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Methanol steam reforming (MSR) has been used for in-situ hydrogen production, while hydrogen purification remains a technical challenge to limit the applications of using hydrogen for fuel cells. This study presents a pure hydrogen generation system (PHGS) by integrating MSR and hydrogen purification modules, this system enables low-temperature reforming and high-temperature purification to achieve high yield and recovery of hydrogen. To investigate the combined effects of feedstock flow rates and pressure on MSR and purification, the performance of each module and overall PHGS system were tested. The results showed that PHGS can stably produce high purity hydrogen (>99.99%) under different conditions, the synergistic effects of two modules lead to hydrogen yield exceeding the numerical sum of individual modules. At methanol feed rate of 1.00 g/min and system pressure of 5.0 bar, the pure hydrogen flow rate, CO content, pure hydrogen yield, hydrogen recovery and thermal power output were tested about 1.70 ± 0.01 L/min, 2.4 ± 0.1 ppm, 81.1 ± 0.5%, 87.6 ± 0.5% and 350 W. The PHGS can deliver up to 500 W of thermal power and is suitable for small range-extended electric vehicles. Our work on the developed PHGS can provide a significant reference for future practical applications of systems combining MSR and membrane purification. [Display omitted] • A separated system based on MSR and Pd membrane is proposed for hydrogen generation. • The effects of feed rate and pressure on system and its modules are investigated. • The system can generate 350 W of high-purity H 2 with an H 2 recovery of 87.6%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cloud computingbased parallel deep reinforcement learning (DRL) energy management strategy (EMS) for connected plug-in hybrid vehicles. First, a proximal policy optimization (PPO) algorithm is developed. Since the cloud computing can reduce the computational burden of the connected vehicles, the PPO is deployed in the proposed cloud computing-based EMS. In order to improve the strategy adaptation, a parallel mechanism is proposed to achieve the information interaction with multiple vehicles. Considering the real-time control requirements, thread pool is proposed and applied in the cloud computing based parallel EMS. The thread pool-based strategy provides an efficient real-time control ability and strategy improvement solution. To verify the PPO based EMS, the dynamic programming, deep Q-network and double deep Q-network strategies are developed for comparison. It is found that the PPO can achieve similar fuel efficiency improvement with the DP strategy among the three DRL algorithms. For parallel training of multiple connected vehicles, the cloud computing-based parallel EMS improves fuel economy by approximately 7.7%. Threadpool based parallel real-time EMS reduces average time for computational interactions by 20% and further improves the fuel efficiency. The proposed strategy has the advantages of realtime control, adaptability and continuous learning for improved fuel efficiency. [ABSTRACT FROM AUTHOR]

One of the industries that use fossil fuels most frequently worldwide is transportation. Therefore, electrifying the transportation system, such as the creation of plug-in electric vehicles (PEV), has become essential to reducing the effects of carbon dioxide emissions and using less traditional energy supplies that are not ecologically friendly. PEV deployment must be flawless, necessitating a well-developed charging infrastructure. The best location for fast charging stations (FCSs) is a crucial issue. As a result, this article offers a practical method for choosing the best site for FCSs using the east delta network (EDN). When transportation is made electric, the infrastructure of the electrical distribution network may also need to be changed. Therefore, when adopting FCSs, three factors need to be considered: actual power loss, reactive power loss, and investment cost. The energy demand from the electrical grid is also increased by including FCSs in the power distribution network. To lessen the impact of FCSs on the system, this research report suggests integrating photovoltaic distributed generation (PVDG) at certain places in the distribution network. Consequently, the system becomes dependable and self-sustaining. After deploying the FCSs and PVDGs, the distribution system's dependability is also examined. Six case studies (CS) have also been suggested for deploying FCSs with or without DG integration. As a result, the CS-6's active power loss decreased from 1015.38 to 830.58 kW. [ABSTRACT FROM AUTHOR]

Due to increasing load demand and the energy crisis, microgrids (MGs) have attracted more attention. The idea and technology of microgrids (MGs) have undergone significant advancements largely aimed at enabling the automation of distribution systems and enhance the integration of renewable energy sources (RESs). In this paper, the MG is a combined form of various distributed generations (DGs), battery energy storage system (BESS), and plug-in hybrid electric vehicles (PHEVs). A novel approach is introduced to minimize the operational expenses of the grid-connected low-voltage microgrid by leveraging a probabilistic expert optimization technique. This methodology takes into account various factors such as power supply costs, power dissipation, PHEV charging requirements, and the optimal capacity of BESS. Three charging techniques are proposed in this paper to control the PHEVs' charging demand at homes and public charging stations. This study employs a comprehensive model to simulate a whole day, accounting for the stochastic charging requirements of PHEVs, load profiles, RESs outputs, and variations in energy price. To address the inherent nonlinearity of the optimization problem, the energy management within the MG is tackled using the slime mould algorithm, a population-based stochastic optimizer. The primary goal is to minimize operational costs in the microgrid by optimizing the efficient utilization of available resources. [ABSTRACT FROM AUTHOR]

For many years Norway has been in the forefront of promoting electromobility. Today, Norway has the world's highest per capita fleet of plug-in electric cars. In 2021, 1.6% of the cars in the EU fleet were plug-in electric vehicles, whereas their share was 21% in Norway. Part of the successful market take-up rate is due to wide-ranging tax exemptions. Increasing plug-in electric vehicles numbers causes tax revenue losses, making exemptions unsustainable. Norway has the ambitious goal that from 2025, all newly registered cars shall be zero-emission vehicles. Keeping tax exemptions in place might be crucial for this goal. The objective of this paper is to provide information to solve this dilemma. Tax exemption reduction and abolition paths which offer a compromise between minimal effects on the development of zero-emission vehicles and tax revenues have been identified. An updated and re-calibrated version of the stock-flow-model SERAPIS was used to simulate and assess different scenarios. Results show that a controlled tax phase-in allows Norway to reach its environmental targets of 100% zero emission vehicles by 2025 and a 55% decrease of CO2-emissions in 2030 relative to 2005 while simultaneously increasing public revenues significantly. [ABSTRACT FROM AUTHOR]

Over the past few decades, many universities have turned to using microgrid systems because of their dependability, security, flexibility, and less reliance on the primary grid. Microgrids on campuses face challenges in the instability of power production due to meteorological conditions, as the output of renewable sources such as solar and wind power relies entirely on the weather and determining the optimal size of microgrids. Therefore, this paper comprehensively reviews the university campuses' microgrids. Some renewable energy sources, such as geothermal (GE), wind turbine (WT), and photovoltaic (PV), are compared in terms of installation costs, availability, weather conditions, efficiency, environmental impact, and maintenance. Furthermore, a description of microgrid systems and their components, including distributed generation (DG), energy storage system (ESS), and microgrid load, is presented. As a result, the most common optimization models for analyzing the performance of campus microgrids are discussed. Hybrid microgrid system configurations are introduced and compared to find the optimal configuration in terms of energy production and flexibility. Therefore, configuration A (Hybrid PV- grid-connected) is the most common configuration compared to the others due to its simplicity and free-charge operation. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) are becoming increasingly widespread due to their environmental benefits. However, PHEV penetration can overload distribution systems and increase operational costs. It is a major challenge to find an economically optimal solution under the condition of flattening load demand for systems. To this end, we formulate this problem as a two-layer optimization problem, and propose a hierarchical algorithm to solve it. For the upper layer, we flatten the load demand curve by using the water-filling principle. For the lower layer, we minimize the total cost for all consumers through a consensus-like iterative method in a distributed manner. Technical constraints caused by consumer demand and power limitations are both taken into account. In addition, a moving horizon approach is used to handle the random arrival of PHEVs and the inaccuracy of the forecast base demand. This paper focuses on distributed solutions under a time-varying switching topology so that all PHEV chargers conduct local computation and merely communicate with their neighbors, which is substantially different from the existing works. The advantages of our algorithm include a reduction in computational burden and high adaptability, which clearly has its own significance for the future smart grid. Finally, we demonstrate the advantages of the proposed algorithm in both theory and simulation. [ABSTRACT FROM AUTHOR]

The transport sector is under scrutiny because of its significant greenhouse gas emissions. Essential strategies, particularly the adoption of zero- and low-emission vehicles powered by electricity, are crucial for mitigating emissions in road transport. Pickups, which are integral to Mexico's fleet, contribute to such emissions. Thus, implementing effective policies targeting pickups is vital for reducing air pollution and aligning with Mexico's decarbonization objectives. This paper presents a simulation model based on system dynamics to represent the adoption process of zero- and low-emission vehicles, with a focus on pickups and utilizing data from the Mexican case. Three policy evaluation scenarios are proposed based on the simulation model: business as usual; disincentives for zero- and low-emission vehicles; and incentives for unconventional vehicles. One of the most significant findings from this study is that even in a scenario with a greater number of vehicles in circulation, if the technology is fully electric, the environmental impact in terms of emissions is lower. Additionally, a comprehensive sensitivity analysis spanning a wide spectrum is undertaken through an extensive computational process, yielding multiple policy scenarios. The analysis indicates that to achieve a maximal reduction in the country's emissions, promoting solely hybrid electric vehicles and plug-in hybrid electric vehicles is advisable, whereas internal combustion engines, vehicular natural gas, and battery electric vehicles should be discouraged. [ABSTRACT FROM AUTHOR]

This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneously optimizing the average speed and operation mode for each road segment in the route. Two mixed-integer nonlinear programming models are proposed for the problem: one with continuous speed decision variables and one with discretized variables. Because the models are nonlinear, we propose reformulation schemes and introduce valid inequalities to strengthen them. We also describe a branch-and-cut algorithm to solve these reformulations. Extensive numerical experiments are performed to demonstrate the algorithm's performance in terms of computing time and energy consumption costs. Specifically, the proposed solution method can efficiently solve instances with a realistic number of customers and outperforms the benchmark approaches from the literature. Integrating speed optimization into the TSP of a PHEV can lead to significant energy savings compared with the fixed-speed TSP. In addition, the proposed model is extended to investigate the impact of the presence of charging stations, which makes the problem harder to solve but has the potential to further reduce energy consumption costs. Funding: F. Wu gratefully acknowledges the support of the National Natural Science Foundation of China [Grant 72271161]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0247. [ABSTRACT FROM AUTHOR]

Increasing distributed generations (DGs) penetration and their plug & play operation challenge the protection coordination of distribution networks due to variable fault current levels. Adaptive protection schemes as a great solution are used to cope with DGs connection status, where the DGs statuses are transmitted to relays via communication links to activate the appropriate Setting Groups (SGs). In this paper, a new hybrid protection scheme is introduced where it is composed of relays with SGs and inter-trip links to develop more sensitive adaptive protection. In the first stage, a sensitivity index is introduced to find the critical constraints and form a set of candidate relays. Then, a new method is proposed to assign the SGs and inter-trips according to the inter-trip effect on coordination restoration. Inter-trips restore the coordination with reducing primary relay operating time reduction, whereas SGs delay the operation of the backup relays. Optimal coordination of the proposed hybrid scheme is simulated on the IEEE 30 Bus distribution grid and compared with other schemes. The results indicate that the proposed hybrid scheme is a more efficient tool to improve the performance of the protection scheme under plug & play operation of DGs achieving lower fault clearance times. [ABSTRACT FROM AUTHOR]

A novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques. [ABSTRACT FROM AUTHOR]

This study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond. [ABSTRACT FROM AUTHOR]

This paper presents the development and practical implementation of a new passive island detection method for grid-connected hybrid distributed generating systems. The method employs positive sequence voltage and current components at the point of common coupling. The product of the difference in positive sequence voltage and the difference in positive sequence current is used to produce the Islanding Detection Index (IDI). Islanding is identified when the IDI magnitude surpasses a predefined threshold over a predefined delay time. Even in the case of a zero power mismatch condition, the proposed method can identify the islanding condition in under 70 msec. Its effectiveness is validated under various operating circumstances. It provides excellent stability against various non-islanding situations, avoiding unwanted tripping. The suggested method also has the advantages of being simple and inexpensive to implement, having no adverse effects on output power quality, not requiring a classifier, not being system-dependent, and working regardless of the quantity and type of distributed generation connected to the utility grid. The obtained detection timings were compared to those previously reported in the literature, confirming the proposed approach's superiority. The suggested work utilizes the OPAL-RT real-time hardware-in-loop environment and MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The Plug-in Electric Vehicles (PEVs) can be influential in containing power system frequency fluctuations. This study, therefore, investigates the efficacy of frequency regulation for PEV-integrated multi-area power network using Grey Wolf Optimizer (GWO) and Salp Swarm Algorithm (SSA) optimized Proportional-Integral-Derivative (PID) control. Instant investigation not only brings out the relative competence of GWO and SSA but also examines the impact of PEV in improving the system performance. The varying operating conditions are realized by subjecting the system to step and random load variations in either or both of the areas. With the proposed control scheme and involvement of PEV, system frequency and tie-line power excursions settle quicker with their peak swings also getting restricted to a lower value, while the oscillations are arrested as well to a great extent. Further, it's the SSA that shows its superiority over GWO as per the simulation results executed in MATLAB. [ABSTRACT FROM AUTHOR]

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. [ABSTRACT FROM AUTHOR]

In addition to providing mobility, plug‐in hybrid electric vehicles (PHEVs) provide a two‐sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all. [ABSTRACT FROM AUTHOR]

Electric vertical take-off and landing (eVTOL) aircraft are becoming more and more attractive due to the improvements in electric road vehicles, and the mounting demand for new urban air mobility. As here discussed, due to the low specific energy density of batteries, hybridization of the propulsion system with fuel chemical energy storage and electricity production on board by either an internal combustion engine and generator, or fuel cells stack, appears to be a better avenue to deliver performance (cruise speed, range, and payload) than using only large and heavy batteries at least through 2030. • Range and efficiency for plug-in electric vertical takeoff and landing (eVTOL) aircraft can be enhanced. • This involves using fuel chemical energy storage for electricity generation onboard. • This electricity generation may occur by internal combustion engines (ICEs) and generators. • Alternatively, it may occur by fuel cells (FCs). [ABSTRACT FROM AUTHOR]

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes. [ABSTRACT FROM AUTHOR]

To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid. [ABSTRACT FROM AUTHOR]

Modern automotive propulsion technologies must achieve the highest CO2 reduction potential quickly to abide by the requirements of the Paris Climate Agreement. A collective utilization of renewable fuels, e‐fuels, hydrogen, and electrical energy will be able to meet different mobility and transport requirements in an optimal and CO2‐neutral approach. The well‐to‐wheel greenhouse gas emissions of a propulsion system are determined by two factors, that is, the energy efficiency of the system and the carbon intensity of the energy source. Regardless of the CO2 emission generated during the battery manufacturing and recycling process, the carbon intensity of the battery electric vehicles during operation is mainly decided by the carbon intensity of the electricity being consumed. The relatively low fleet ratios of battery electric and hydrogen‐powered vehicles and the massive remaining useful life of current internal combustion engine vehicle stock limit their impact on decarbonization in the near term. The expansion of charging infrastructure requires significant acceleration for the success of large‐scale and rapid electric vehicle adoption. For internal combustion engines, the focus is to further improve energy efficiency and the adoption of low‐to‐zero carbon renewable fuels. Hybrid and plug‐in hybrid vehicles are demonstrating the advantages of combining state‐of‐the‐art technologies to reduce both energy consumption and carbon emissions. In this review, the present status of propulsion systems is reviewed in detail, considering both the market penetration and well‐to‐wheel carbon emissions. The decarbonization potentials of various propulsion systems are then discussed. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid energy model comprising of various stand-alone and grid-connected energy systems including grid-connected hybrid, off-grid hybrid, fuel cell-photovoltaic, fuel cell-wind turbine, photovoltaic-wind turbine, wind turbine, and photovoltaic to fulfill the power requirements of an educational institute in Pakistan. These systems have been developed, simulated, and optimized for techno-economic feasibility, taking into consideration the electricity demand profile, geographic regions, and climatic data. A sensitivity analysis has been conducted to determine the most economical systems for producing 0.90079 MWh of energy per day. Simulation results show that the net present cost of the grid-connected hybrid system is between M$ 1.536859 and M$ 6.818162, while the cost of energy is between $ 0.155 and $ 0.3749 per kWh. The fuel cell -wind turbine system incurs a maximum energy cost of $ 0.3749/kWh. The solar and grid-connected hybrid systems offers the lowest net present cost and cost of energy, which are M$ 1.536859 and $ 0.155/kWh, respectively. The research findings suggest that utilizing photovoltaic, wind, and fuel cells is a more feasible and economical strategy due to system benefits. The study also showed the viability and efficiency of using solar and wind resources for hydrogen production and power generation. Additionally, the study demonstrated that hydrogen is a more affordable option for long-term energy storage than batteries. • Seven different energy systems are considered in this research. • Sensitivity investigation is conducted in effort to determine the most economic systems. • Cost of energy is found to be between 0.155 and 0.3495 $/kWh. • Maximum cost of energy is accounted by wind turbine system 0.3495 $/kWh. • Hydrogen is a more affordable long-term energy storage alternative than batteries. [ABSTRACT FROM AUTHOR]

Emissions of brake-wear particles are commonly associated with vehicular traffic. We investigated the feasibility of quantifying brake-wear particle emissions under realistic vehicle driving and braking conditions with a currently used regenerative friction brake coordination system. We used a braking system installed in commercially available plug-in hybrid electric vehicles and found that it reduced emissions by 85% for PM10, 78% for PM2.5, and 87% for particle numbers (PNs) compared with the system installed in vehicles with internal combustion engines. Brake friction work showed a linear relationship with PM10 and PM2.5. Nanoparticle PM emissions tended to increase slightly with regenerative braking but did not contribute significantly to the overall PM percentage. The emission events of high concentrations of nuclei-mode particles (<20 nm in diameter) in electric vehicle brake assemblies designed for regenerative braking use under high-temperature, high-load braking conditions with full-friction brakes. The nuclei-mode particles amplified the PN emissions and led to high variability. In strict regulatory certification tests where measurement reproducibility and stability are required, it is appropriate to measure PNs under brake conditions appropriate for the actual use of electric vehicles rather than under full-friction brake conditions or to remove particle measurements smaller than 20 nm. [ABSTRACT FROM AUTHOR]

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery's state of charge (SoC) and to increase the battery's lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions. [ABSTRACT FROM AUTHOR]

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit's committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year. [ABSTRACT FROM AUTHOR]

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a quasi‐two‐switch buck‐boost power factor correction (PFC) converter for use in on‐board battery chargers to create a variable output voltage that is less than or greater than the peak input voltage. A two‐stage converter links the input grid power to the battery pack both in battery‐operated electric cars (BEVs) and plug‐in hybrid electric vehicles (PHEVs), with battery pack voltages ranging from 100 to 500 V depending on vehicle size and capacity. A universal charger that can manage such a wide range of battery pack voltages is appropriate for all vehicle designs. This requirement is met by supplying a changeable DC link voltage at the input of the DC/DC converter, which is a major obstacle in battery chargers when it comes to achieving universal output voltages. The major contribution of this research is the analysis and design of a dual‐control technique for a cascaded buck‐boost converter suited for a power factor correction (PFC) rectifier. The control loop is designed to allow a seamless transition from buck‐boost operation while putting less stress on the devices. The converter's power loss and small‐signal model are also investigated. From an economic standpoint, this concept allows the automobile industry to manufacture a single power converter, which is flexible and capable of charging numerous vehicle variants. Results have been verified both with a PSIM (11.0) simulation model and an experimental setup for a 1‐kW PFC converter suitable for universal input voltages of 85–265 Vrms and broad output voltages. [ABSTRACT FROM AUTHOR]

Integration of electric vehicles (EVs) into the unbalanced distribution system (UDS) brings more challenges to power system operators as this additional load deteriorates the performance parameters of the UDS. To deal with the growing load demand and variation of load throughout the day, optimal allocation of renewable energy resources is becoming common practice as they help to reduce greenhouse gas emissions along with enhancing the performance parameter of UDS. With the advancement of the vehicle-to-grid (V2G) technology, plugged-in EVs can play a critical role to support the grid. But uncontrollable and volatile nature of renewable energy sources (RES) and EVs increases the difficulty of managing power dispatch according to demand. This critical task of flexible operation is tackled by introducing an optimal coordinated approach along with incentive-based demand-side management (DSM) of EVs that allow the optimal dispatch and scheduling of distributed energy resources and EVs to minimize the operating cost and enhance the performance parameters of UDS. A new optimization algorithm is also proposed for optimal allocation of distributed energy resources (DERs) by solving multipoint multi-objective optimization problem considering plug-in EVs. It has been shown that using the proposed strategy average active power loss reduced up to 13.93%, whereas voltage deviation and voltage unbalance decreased to 70.24 % and 19.34 % respectively. The entire running cost is decreased to 78.72% using the optimal coordinated operation of DER-DSM. [ABSTRACT FROM AUTHOR]

Battery technologies have recently undergone significant advancements in design and manufacturing to meet the performance requirements of a wide range of applications, including electromobility and stationary domains. For e-mobility, batteries are essential components in various types of electric vehicles (EVs), including battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs). These EVs rely on diverse charging systems, including conventional charging, fast-charging, and vehicle-to-everything (V2X) systems. In stationary applications, batteries are increasingly being employed for the electrical management of micro/smart grids as transient buffer energy storage. Batteries are commonly used in conjunction with power electronic interfaces to adapt to the specific requirements of various applications. Furthermore, power electronic interfaces to batteries themselves have evolved technologically, resulting in more efficient, thermally efficient, compact, and robust power converter architectures. This article offers a comprehensive review of new-generation battery technologies. The topic is approached from the perspective of applications, emerging trends, and future directions. The article explores new battery technologies utilizing innovative electrode and electrolyte materials, their application domains, and technological limitations. In conclusion, a discussion and analysis are provided, synthesizing the technological evolution of batteries while highlighting new trends, directions, and prospects. [ABSTRACT FROM AUTHOR]

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation's significant hydroelectric power generation potential to fulfill EVs' charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers' likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal. [ABSTRACT FROM AUTHOR]

The international share of plug-in electric vehicle sales was 8.6% in 2021, compared with South Africa's 0.1%. Increasing the sale of these vehicles in South Africa is directly tied to the continued existence of South Africa's vehicle manufacturing industry. To address this challenge, businesses and governments need to understand better the factors influencing electric vehicle adoption. This study identified significant factors that influence and are influenced by electric vehicle adoption. By reviewing the current literature, numerous factors were identified and mapped in a causal loop diagram with an adapted system dynamics approach. The impact of taxation on the cost of electric vehicles and the low availability of electricity negatively affects both consumer confidence in owning an electric vehicle and local manufacturing capacity. This model serves as a useful tool to piece together how various factors in South Africa affect and are affected by electric vehicle adoption. [ABSTRACT FROM AUTHOR]

With electro-mobility, the demand for charging services is increasing. In Germany alone, the Federal Ministry for Digital Affairs and Transport required for at least 843,000 new charging stations by 2030. Charging service is key for electro-mobility and requires a new innovative technology for the future. Plug and Charge (PnC) enables easy, convenient, and secure charging process compared in existing technologies (RFID, SMS, or App). This secure and convenient charging process is based on digital certificates from PnC ecosystem. Today, validation of certificates between electric vehicle (EV) and charging station (EVSE) in PnC ecosystem is very simple. For the future, there are many new requirements for PnC ecosystem from new standard (ISO 15118-20), Federal Ministry of Economics and Climate Protection, new certificates (V2G root CAs) and new Pool&Service (e.g., Root Certificate Pool). This will make the validation of certificates between EV and EVSE more and more difficult. These new and existing requirements are identified, analyzed and evaluated here. With this article, for the first time, these requirements are derived on entire PnC ecosystem, on cloud components (e.g., MO) and on individual functions (e.g., mTLS, certificate authentication, and authorization). From these requirements, a three-level solution (1-EV, 2-PKI, and 3-Cloud components (e.g., MO, CPO)) is generated. These three level solutions enable successful validation of the certificate with or without interoperability of the certificate. New and various solutions for future networks (e.g., contract and root certificate pools) for either interoperable or non-interoperable networks are illustrated here. [ABSTRACT FROM AUTHOR]

This paper discusses emissions from plug-in hybrid vehicles under various driving scenarios and reports experimental data obtained under laboratory and real-world conditions. Two European plug-in hybrid passenger cars were tested using the two test types in use in the EU (chassis dynamometer and on-road), with some modifications. The best-case and near-worst-case battery states of charge were used for testing. Behavior in terms of CO2 emissions, regulated emissions, and unregulated emissions was characterized and analyzed. Differences were generally much greater for on-road testing, especially for urban driving, during which the potential for purely electrical propulsion of the vehicle is greatest. The long distances covered by current EU legislative test procedures limit the impacts of some effects. Regardless of the traction battery's state of charge, regulated emissions were well below the applicable EU limits under all driving conditions--for example, combined emissions of reactive nitrogen compounds (nitrogen oxides, ammonia, and nitrous oxide) were consistently < 10 mg/km when tested under laboratory conditions. The two vehicles tested showed that the state of the battery had a large impact on the proportion of electrical propulsion and the resulting CO2 emissions, but differences in regulated pollutants decrease with increasing distance and are generally relatively limited for longer journeys, which include non-urban driving. [ABSTRACT FROM AUTHOR]