By leveraging sensors, connectivity, and intelligent systems, smart sensor homes optimize energy consumption, provide real-time information, and enable seamless integration of devices and services within the home. This introductory abstract provides an overview of the concept of smart sensor homes and highlights the key advantages they offer to homeowners. The future prospects of smart sensor homes, including technological advancements and integration with smart city initiatives, are also discussed. However, it is crucial to address security and privacy concerns associated with these homes to ensure the protection of personal information. As smart sensor homes become increasingly prevalent, promoting user education and awareness of best security practices will be vital to maximize benefits and mitigate risks. Overall, smart sensor homes have the potential to transform residential living, providing a more connected, efficient, and sustainable living environment.

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure's impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle's primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process

A major concern for a real-time operation is the reliability of measurements. Especially for the petrochemical industry, which reveals complexity and uncertainty, the measurement fault causes consequences on safety, profitability, and utility management. Degraded signal quality not only leads to improper control action, but it also creates more challenges for real-time energy efficiency management by reducing model performance and wasting more utility than standard operating practice. To improve system reliability and establish an effective energy efficiency monitoring tool, the combined framework for fault detection identification and energy efficiency prediction (FDI-EEP) based on a deep learning approach is proposed in this study. The FDI-EEP model uses the fault detection and identification result as a co-predictor for estimating energy efficiency aimed at improving the performance and reproducibility of the model and studying the effect of these faults on the downstream data-driven framework. Since process information is time-dependent, the long-short term memory layer is deployed on both networks to avoid gradient vanishing problems. A case study on the vinyl chloride monomer process datasets demonstrates that the proposed model precisely detected the measurement uncertainty and accurately performed the prediction task compared to other machine learning and prediction-based data cleaning methods.

Monitoring of grinding condition in drum mills based on resulting shaft torque

Grinding is the most energy-intensive process among all stages of raw material preparation and determines the course of subsequent ore beneficiation stages. Level of electricity consumption is determined in accordance with load characteristics forming as a result of ore destruction in the mill. Mill drum speed is one of process variables due to which it is possible to control ore destruction mechanisms when choosing speed operation mode of adjustable electric mill drive. This study on increasing energy efficiency due to using mill electric drive is based on integrated modelling of process equipment – grinding process and electromechanic equipment – electric drive of grinding process. Evaluating load torque by means of its decomposition into a spectrum, mill condition is identified by changing signs of frequency components of torque spectrum; and when studying electromagnetic torque of electric drive, grinding process is monitored. Evaluation and selection of efficient operation mode of electric drive is based on the obtained spectrum of electromagnetic torque. Research results showed that with increasing mill drum speed – increasing impact energy, load torque values are comparable for the assigned simulation parameters. From the spectra obtained, it is possible to identify mill load condition – speed and fill level. This approach allows evaluating the impact of changes in process variables of grinding process on parameters of electromechanical system. Changing speed operation mode will increase grinding productivity by reducing the time of ore grinding and will not lead to growth of energy consumption. Integration of digital models of the technological process and automated electric drive system allows forming the basis for developing integrated methods of monitoring and evaluation of energy efficiency of the entire technological chain of ore beneficiation.

Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model

For energy modelling of commercial building stock (CBS), several methodologies were developed focusing on technology alternatives and retrofits for climate change mitigation, but they often have limitations to involve a differentiated description of practices and policies across a range of scales and sectors. This paper presents a novel hybrid model by integrating spatial and synthetic modelling approaches to facilitate the concurrent consideration of multiple building-oriented elements. The model is further implemented on Japanese building stock to demonstrate multi-scale and long-term framework for energy modelling of CBS. The results indicate the following findings: the scale choice introduces significant variability in energy consumption estimation with a relative error of 10–17% mainly depending on building geometry and plug loads; the long-term model exhibits a higher scale-bounded uncertainty in terms of energy saving potential that varies by 2–3 times when a non-representative scale is applied; disregarding physical and technical factors results in a cumulative performance gap of up to 32%; and the accuracy and extent of the model across the stock are more sensitive to technical factors, such as building system stock dynamics and the heterogeneity of HVAC systems, than physical factors. The model provides a multi-tier framework using spatial intelligence building stock approach to develop long-term energy efficiency monitoring strategies for CBS.

Monitoring Energy Efficiency and Environmental Ship Index by Cruise Seaports in Northern Europe

Environmental organizations have alerted that rapid weather phenomena have intensified in recent years, such as floods, hurricanes, whirlwinds, droughts, fires and storms, caused by the deteriorating condition of the natural environment. Carbon emissions, which are the main cause of the deteriorating environment condition and dramatic climate change, are largely caused by the use of heavy fuel by shipping companies and generate large quantities of sulfur oxides, nitrogen oxides and particulate matter. These compounds exert a strong negative impact on the environment and human health. Care for improving the marine environment and coastal areas has become the subject of interest of many international institutions, such as the International Maritime Organization, Organization of United Nations, European Sea Port Organization, European Commission and others, which introduce restrictive regulations and guidelines on the level of permissible harmful emissions into the environment as a result of ship operations in ports and at sea. In addition, they propose to monitor the level of pollution through the use of indicators, such as the Energy Efficient Design Index (EEDI), Ship Energy Efficiency Management Plan (SEEMP) and Environmental Ship Index (ESI), among others. The aim of this paper is to assess the Northern Europe seaports’ commitment to monitor the level of pollutants emitted by cruise ships in their ports. This article may constitute a subject of interest for seaport authorities and cruise ship operators.

Solar Panel Monitoring System Using IOT

Abstract: The invention of the smart grid goes beyond the traditional notion of a one-way power supply. Developed countries have already begun to adopt smart meters, devices and renewable energy sources. Developing and countries still face power shortages on a daily basis. The integration of IoT and energy systems has revolutionized the world in terms of energy efficiency and real-time monitoring. This paper describes an experimental study of how IoT can power the current/ voltage and power generation of self-contained renewable energy sources. Solar modules can be monitored. This document also describes how to modify the tilt angle of the solar panel to improve the efficiency of the solar panel. Solar modules are monitored via a network system with NodeMCU, Atmega328 IC, Arduino. By carrying out the proposed work at a photovoltaic (PV) power plant, you can simplify the monitoring of solar panels. In addition, monitoring power generation can significantly improve the health of PV systems. Keywords: IoT based Solar Panel, Solar monitoring, NodeMCU,

Cost Effective Indoor HVAC Energy Efficiency Monitoring based on Intelligent Decision Support System under Fermatean Fuzzy Framework

The heating, ventilation, and air conditioning (HVAC) control system is responsible for the efficient building energy system. Indoor energy consumption patterns can be monitored and reduced intelligently. Occupancy information plays a vital role to save a reasonable amount of energy. Traditional energy monitoring and control systems can be improved with the installation of the occupancy monitoring system which will consist of a network of sensors and cameras. In this research work, we propose a new and revolutionary convolutional neural network (CNN) based on real-time camera occupancy detection and recognition techniques across different sorts of sensors that provide realistic low-cost energy-saving solutions with robust graphical processing units (GPUs). This occupancy information will decide the energy behaviour inside buildings. Decision-making tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we introduce and develop the "Fermatean fuzzy prioritized weighted average and geometric operator". These aggregation operators (AOs) are a modern approach to modelling complexities in decision-making. In the end, we give an algorithm for an intelligent decision support system (IDSS) using proposed AOs to compare our CNN based method with other existing sensors techniques.

Smart Sustainable Freight Transport for a City Multi-Floor Manufacturing Cluster: A Framework of the Energy Efficiency Monitoring of Electric Vehicle Fleet Charging

This study focuses on the problem of the efficient energy management of an independent fleet of freight electric vehicles (EVs) providing service to a city multi-floor manufacturing cluster (CMFMC) within a metropolis while considering the requirements of smart sustainable electromobility and the limitations of the power system. The energy efficiency monitoring system is considered an information support tool for the management process. An object-oriented formalization of monitoring information technology is proposed which has a block structure and contains three categories of classes (information acquisition, calculation algorithms, and control procedures). An example of the implementation of the class “Operation with the electrical grid” of information technology is presented. The planning of the freight EVs charging under power limits of the charging station (CS) was carried out using a situational algorithm based on a Fuzzy expert system. The situational algorithm provides for monitoring the charging of a freight EV at a charging station, taking into account the charge weight index (CWI) assigned to it. The optimization of the CS electrical load is carried out from the standpoint of minimizing electricity costs and ensuring the demand for EV charging without going beyond its limits. A computer simulation of the EV charging mode and the CS load was performed. The results of modeling the electrical grid and CS load using the proposed algorithm were compared with the results of modeling using a controlled charging algorithm with electrical grid limitations and an uncontrolled charging algorithm. The proposed approach provides a reduction in power consumption during peak hours of the electrical grid and charging of connected EVs for an on-demand state of charge (SOC).