Search

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy's real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinearity-considered regenerative brake torque compensation control (NL-TCC) strategy, aimed at enhancing the brake stability for plug-in hybrid electric vehicles (PHEVs) with dual-motor powertrain. The dynamics models of hybrid brake system considering gear backlash and hydraulic brake nonlinearity are established in MATLAB/Simulink environment. Furthermore, the fluctuation of half-shaft torque is adopted as performance indexes to design the NL-TCC strategy for PHEVs with dual-motor powertrain, and the grey wolf optimizer (GWO) is used to obtain the optimal weight coefficients of different stage error functions. Finally, by means of hardware-in-the-loop (HiL) test, it is concluded that the vehicle jerk of the proposed NL-TCC strategy decreases by a minimum of 51.2% than the strategy without torque compensation control. [ABSTRACT FROM AUTHOR]

This study employed the Life Cycle Assessment (LCA) method to evaluate the greenhouse gas (GHG) emissions of passenger vehicles across 31 provinces in China. It examined the potential of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) in significantly reducing emissions compared to Internal Combustion Engine Vehicles (ICEVs). The findings revealed that HEVs, PHEVs, and BEVs substantially lower emissions under the national average electricity GHG emissions coefficient, with PHEVs being the most effective, reducing emissions by over 30%. However, disparities exist at the provincial level due to varying electricity structures, with BEVs not fully effective in reducing emissions in about ten provinces, including Liaoning. The study further categorized provinces into six groups using the K-means method to guide the promotion of different types of New Energy Vehicles (NEVs). Sensitivity analysis highlights the critical role of reducing electricity emission coefficients and adjusting PHEV's utility factor in achieving emission reductions. The study also notes that battery replacements in PHEVs and BEVs during their lifecycle could impede emission reduction in most provinces. To maximize the environmental benefits of NEVs, there is a need to move to a greener electricity mix. At the same time, strategies for promoting NEVs should vary for different provinces. [ABSTRACT FROM AUTHOR]

Intelligent energy management of new energy vehicles is an important topic of advanced automotive technology research, and is critical to further improve the fuel economy performance of the entire vehicle. Aiming at the challenge of considering both real-time and optimal capabilities for global energy management and control of plug-in hybrid electric vehicle (PHEV), this paper studies full trip adaptive energy management based on energy consumption prediction. An adaptive equivalent fuel consumption minimum control algorithm for full-trip PHEV is proposed. Finally, MATLAB/Simulink modeling and simulations are conducted, and the results show that the proposed control strategy has good followability and adaptability to the changes of actual driving conditions, driving mileage and automobiles’ energy states. The approximate global optimization control has good effects on the full path, and the fuel economy of the vehicle is significantly improved. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets. [ABSTRACT FROM AUTHOR]

The demand for the integration of renewable energy sources (RESs) with the existing distribution grid is increasing rapidly because of the growing power requirement. The variable power generation from RESs and changing power demand make it necessary to integrate energy storage units. To get stable and trouble-free operation in both transient state and steady state, a combination of battery and supercapacitor storage devices are considered in this work as hybrid energy storage devices (HESDs). But to ensure the power balance in the grid-connected microgrid is a critical aspect nowadays. Hence, a real power management algorithm is proposed in this work to ensure a balance between energy production and demand and provide stability in the microgrid. Both simulation and experimental implementation of the proposed scheme confirm the efficacy and smooth operation of the hybrid microgrid. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources. [ABSTRACT FROM AUTHOR]

The development and potential adoption of drones or unmanned aerial vehicles as delivery vehicles creates incredible opportunities and unique challenges for last mile delivery. This research first presents a last mile delivery fleet model with drones that can be further modified and expanded over time. The model shows the optimal number of drones needed based on deterministic or stochastic demand using both traditional charging and battery swapping. The research then compares the carbon emissions of four delivery modes: traditional internal combustion delivery vehicles, all-electric vehicles, plug-in hybrids, and drones within the context of last mile delivery. Findings reveal that the breakdown of carbon emissions by delivery modality depends on parameter assumptions, ambient temperature, delivery radius, electric grid pollution rate, and number of customers. [ABSTRACT FROM AUTHOR]

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs' life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 104, 1.12 × 104 and 1.01 × 104 kg CO2-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a Multi-Criteria Decision Analysis methodological framework is developed to rank alternative fuel buses. It is applied to the case-study of the public transportation system of the island of Gotland in Sweden. Seven alternative fuel buses are identified: bioethanol, biodiesel, biogas, plug-in hybrid electric biodiesel, plug-in hybrid electric biogas, battery electric and hybrid electric hydrogen fuel cell buses. These alternatives are evaluated under 11 criteria that are categorized under four dimensions and measured under a Life Cycle Sustainability Analysis approach where possible. The ranking is accomplished by means of the weighted summation method; weights are determined through the Analytic Hierarchy Process. Explicit weights on these criteria provided by local experts and stakeholders are used to evaluate the alternatives under various island-specific scenarios. Results show that biogas and plug-in hybrid electric biogas buses are the preferred alternatives as long as biogas is produced from waste residuals; otherwise, the import of biodiesel could be the preferred short-term alternative. Bioethanol appears to be the least suitable fuel. The developed methodological framework proved helpful to the local authorities of Gotland when they sought to identify the best compromise fuel bus solution and has shown that it can be applied in similar case-studies. [ABSTRACT FROM AUTHOR]

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study applied a synthetic control method (SCM) to investigate the impacts of state electric vehicle purchase incentives implemented in the United States between 2011 and 2018. The goal was to develop a model to replicate the impacts of PEV (plug-in electric vehicle) incentives on vehicle sales given the socio-economic characteristics of the treatment states. The goodness-of-fit between trajectories reproduced by SCM and the corresponding treatment region provides a clear means of visualizing the effectiveness of the model and results. For example, the results show that Georgia income tax credits appear to have induced 17,000 BEV (battery electric vehicle) sales between January 2011 and July 2015. The SCM analysis was also applied to other states, showing that the SCM methodology can represent PEV sales in many states in the United States. Accordingly, the modeling results suggest that incentives implemented in some U.S. states were effective in promoting PEV sales. The study also concluded that the SCM with a weighting factor between 0 and 1 (i.e., extrapolation avoided) may not be the approach of a choice for the states whose demographic/economic attributes significantly deviate from the control regions, or the states that implement "unique" programs, because there are insufficient system data for counterfactual analysis. [ABSTRACT FROM AUTHOR]

This article proposes a hybrid technique for a grid-connected solar–wind hybrid system with electric vehicles. The Mexican Axolotl Optimization and wild horse optimizer are the proposed optimization techniques. The wild horse optimizer improves the axolotl's life behavior. As a result, the proposed scheme is conducted while reducing the annualized cost of the system and utilizing the proposed method. Using modern optimization approaches, the component is sized to achieve the lowest levelized cost of electricity by decreasing the loss of power supply probability. Lastly, the sensitivity analysis is performed to analyze the influence of maximum grid sales and buy capabilities on levelized cost of electricity. The proposed technique's performance is then executed in MATLAB environment and compared to several current methodologies. As a result of the simulation outcomes, the efficiency and performance of the current method are compared to other techniques. According to simulation outcomes, the energy management system (EMS) may lower general expenses by more than 55% and 29% in summer and winter, respectively, while ensuring the satisfaction rate of demand for electric vehicle-charging without knowing the departure times of electric vehicles. [ABSTRACT FROM AUTHOR]

This study compares different powertrains of sport utility vehicles (SUVs) with respect to ambient temperature and energy mix in Slovakia using the well-to-wheel (WTW) Life Cycle Assessment (LCA) method. Battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and petrol and diesel vehicles were assessed and compared. The WTW study was conducted in SimaPro software assessing electricity/petrol/diesel production, transport, and use (energy conversion in the vehicle), with impact categories being climate change, particulates, NOx emissions, ionizing radiation, and fossil resource scarcity depending on the season (summer and winter). The results indicate that for Slovak conditions, BEV generally had the lowest environmental impact in both seasons studied. The only exceptions were ionizing radiation, which is clearly caused by the high share of nuclear power in the Slovak energy mix, and NOx emissions, which are caused by the combustion of biomass for electricity generation. The other impact categories were dominated by vehicles with an internal combustion engine. The results of emissions from fuel production are also given for each impact category. The transportation of fuel did not exceed the value of 1% for any impact category or for any powertrain. The conclusions of the study support the global trend in favour of vehicle electrification as an important way to reduce the negative environmental impacts of internal combustion engine vehicles in Slovakia. [ABSTRACT FROM AUTHOR]

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine's operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy. [ABSTRACT FROM AUTHOR]

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively. [ABSTRACT FROM AUTHOR]

Rapid growth in vehicle ownership in the developing world and the evolution of transportation technologies have spurred a number of new challenges for policymakers. To address these challenges, this study develops a system dynamics (SD) model to project the future composition of Iran's vehicle fleet, and to forecast fuel consumption and CO2 emissions through 2040. The model facilitates the exploration of system behaviors and the formulation of effective policies by equipping decision-makers with predictive insights. Under various scenarios, this study simulates the penetration of five distinct vehicle types, highlighting that an increase in fuel prices does not constitute a sustainable long-term intervention for reducing fuel consumption. Additionally, the model demonstrates that investments aimed at the rapid adoption of electric transportation technologies yield limited short-term reductions in CO2 emissions from transportation. The projections indicate that the number of vehicles in Iran is expected to surpass 30 million by 2040, with plug-in and hybrid electric vehicles (EVs and PHEVs) comprising up to approximately 2.2 million units in the base scenario. It is anticipated that annual gasoline consumption and CO2 emissions from passenger cars will escalate to 30,000 million liters and 77 million tons, respectively, over the next two decades. These findings highlight the need for a strategic approach in policy development to effectively manage the transition towards a lower-carbon vehicle fleet. [ABSTRACT FROM AUTHOR]

Smart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid‐connected hybrid AC–DC microgrid. The biogeography‐based optimization algorithm was utilized to seek the best solution for scheduling micro‐turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33‐bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation. [ABSTRACT FROM AUTHOR]

This paper focuses on the modeling and analysis of a high-voltage layer heater (HVLH) designed for environmentally friendly vehicles, including electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), through multiphysics simulations that cover electrical, thermal, and fluid dynamics aspects. Due to the significant expenses and extensive time needed for producing and experimentally characterizing HVLHs, simulation and physical modeling methods are favored in the development stage. This research pioneers the separate modeling of thermal boundary conditions for the heating element (TFE) within the electrical domain, enabling the calculation of Joule heating and the analysis of transient conjugate heat transfer. Moreover, this research initiates the application of transfer function modeling for the HVLH component, expanding its use to the broader context of heating, ventilation, and air conditioning (HVAC) systems. The simulation results, which include calculations for Joule heating and temperature fields based on input voltage and flow conditions, closely follow experimental data. The derived transfer function, along with the regression parameters, precisely predicts the dynamic behavior of the system. The simulation-based modeling approach presented in this study significantly advances the design and control of environmentally friendly electric heating systems, providing a sustainable and cost-effective solution. [ABSTRACT FROM AUTHOR]

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]

Ethanol as a renewable fuel has been applied in fuel vehicles (FVs), and it is promising in hybrid electric vehicles (HEVs). This work aims to investigate the emission characteristics of ethanol applied in both FVs and plug-in hybrid electric vehicles (PHEVs). The paper conducted a real-road test of an internal combustion FV and PHEV, respectively, based on the world light vehicle test cycle (WLTC) by using gasoline and regular gasoline under different temperature conditions. The use of E10 and E20 in FVs has been effective in reducing the conventional emissions of the vehicles. At 23 °C, E10 and E20 reduced the conventional emissions including carbon monoxide (CO), total hydrocarbon compound (THC), non-methane hydrocarbon compound (NMHC), particulate matter (PM), and particulate number (PN) by 15.40–31.11% and 11.00–44.13% respectively. At 6 °C, E10 and E20 reduced conventional emissions including THC, CO, and PM by 2.15–8.61% and 11.02–13.34%, respectively. However, nitrogen oxide (NOX) emissions increased to varying degrees. The reduction trend of non-conventional emissions including methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from FVs fueled with E10 and E20 is not significant for vehicles. Overall, the emission reduction effect of E20 is better than that of E10, and the emission reduction effect of ethanol gasoline on vehicle emissions is reduced at low temperatures. Lower ambient temperatures increase vehicle emissions in the low-speed segment but decrease vehicle emissions in the ultra-high-speed segment. HEV emissions of THC, CO, PN, and PM are reduced by 25.28%, 12.72%, 77.34%, and 64.59%, respectively, for E20 compared to gasoline, and the use of E20 in HEVs contributes to the reduction of overall vehicle emissions. [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]

In this work, a grid-connected hybrid energy system composed of wind/PV/FC/DG with a HESS based on a battery and supercapacitor. To protect the latter from deep discharge and enhance network flexibility, we use a diesel generator. The objectives of this research are: For generator-side control systems, we apply the TSR strategy for the wind power generator and the FLC strategy for the photovoltaic generator to achieve maximum energy operation. On the management side, we proposed and implemented a new hybrid intelligent energy management strategy based on the FLC-ANN to balance energy supply and demand and increase the life cycle of the proposed system. As for the grid side, the DPC-FLC was applied to control the flow of active and reactive power flowing in the grid. To evaluate the effectiveness of the proposed controllers under changing conditions in energy demand and climate, a simulation test of the proposed system was conducted using the Matlab-Simulink platform. Finally, the results obtained demonstrated the efficiency of the proposed system in terms of the feasibility and effectiveness of the proposed control techniques, in addition to the THD of the network current, which reached 0.34%, which contributes to improving the power quality of hybrid systems. [ABSTRACT FROM AUTHOR]

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides a comprehensive analysis of a grid-connected hybrid microgrid system that seamlessly integrates renewable energy sources, encompassing wind generators, solar arrays, and Fuel Cells (FCs). Emphasis is placed on the pivotal role of power electronic converters in optimizing control and energy management strategies for these diverse sources. The wind and solar subsystems employ Perturb and Observe (P&O) controllers to achieve Maximum Power Point Tracking (MPPT). Additionally, the study delves into the analysis and control design of the grid-connected hybrid system inverter, employing a Proportional-Integral (PI) control technique in the synchronous d-q frame to maximize the output voltage response and active power. In managing renewable grid energy based on artificial neural networks (ANNs), the main goal is to address grid availability concerns by prioritizing renewable sources. The hybrid system acts as a backup during grid unavailability and simultaneously produces hydrogen via electrolysis. The excess energy is seamlessly supplied to the grid upon filling the hydrogen tank. The proposed solution shows great promise for use in renewable energy management systems that combine hybrid technologies. [ABSTRACT FROM AUTHOR]

To design relevant policy measures and incentives to accelerate the uptake of electric vehicles in Australia, it is important to understand who the early adopters of electric vehicles are, their reasons for choosing an electric vehicle and their levels of satisfaction in driving, charging locations, home charging and other experiences. This article presents the results of a survey of early adopters in Queensland, Australia. The data have been analysed to understand the socio-demographic profiles of early adopters and to explore variations between two groups of adopters: Tesla owners, having the highest uptake of battery electric vehicles in Australia, and non-Tesla owners of battery electric vehicles and plug-in hybrid electric vehicles. The analysis suggests that both groups of early adopters are predominantly male, university educated and have substantially higher incomes than the state average. Peer and media influences, and having a test drive, play a significant role in owning a Tesla. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively. [ABSTRACT FROM AUTHOR]