Search Results (1,163)

The rapid development of electromobility increases the need for fast, accessible and robust charging stations devoted to EVs (electric vehicles). Planning a network of such stations poses new challenges—amongst others, a power supply that may power such chargers. One major concept is to utilise wind energy as a power source. The paper analyses meteorological data gathered since 2001 in several stations across Poland to achieve quantitative indexes, which summarise (a) wind power density (WPD) as a metric of energy amount, (b) long-term (multiannual) time trends of amount of energy, (c) short-term stability (and thus predictability) of the wind power. The indexes that cover the abovementioned factors allow the authors to answer the research questions, where the local wind conditions allow the authors to consider the integration of a wind powerplant and a network of EV chargers. Additionally, we investigated locations where the amount of available energy is sufficient, but the variability of wind power impedes its practical exploitation. In such cases, the power system may be extended by an energy storage system that acts as a buffer, smoothing power fluctuations and thereby improving the robustness and reliability of downstream charging systems. Full article

Green hydrogen production and the use of fuel cells (FCs) in microgrid (MG) systems have become viable and feasible solutions due to their continuous cost reduction and advancements in technology. Furthermore, green hydrogen electrolyzers and FC can mitigate fluctuations in renewable energy generation and various demand-related disturbances. Proper incorporation of electrolyzers and FCs can enhance load frequency control (LFC) in MG systems. However, they are subjected to multiple false data injection attacks (FDIAs), which can deteriorate MG stability and availability. Moreover, most existing LFC control schemes—such as conventional PID-based methods, single-degree-of-freedom fractional-order controllers, and various optimization-based structures—lack robustness against coordinated and multi-point FDIAs, leading to significant degradation in frequency regulation performance. This paper presents a new, modified, multi-degree-of-freedom, cascaded fractional-order controller for green hydrogen-based MG systems with high fluctuating renewable and demand sources. The proposed LFC is a cascaded control structure that combines a 1+TID controller with a filtered fractional-order PID controller (FOPIDF), namely the cascaded 1+TID/FOPIDF LFC control. Furthermore, another tilt-integrator derivative electric vehicle (EV) battery frequency regulation controller is proposed to benefit from EVs installed in MG systems. The proposed cascaded 1+TID/FOPIDF LFC control and EV TID LFC methods are designed using the powerful capability of the exponential distribution optimizer (EDO), which determines the optimal set of design parameters, leading to guaranteed optimal performance. The effectiveness of the newly proposed cascaded 1+TID/FOPIDF LFC control and design approach employing multi-generational-based two-area MG systems is studied by taking into account a variety of projected scenarios of FDIAs and renewable/load fluctuation scenarios. In addition, performance comparisons with some featured controllers are provided in the paper. For example, in the case of fluctuation in RESs, the measured indices are as follows: ISE (1.079, 0.5306, 0.3515, 0.0104); IAE (15.011, 10.691, 9.527, 1.363); ITSE (100.613, 64.412, 53.649, 1.323); and ITAE (2120, 1765, 1683, 241.32) for TID, FOPID, FOTID, and proposed, respectively, which confirm superior frequency deviation mitigation using the proposed optimized cascaded 1+TID/FOPIDF and EV TID LFC control method. Full article

The electrification of transport is rapidly reshaping power distribution networks, introducing new technical, regulatory, and operational challenges for Distribution System Operators (DSOs). This article presents an international review of electromobility integration strategies, analyzing experiences from Europe, Canada, Australia, and Greece. It examines how DSOs address grid impacts through smart charging, vehicle-to-grid (V2G) services, and demand flexibility mechanisms, alongside evolving regulatory and market frameworks. European initiatives—such as Germany’s Energiewende and the UK’s Demand Flexibility Service—demonstrate how coordinated planning and interoperability standards can transform electric vehicles (EVs) into valuable distributed energy resources. Case studies from Canada and Greece highlight region-specific challenges, such as limited access in remote communities or island grid constraints, while Australia’s high PV penetration offers unique opportunities for PV–EV synergies. The findings emphasize that DSOs must evolve into active system operators supported by digitalization, flexible market design, and user engagement. The study concludes by outlining implementation barriers, policy implications, and a roadmap for DSOs. Full article

Lithium-ion batteries (LIBs) are currently widely used in the electric vehicle sector and have become one of the core components of new energy vehicles. To ensure that the maximum temperature (T_max) and maximum temperature difference (∆T_max) remain within acceptable limits after high-rate discharge, this study proposes a novel air-cooled battery thermal management system (BTMS). This BTMS features innovative design elements in its novel battery case walls and contour-following fin structure. Through physical testing of 21,700 LIB discharges and comparative numerical simulations, the accuracy of the simulation model is ensured. Orthogonal experimental analysis is conducted at four distinct levels for each of the four structural factors involved. The final results demonstrate that the novel battery pack wall and contour-shaped fin structure proposed in this paper significantly enhance the heat dissipation capability of air-cooled BTMS. The proposed Model 9 configuration exhibits optimal thermal performance metrics. The T_max after 3C rate discharge reaches 39.4 °C, with a ∆T_max of 7.4 °C. This study demonstrates significant application potential in the structural implementation of air-cooled BTMSs. Full article

The trend of the world to electric mobility and the inclusion of renewable energy requires complex control and predictive models of Smart Electric Vehicle Charging Stations (SEVCSs). The paper describes an experimental artificial intelligence (AI) model that can be used to optimize EV charging in New York City based on ten years of historical load and weather information. Nonlinear environmental relationships with urban energy demand and the use of Neural Fitting and Regression Learner models in MATLAB were used to explore the nonlinear relationships between the environment and energy demand. The quality of the input data was maintained with a lot of preprocessing, such as outlier removal, smoothing, and time alignment. The performance measurements showed that there was a Mean Absolute Percentage Error (MAPE) of 4.9, and a coefficient of determination (R²) of 0.93, meaning that there was a high level of concordance between the predicted and measured load profiles. Such findings indicate that AI-based models can be used to replicate load dynamics during renewable energy variability. The research combines the findings of long-term and multi-source data with the short-term forecasting to address the research gaps of past studies that were limited to a few small datasets or single-variable-based time series, which will provide a replicable base to develop energy-efficient and intelligent EV charging networks in line with future grid decarbonization goals. The proposed neural network had an R² = 0.93 and RMSE = 36.4 MW. The Neural Fitting model led to less RMSE than linear regression and lower MAPE than the persistence method by a factor of about 15 and 22 percent, respectively. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Electromobility provides an effective solution for developing countries to reduce dependence on fossil fuels, enhance energy security, and increase environmental sustainability. The current study evaluates the feasibility of implementing electric vehicles (EVs) powered by renewable energy in developing countries. Based on qualitative methods, including expert interviews, it discusses existing transportation systems, the benefits of EVs, and significant constraints such as poor infrastructure, high initial investment, and ineffective policy structures. Evidence further suggests that EV adoption is likely to bring considerable benefits, particularly in cities with high population densities, adequate infrastructure, and supportive regulations that facilitate rapid adoption. Countries like India and Kenya have reduced their fuel import bills and created new jobs. At the same time, cities such as Bogota and Nairobi have seen improved air quality through the adoption of electric public transit. However, the transition requires investments in charging infrastructures and improvements in power grids. Central to this is government backing, whether through subsidy or partnership. Programs like India’s Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) initiative and China’s subsidy program are prime examples of such support. The study draws on expert interviews to provide context-specific insights that are often absent in global EV discussions, while acknowledging the limitations of a small, regionally concentrated sample. These qualitative findings complement international data and offer grounded implications for electromobility planning in developing contexts. It concludes that while challenges remain, tailored interventions and multi-party public–private partnerships can make the economic and environmental promise of electromobility in emerging markets a reality. Full article

The rapid growth of electric vehicles (EVs) is expected to create a substantial stream of retired automotive batteries over the coming decades, offering an opportunity for low-cost stationary storage deployment. This paper quantifies how much additional photovoltaic (PV) capacity can be unlocked in Greece through the systematic use of second-life EV batteries under the new self-consumption and zero feed-in regulatory framework. First, a deterministic cohort model is developed to estimate the annual potential of second-life batteries, considering parameters like EV sales, first-life duration, repurposing eligibility, and second-life operational lifetime. The results indicate that Greece could accumulate from 3.5 GWh to 12.1 GWh of second-life batteries until 2050, depending on future EV growth rates. Next, to link battery capacity with PV unlocked potential, an hourly time-series simulation is implemented under a zero feed-in scheme, i.e., without exporting energy to the grid, indicating that each kilowatt-hour of second-life battery can unlock 0.33 kW of PVs in residential zero feed-in systems. On this basis, second-life batteries could unlock from 1.1 GW to 3.9 GW of additional PV capacity that would otherwise be infeasible. For comparison, the peak load of Greece is about 10 GW. Importantly, unlike large-scale grid-connected PV plants—where transmission system operators increasingly impose curtailments—zero feed-in installations can operate seamlessly without creating additional operational stress for the grid. Full article

The evolution of electrical energy in all its aspects is one of the biggest challenges facing the operation of modern power systems. With the addition of numerous new, networked components to these systems, such as intelligent devices, electric vehicles, renewable energy sources, and battery storage, maintaining reliability and security is becoming increasingly difficult. To ensure that power transmission systems are secure and reliable, network security and reliability evaluations must be performed on a regular basis. The conventional approaches of using engineering tools for power flow analyses need to be enhanced in light of current grid challenges. More comprehensive analyses require the use of complex network concepts. This research proposes employing a combination of the Neplan engineering software and complex network concepts using Python NetworkX, both of which are recent advances, to address the challenges related to the modern grid by assessing the security and reliability of the power transmission system. The experimental results show enhanced reliability and security by employing complex network concepts to assess the grid’s topology and identify essential elements based on their centrality and betweenness and power flow analyses to comprehend how power flows and how various operating conditions impact the system. Full article

This study systematically investigates the structure–performance relationship of PMA (PolyMethacrylate) viscosity index improvers in new energy vehicle (NEV) transmission fluids. We developed an integrated analytical framework combining spectroscopic and chromatographic techniques to simultaneously characterize its side chain length distribution, molecular weight polydispersity, and branching architecture. Key findings reveal that the kinematic viscosity of formulated oils positively correlates with PMA molecular weight, low-temperature performance is governed by side-chain length (≥C14 fatty alcohols), shear stability is predominantly determined by molecular weight, and nitrogen-modified PMA enhances oxidation resistance by mitigating kinematic viscosity increase. These insights provide actionable guidance for the molecular design of viscosity index improvers and the formulation optimization of advanced lubricants to meet the stringent demands of electric vehicle transmission systems. Full article

With the global trend towards “carbon neutrality,” the use of electric vehicles is becoming increasingly widespread, leading to new impacts on urban spaces. In the process of allocating resources for urban charging stations, there are widespread issues such as a singular planning approach and inadequate adaptation to actual travel demands. Therefore, this study adopts a method of integrating multi-source data to optimize the planning and layout of public electric vehicle charging facilities in Macau, striving to achieve breakthroughs in theoretical methods and key technologies. The study obtained a determination coefficient of R² = 0.43 through quantitative analysis, which is within a reasonable range of fitting spatial syntax and charging facility layout. This indicates that there is a moderate positive correlation between the distribution of charging facilities and core indicators such as road network integration and accessibility—about 43% of layout differences can be explained by spatial syntax indicators, and the remaining 57% of differences reserve space for optimizing multiple factors such as population density and parking lot distribution. On this basis, this study compares the layout experience of medium to high-density cities such as Hong Kong and Singapore, and combines the common characteristics of old parishes on Macau Island and new urban areas on outlying islands to explore innovative sustainable development technology paths that are suitable for Macau. This study not only summarizes the key factors and optimization breakthroughs that affect the spatial distribution of charging facilities in Macau, providing basic data and methodological strategies for charging facility planning, but also helps Macau save energy and reduce emissions, build a green city through layout optimization, provide practical reference for the development of land reclamation areas, and provide reference for carbon neutrality and smart city construction in the Guangdong Hong Kong Macau Greater Bay Area. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles. Full article

The increasing global emphasis on sustainable transportation drives the need for strong electric vehicle (EV) charging networks. While national plans set high targets for EV adoption, translating these into practical infrastructure placement poses a significant hurdle. This study tackles this by creating detailed maps to show suitable locations for EV charging stations (EVCS) across the Attica region of Greece. Our main approach combines Geographic Information System (GIS) with Multi-Criteria Decision-Making (MCDM), specifically using the Analytic Hierarchy Process (AHP). After reviewing existing research to find important location factors, we adjusted these to fit the unique urban and social features of metropolitan Athens. We established four main criteria, accessibility, social, energy, and environmental, which were then divided into nine sub-criteria for our analysis. We developed four different models, each applying a unique weighting to these criteria (basic, energy-focused, environmental, and social) to see how various planning goals affect spatial outcomes. These models generated graded suitability maps, highlighting areas with high potential for new infrastructure. Central Athens consistently showed the highest suitability, which matches current research and confirms our method’s reliability. This work provides a useful, repeatable framework for local governments to strategically deploy EVCS, supporting urban planning and helping meet national goals for decarbonization and air quality. Full article

To achieve the optimal energy allocation between the auxiliary power unit (APU) and battery of connected automated range-extended electric vehicle (CAR-EEV), the hierarchical eco-driving control with dynamic game energy management were investigated and the optimization design of APU working mode was carried out from a multi-objective perspective. Initially, the acceleration and speed of the host vehicle were adjusted in real time, based on the driving status of the preceding vehicle, and the ecological driving speed was obtained in the adaptive car-following eco-driving mode. The dynamic game energy management strategy was proposed, leveraging the real-time interactive information between the vehicle and the traffic environment, and intelligently allocating and scheduling the energy flow within the powertrain. Dynamic game optimization was adopted to achieve dynamic decision-making and control optimization on whether to switch the APU operating speed or not. The multi-objective optimization analyses are carried out based on the weight coefficient matrix. The hierarchical dynamic game energy management strategy based on eco-driving speed (HDGEMS) is implemented through dynamic games and exhibits excellent performance. This strategy enables dynamic adjustment of power distribution between the APU and the battery, thereby allowing the APU to operate efficiently under optimal operating conditions. Meanwhile, it effectively reduces secondary charging losses and the dynamic switching time of the APU, and ultimately achieves energy optimization. Eventually, the results of simulation and experimental thoroughly indicated that economy improvement, emission reduction, and battery life enhancement of CAR-EEV were effectively kept in balance under the control of the proposed HDGEMS with intelligent optimization mode. New research ideas and technical directions are provided for the field of EMS, which is expected to promote technological progress in the industry. Full article