Optimal Charging Research Articles

Edge-Rich 3D Structuring of Metal Chalcogenide/Graphene with Vertical Nanosheets for Efficient Photocatalytic Hydrogen Production.

Constructing pertinent nanoarchitecture with abundant exposed active sites is a valid strategy for boosting photocatalytic hydrogen generation. However, the controllable approach of an ideal architecture comprising vertically standing transition metal chalcogenides (TMDs) nanosheets on a 3D graphene network remains challenging despite the potential for efficient photocatalytic hydrogen production. In this study, we fabricated edge-rich 3D structuring photocatalysts involving vertically grown TMDs nanosheets on a 3D porous graphene framework (referred to as 3D Gr). 2D TMDs (MoS2 and WS2)/3D Gr heterostructures were produced by location-specific photon-pen writing and metal-organic chemical vapor deposition for maximum edge site exposure enabling efficient photocatalytic reactivity. Vertically aligned 2D Mo(W)S2/3D Gr heterostructures exhibited distinctly boosted hydrogen production because of the 3D Gr caused by synergetic impacts associated with the large specific surface area and improved density of exposed active sites in vertically standing Mo(W)S2. The heterostructure involving graphene and TMDs corroborates an optimum charge transport pathway to rapidly separate the photogenerated electron-hole pairs, allowing more electrons to contribute to the photocatalytic hydrogen generation reaction. Consequently, the size-tailored heterostructure showed a superior hydrogen generation rate of 6.51 mmol g-1 h-1 for MoS2/3D graphene and 7.26 mmol g-1 h-1 for WS2/3D graphene, respectively, which were 3.59 and 3.76 times greater than that of MoS2 and WS2 samples. This study offers a promising path for the potential of 3D structuring of vertical TMDs/graphene heterostructure with edge-rich nanosheets for photocatalytic applications.

Evaluating optimal quality assurance and quality control conditions of activation measurements at the accelerator-based boron neutron capture therapy system employing a lithium target

Evaluating neutron output is important to ensure proper dose delivery for patients in boron neutron capture therapy (BNCT). It requires efficient quality assurance (QA) and quality control (QC) while maintaining measurement accuracy. This study investigated the optimal measurement conditions for QA/QC of activation measurements using a high-purity germanium (HP-Ge) detector in an accelerator-based boron neutron capture therapy (AB-BNCT) system employing a lithium target. The QA/QC uncertainty of the activation measurement was evaluated based on counts, reproducibility, and standard radiation source uncertainties. Measurements in a polymethyl methacrylate (PMMA) cylindrical phantom using aluminum-manganese (Al–Mn) foils and aluminum-gold (Al–Au) foils and measurements in a water phantom using gold wire with and without cadmium cover were performed to determine the optimal measurement conditions. The QA/QC uncertainties of the activation measurements were 4.5% for Au and 4.6% for Mn. The optimum irradiation proton charge and measurement time were determined to be 36 C and 900 s for measurements in a PMMA cylindrical phantom, 7.0 C and 900 s for gold wire measurements in a water phantom, and 54 C and 900 s at 0–2.2 cm depth and 3,600 s at deeper depths for gold wire measurements with cadmium cover. Our results serve as a reference for determining measurement conditions when performing QA/QC of activation measurements using HP-Ge detectors at an AB-BNCT employing a lithium target.

Health-conscious optimal control of Li-ion cell using simplified full homogenized macro-scale model

This article presents the optimal charging of a Li-ion cell based on a simplified full homogenized macro-scale (FHM) model. A solid electrolyte interface (SEI) layer model is included in the simplified FHM model to quantify cell degradation. With these models, a multi-objective optimal control problem subject to constraints from safety concerns is formulated to achieve health-conscious optimal charging. This constrained optimal control problem is converted to a nonlinear programming problem (NLP). A nonlinear model predictive control (NMPC) strategy is adopted by solving the NLP at each sampling time using the pseudo-spectral optimization method. The effect of the input current upper bound on the cell film resistance Rfilm and state of health (SoH) reveals that Rfilm and SoH are more sensitive to input current upper bound at lower values of input current upper bound. Simulation results show that the simplified model and pseudo-spectral method are crucial for reducing the computational load of the model. The proposed algorithm is more efficient in reducing health degradation than the conventional constant current constant voltage (CCCV) charging algorithm since it can explicitly handle the film resistance and capacity as health parameters. Multiple cycle charging simulations revealed that the health-conscious algorithm decreases health degradation and increases battery life.

PARAMETRIC IDENTIFICATION OF A MATHEMATICAL MODEL FOR THE COPPER MATTE CONVERSION PROCESS

The paper provides data on the construction of mathematical models using parametric identification of objects, as well as the development of algorithms that allow calculating optimal charge plans in production, and also addresses issues related to modeling and control of electric melting. As a result of the work, the issues of partial automation through channels have been solved: regulation of electric power, charge loading control, melt level measurement, research on mechanization and automation of smelting products, as well as theoretical, experimental and pilot industrial studies on automation of electric furnaces of non-ferrous metallurgy. This control system of the electrothermal furnace operates in open mode, solves individual functional tasks of process control and management and provides solutions to the problems of optimal control of the technological process of electric melting. The paper also discusses issues related to the automatic control of the electric power of electrothermal furnaces. The furnace is considered as an object of channel control: electrode deepening-electrode currents.

Minimizing the impact of electric vehicle charging station with distributed generation and distribution static synchronous compensator using PSR index and spotted hyena optimizer algorithm on the radial distribution system

This research investigates the impact of electric vehicle charging station on power loss, voltage stability, and reliability within radial distribution systems. With the escalating use of electric vehicles, understanding these effects is crucial for ensuring the stability and efficiency of radial distribution systems infrastructure. The study formulates the electric vehicle charging station allocation problem as a multi-objective function, integrating power loss, voltage stability, and reliability metrics expressed as the PSR index. Using the IEEE 69-bus system as a test system, the research explores the ramifications of electric vehicle charging station integration and proposes strategies to mitigate their effects. An innovative combination of distributed generation and distribution static compensator are employed to alleviate the impact of electric vehicle charging station on radial distribution systems. A recent optimization methodology called spotted hyena optimizer algorithm is introduced to determine optimal electric vehicle charging station locations, with results compared to other existing algorithms. The study assesses potential power losses induced by additional electric vehicle loads, explores strategies to optimize charging rates for minimizing resistive losses, and evaluates voltage stability and reliability implications. The important results include objective function values obtained using various algorithms: Spotted hyena optimizer algorithm (0.7683), cuckoo search algorithm (0.7709), bat algorithm (0.8035), and African vultures optimization algorithm (0.7856). The findings demonstrate that the proposed algorithm outperforms other algorithms in minimizing the objective function value, indicating its efficacy in optimizing electric vehicle charging station placement within radial distribution systems. In conclusion, this research contributes valuable insights into the challenges associated with integrating electric vehicle charging station into radial distribution systems and provides innovative solutions for optimizing electric vehicle charging station placement, thereby advancing the understanding and management of distribution systems in the context of electric vehicle adoption.

Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control

Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life.

Experimental investigation into the influence of different system configurations on charge optimization in an R410A chiller with a dedicated subcooler

The current study addresses the important issue of reducing the amount of refrigerant used in HVAC&R systems due to new regulations and the phase-down of high Global Warming Potential (GWP) refrigerants. Specifically, this research compares the optimal refrigerant charge for two distinct scroll compressors: a variable speed compressor and a single speed compressor, both operating within the same R410A chiller equipped with a condenser, receiver and dedicated subcooler. The nominal cooling capacity of this chiller is 8 kW. One novelty of this study lies in assessing the charge optimization not only at full load but also at part load conditions to determine if there is any effect of using continuous capacity modulation like a variable speed compressor on the optimum charge. The results reveal that the COP maximizing charge corresponds to when some part of the two-phase heat transfer happens in the dedicated subcooler and not when the receiver is partially filled with liquid refrigerant as expected. Interestingly, at part load conditions, the compressor lacking continuous capacity modulation demonstrates higher optimum subcooling and COP improvement potential compared to full load conditions. Moreover, the study also analyzes the impact of operating at peak COP charge on seasonal performance using a variable speed compressor. This seasonal performance investigation reveals that although cooling capacity obtained when the system is operating with COP maximizing charge is 6 % lower, the seasonal performance is 11 % higher when compared to typical operating charge. Notably, for the first time, charge optimization is conducted in the same experimental with different configurations of the condenser with/without receiver and with/without subcooler to understand the contribution of these components. This systematic comparison revealed the absence of abnormal COP peak in configurations without a dedicated subcooler.

Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

Optimal charge amount for semiconductor chiller applying eco-friendly refrigerant R-744

The refrigerants used to cool the heat generated in semiconductor production are Freons, which are now subject to regulation because of their high ozone depletion potential (ODP) and global warming potential (GWP). Therefore, R-744 is considered an eco-friendly refrigerant. This study experimentally analyzed the effect of the refrigerant charge amount based on the temperature discharged after cooling objects when operating a brine chiller using R-744 under subcritical conditions. The results showed that the optimal charge amount in terms of the coefficients of performance (COP) differed from that in terms of the maximum cooling capacity. In case of the apparatus used in this study, the optimal refrigerant charge amount with maximum cooling capacity was estimated at 8.2 kg when the temperature of the cooling object was in the range of −20 °C and −10 °C and at 9.2 kg when the temperature of the cooling object was 0 °C. However, in terms of COP, the optimal refrigerant charge amount was estimated at 8.2 kg when the temperature of the cooling object was in the range of −20 °C and 0 °C. This difference was attributed to the variation in the required evaporation heat capacity with the target temperature of the cooling object.

Investigating heterogeneity in the price elasticity of traffic flow in Melbourne: Evidence from traffic volume data

AbstractThis paper leverages a rich dataset of 1.24 billion traffic flow observations across 36,666 vehicle detectors to estimate the price elasticity of traffic flow with respect to fuel prices in Melbourne, Australia. I find that a 1% increase in fuel prices leads to a 0.0556% decrease in traffic flow across Melbourne, where traffic flow is defined as the number of vehicles passing a vehicle counter per hour. I find significant regional heterogeneity across Melbourne, with evidence of positive own‐price elasticities in regions in the centre of the city, and in peak commute times on both weekdays and weekends, which I argue is evidence of a congestion‐speed effect. Moreover, regions near the CBD with high incomes tend to be less elastic than regions further away with reduced access to public transport. These findings can be used to determine the optimal tax or charge to internalise the externalities produced by motor vehicles.

Experimental investigation on the influence of electronic expansion valve opening on the performance of miniature refrigeration system

In the cooling of personnel and electronic equipment under specific conditions, the miniature refrigeration system needs to provide different cooling capacity to adapt to different working conditions. At present, most scholars use capillaries as throttling components. Compared with capillaries, miniature refrigeration systems using electronic expansion valves as throttling components can provide a wider range of cooling capacity adjustment. Therefore, this study constructed a miniature vapor compression refrigeration system based on electronic expansion valves as throttling devices, with a system weight of 3.0 kg and dimensions of 380 mm × 300 mm × 200 mm. The experiment investigated the impact of electronic expansion valve opening on compressor power, cooling capacity, and COP under different refrigerant charge conditions. Results indicated that at an electronic expansion valve opening of 90, within the experimental range, there was a relative change in cooling capacity of 39.85 % and a relative change in COP of 29.11 % as the refrigerant charge increased. Refrigerant charge had a significant effect on the cooling capacity and COP of the miniature refrigeration system. Under the optimal refrigerant charge of 70 g, within the experimental range, there was a relative change in cooling capacity of 88.14 % with varying electronic expansion valve opening. The use of electronic expansion valves as throttle components in miniature refrigeration systems allows for a wider range of cooling capacity adjustments, making them suitable for more cooling conditions.

Determining The Location of Charging Station Facilities in Surakarta City Using Gravity Location Models

Environmental problems caused by motorized vehicles are in the spotlight in the modern era. Various methods have been taken to reduce the environmental impact caused by motorized vehicles. One way to reduce the negative environmental impacts caused by motorized vehicles is to use environmentally friendly vehicles, namely electric vehicles. Presidential Regulation No. 55 of 2019 encourages the electrification of motorized vehicles in Indonesia. To support these regulations, there needs to be infrastructure for the smooth and easy use of electric vehicles. This research aims to determine the optimal charging station location in the city of Surakarta using gravity location models to support the implementation of the government program for vehicle electrification. Based on the calculations carried out, Banjarsari is the optimal point for the location of the charging station.

Optimizing battery energy storage prototypes for improved resilience in commercial buildings: Gaussian mixture modeling and hierarchical analysis of energy storage potential

The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management.

Enhancing Electric Bus Charging Scheduling: An Energy-Integrated Dynamic Bus Replacement Strategy with Time-of-Use Pricing

Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a dynamic bus replacement strategy. When the power of an in-service EB is insufficient, a standby EB stationed at nearby charging stations is dispatched in advance to replace this in-service EB at a designated bus stop. Passengers then transfer to the standby bus to complete their journey. The replaced bus proceeds to the charging station and transitions into a “standby bus” status after recharging. A mixed-integer nonlinear programming (MINLP) model is established to determine the dispatching plan for both standby and in-service EBs while also designing optimal charging schemes (i.e., the charging time, location, and the amount of charged power) for electric bus systems. Additionally, this study also incorporates the strategy of time-of-use electricity prices to mitigate the adverse impact on the power grid. The proposed model is linearized to the mixed-integer linear programming (MILP) model and efficiently solved by commercial solvers (e.g., GUROBI). The case study demonstrates that EBs with different energy levels can be dynamically assigned to different bus lines using bus replacement strategies, resulting in reduced electricity costs for EB systems without compromising on scheduling efficiency.

Distributed Charging Strategy of PEVs in SCS with Feeder Constraints Based on Generalized Nash Equilibria

In this article, a distributed charging strategy problem for plug-in electric vehicles (PEVs) with feeder constraints based on generalized Nash equilibria (GNE) in a novel smart charging station (SCS) is investigated. The purpose is to coordinate the charging strategies of all PEVs in SCS to minimize the energy cost of SCS. Therefore, we build a non-cooperative game framework and propose a new price-driven charging control game by considering the overload constraint of the assigned feeder, where each PEV minimizes the fees it pays to satisfy its optimal charging strategy. On this basis, the existence of GNE is given. Furthermore, we employ a distributed algorithm based on forward–backward operator splitting methods to find the GNE. The effectiveness of the employed algorithm is verified by the final simulation results.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Research on refrigerant charge determination under different compressor speed and its effects on the performance of transcritical CO2 air-conditioning heat pump system in electric vehicle

CO2 is assumed to be one of the most potential refrigerant alternatives for electric vehicles for its excellent properties. However, the charge determination of CO2 in current studies remain controversial. In this study, a transcritical CO2 air-conditioning heat pump system was established and experimentally tested to analyze the optimal charge amount. Based on two conflicting methods of charge determining proposed by the preceding research, this paper substantiated the existing controversy and subsequently proposed a more comprehensive method. The effects of different refrigerant charge on the system characteristics were investigated. The influence of the compressor speed on the optimal refrigerant charge and system characteristics was also analyzed. It was found that the optimal charge plateau occurred from refrigerant of 500 g–580 g at the compressor speed of 3000 r·min−1. However, the optimal charge declined with the increment of compressor speed from 3000 r·min−1 to 4500 r·min−1. Among three models, Hughmark's model was proved to be the most appropriate for the theoretical calculation of optimal charge within an error of 6.09%. Further study illustrates that refrigerant mass in high-pressure pipe and intermediate heat exchanger/accumulator (IHX/A) accounted for the main proportion about 52.6%–55.14%.

Intelligent, optimal charging with MSSC and PCM protocols for healthy and long life on Li-ion batteries

MSCC is one of the most efficient charging methods with the appropriate advantages over the CC/CV method most commonly used in the Li-ion battery charger market. Our approach to this optimal charging is based on the weak points of CC/CV charging, as well as the battery's reaction after each CC step in testing the new optimal current. Given the needs of the industrial market, which demands fast charging and safety against overcharging and side effects, we decided to develop a fast, optimal charging method aimed first and foremost at guaranteeing a long battery life cycle. Our intelligent charging strategy uses a combination of two charging protocols, MSSC and PCM, each with functionality and a communication link via an STM32 MCU. The principle is to find the optimum charging current for the battery at each stage of charging, depending on its state of health, and then apply the MSSC with a modern, optimized method based on methodological calculations, always respecting the temperature conditions.
 Finally, based on several experimental tests, we were able to prove that the optimal, intelligent method proposed in this article reduces charging time by 16.8% and temperature by 20%, compared with our own intelligent CC/CV+PCM method.

Machine learning Approach to Battery Management and Balancing Techniques for Enhancing Electric Vehicle Battery Performance

The objective of this research is to apply machine learning techniques to optimize electric vehicle battery management and balance to attain maximum battery performance. Here, we will assess and compare the efficiency and accuracy of decision tree classifier, ANN, and Naive Bayes classifiers in: predicting two optimal charging and discharging battery management strategies, prediction of the battery’s performance and detecting any abnormalities in it. . In this context, one machine learning model will be proposed to have the most advantageous performance. The experimental results details that such findings are attained, and the precision rates, recall rates, and F1-scores attained by net models exceed 98%. Additionally, the decision tree, as well as the Naive Bayes classifier, have impressive performance, and their accuracy rates exceed 90%. Decision trees along with Naive Bayes classifiers have also important impacts on the identification of classification of battery’s responses through probabilistic classification in the former. Our findings have important implications for ensuring electric vehicle batteries are managed more properly for optimal performance. Additionally, based on the results obtained, they can be utilized to help relevant stakeholders in the EV industry and other industries with battery usage implement practical measures that optimize battery performance, increase battery life, and reduce the incurred operational expenses of electric vehicle fleets.

Integrating IoT and Artificial intelligent for Enhanced Electric Vehicle Charging and Autonomous Driving for sustainable transportation

This research explores the confluence of Internet of things and artificial intelligence towards fostering electric vehicle charging and autonomous driving for sustainable transportation. By meticulously collecting and preprocessing, and executing multiple machine learning models such as Artificial Neural Network , Decision Trees , Naive Bayes , and Random Forest , the research delves into charging infrastructure and easy autonomous driving. Particularly, real-time datasets, such as EV station data, road geography and mapping data, weather and telematics data, were properly collected and processed . Then, the preprocessing stage, which included data cleaning, normalization, and feature engineering had improved the datasets’ predictability. Consequently, the datasets such as charging recommendation, predicting energy use, predicting of optimal route, had produced highly accurate training and testing for forecasting elelctric vehicles’ demands. The ML models indicated their proficiency in predicting not only the optimal charging schedules, but also predictive energy consumption, and predictive routes. Specifically, ANN with 0.945 held the highest precision, while DT 0.912, NB 0.887, and RF 0.819 were almost identical . Likewise, the recall scores, F1 scores, and AUC ROC scores also coincided the prized outcome of the models. The findings of the research are groundbreaking for both transportation sector, policymakers, as well as urban planners and stakeholders at large. Following the data-fed recommendations would not be excessively challenging, and they would yield favourable results whether the stakeholders are dealing with EV charging infrastructure, or dealing with traffic congestions, or are establishing their own sustainable solution for future mobility via not just electric vehicles but also other tools and devices. Hence, we are hopeful that our research serves as a lighthouse for the transportation domain towards a greener future.