Search Results (368)

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

Driven by the rapid advancement of Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies, improving power quality and system stability during charging and discharging has become a research focus. To address this, this paper proposes a Model Predictive Control (MPC) strategy for Active Neutral-Point Voltage Balancing (ANPVB) in a single-phase three-level converter used in on-board V2G chargers. Traditional converters rely on passive balancing using redundant vectors, which cannot ensure neutral-point (NP) voltage stability under sudden load changes or frequent power fluctuations. To solve this issue, an auxiliary leg is introduced into the converter topology to actively regulate the NP voltage. The proposed method avoids complex algorithm design and weighting factor tuning, simplifying control implementation while improving voltage balancing and dynamic response. The results show that the proposed Model Predictive Current Control-based ANPVB (MPCC-ANPVB) and Model Predictive Direct Power Control-based ANPVB (MPDPC-ANPVB) strategies maintain the NP voltage within ±0.7 V, achieve accurate power tracking within 50 ms, and reduce the total harmonic distortion of current (THDi) to below 1.89%. The proposed strategies are tested in both V2G and G2V modes, confirming improved power quality, better voltage balance, and enhanced dynamic response. Full article

The low-carbon transition of the global energy system is an urgent necessity to address climate change and meet growing energy demand. As a major source of energy consumption and emissions, buildings play a key role in this transition. This study systematically analyzes the flexible resources of building energy systems and vehicle-to-grid (V2G) interaction technologies, and mainly focuses on the regulation characteristics and coordination mechanisms of distributed energy supply (renewable energy and multi-energy cogeneration), energy storage (electric/thermal/cooling), and flexible loads (air conditioning and electric vehicles) within regional energy systems. The study reveals that distributed renewable energy and multi-energy cogeneration technologies form an integrated architecture through a complementary “output fluctuation mitigation–cascade energy supply” mechanism, enabling the coordinated optimization of building energy efficiency and grid regulation. Electricity and thermal energy storage serve as dual pillars of flexibility along the “fast response–economic storage” dimension. Air conditioning loads and electric vehicles (EVs) complement each other via thermodynamic regulation and Vehicle-to-Everything (V2X) technologies, constructing a dual-dimensional regulation mode in terms of both power and time. Ultimately, a dynamic balance system integrating sources, loads, and storage is established, driven by the spatiotemporal complementarity of multi-energy flows. This paper proposes an innovative framework that optimizes energy consumption and enhances grid stability by coordinating distributed renewable energy, energy storage, and flexible loads across multiple time scales. This approach offers a new perspective for achieving sustainable and flexible building energy systems. In addition, this paper explores the application of demand response policies in building energy systems, analyzing the role of policy incentives and market mechanisms in promoting building energy flexibility. Full article

Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil fuel vehicles. Additionally, electric vehicles are highly efficient, with an efficiency of around 90%, in contrast to fossil fuel vehicles, which have an efficiency of about 30% to 35%. The higher energy efficiency of electric vehicles contributes to lower operational costs, which, alongside regulatory incentives and shifting consumer preferences, has increased their strategic importance for many vehicle manufacturers. In this paper, we present a thematic literature review on electric vehicles charging station location planning and scheduling. A systematic literature review across various data sources in the area yielded ninety five research papers for the final review. The research results were analyzed thematically, and three key directions were identified, namely charging station deployment and placement, optimal allocation and scheduling of EV parking lots, and V2G and smart charging systems as the top three themes. Each theme was further investigated to identify key topics, ongoing works, and future trends. It has been found that optimization methods followed by simulation and multi-criteria decision-making are most commonly used for EV infrastructure planning. A multistakeholder perspective is often adopted in these decisions to minimize costs and address the range anxiety of users. The future trend is towards the integration of renewable energy in smart grids, uncertainty modeling of user demand, and use of artificial intelligence for service quality improvement. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article

Electric vehicles (EVs) offer a sustainable solution for reducing carbon emissions in the transportation sector. However, their increasing widespread adoption poses significant challenges for local distribution grids, many of which were not designed to accommodate the heightened and irregular power demands of EV charging. Components such as transformers and distribution networks may experience overload, voltage imbalances, and congestion—particularly during peak periods. While upgrading grid infrastructure is a potential solution, it is often costly and complex to implement. The unpredictable nature of EV charging behavior further complicates grid operations, as charging demand fluctuates throughout the day. Therefore, efficient integration into the grid—both for charging and potential discharging—is essential. This paper reviews recent studies on the impacts of high EV penetration on distribution grids and explores various strategies to enhance grid performance during peak demand. It also examines promising optimization methods aimed at mitigating negative effects, such as load shifting and smart charging, and compares their effectiveness across different grid parameters. Additionally, the paper discusses key challenges related to impact analysis and proposes approaches to improve them in order to achieve better overall grid performance. Full article

This study analyses aspects related to the electromobility transition. Emerging technologies have enabled the production and commercialisation of electric vehicles to reduce polluting emissions. However, significant obstacles are present in this global transition. The analysis identifies that public policies play a crucial role in the development of electromobility, and emphasises how new business models in electromobility are emerging to satisfy changing customer demands. Concerns related to raw materials extraction, battery disposal, and vehicle-to-grid (V2G) integration are also important to consider. The relationship between technologically advanced countries and raw material-producing nations must balance socioeconomic, historical, labour, and ecological factors. In order to have a standard reference, this study considers for the analysis the political, economic, social, technological, environmental, and legal factors (PESTLE). An analysis of future scenarios considering pessimistic and optimistic trends revealed that, compared with the actual trends, important actions must be taken to develop electromobility not only from the technological aspect. These results provide a comprehensive analysis of electromobility sustainability and its importance for multidisciplinary stakeholders related to the actual challenges towards electromobility, the electric network capabilities, and the importance of creating new jobs and products based on a circular and sustainable economy. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

As electric vehicles (EVs) become increasingly integrated into urban mobility, the load on electrical grids increases, prompting innovative energy management strategies. This paper investigates the deployment of vehicle-to-grid (V2G) services at the University of Rome Tor Vergata, leveraging high-resolution floating car data (FCD) to forecast and schedule energy transfers from EVs to the grid. The methodology follows a four-step process: (1) vehicle trip detection, (2) the spatial identification of V2G in the campus, (3) a real-time scheduling algorithm for V2G services, which accommodates EV user mobility requirements and adheres to charging infrastructure constraints, and finally, (4) the predictive modelling of transferred energy using ARIMA and LSTM models. The results demonstrate that substantial energy can be fed back to the campus grid during peak hours, with predictive models, particularly LSTM, offering high accuracy in anticipating transfer volumes. The system aligns energy discharge with campus load profiles while preserving user mobility requirements. The proposed approach shows how campuses can function as microgrids, transforming idle EV capacity into dynamic, decentralised energy storage. This framework offers a scalable model for urban energy optimisation, supporting broader goals of grid resilience and sustainable development. Full article

The utilization of electric vehicles (EVs) has grown significantly and continuously in recent years, encouraging the creation of new implementation opportunities. The battery electric vehicle (BEV) charging system can be effectively used during peak load periods, for voltage regulation, and for the improvement of power system stability within the smart grid. It provides an efficient bidirectional interface for charging the battery from the grid and discharging the battery into the grid. These two operation modes are referred to as grid-to-vehicle (G2V) and vehicle-to-grid (V2G), respectively. The management of power flow in both directions is highly complex and sensitive, which requires employing a robust control scheme. In this paper, an Integral Fast Terminal Synergetic Control Scheme (IFTSC) is designed to control the BEV charger system through accurately tracking the required current and voltage in both G2V and V2G system modes. Moreover, the Black-Winged Kite Algorithm is introduced to select the optimal gains of the proposed IFTS control scheme. The system stability is checked using the Lyapunov stability method. Comprehensive simulations using MATLAB/Simulink are conducted to assess the safety and efficacy of the suggested optimal IFTSC in comparison with IFTSC, optimal integral synergetic, and conventional PID controllers. Furthermore, processor-in-the-loop (PIL) co-simulation is carried out for the studied system using the C2000 launchxl-f28379d digital signal processing (DSP) board to confirm the practicability and effectiveness of the proposed OIFTS. The analysis of the obtained quantitative comparison proves that the proposed optimal IFTSC provides higher control performance under several critical testing scenarios. Full article

The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions. Full article

This study presents a comprehensive life-cycle assessment of Vehicle-to-Grid (V2G) economic viability, explicitly integrating the costs of both battery cycling degradation and calendar aging. While V2G offers revenue through energy arbitrage, its net profitability is critically dependent on regional electricity price differentials and the associated battery degradation costs. We develop a dynamic cost–benefit model, validated over a 10-year horizon across five diverse regions (Shanghai, Chengdu, the U.S., the U.K., and Australia). The results reveal stark regional disparities: Chengdu (0.65 USD/kWh peak–valley gap) and Australia (0.53 USD/kWh) achieve substantial net revenues of up to USD 25,000 per vehicle, whereas Shanghai’s narrow price differential (0.03 USD/kWh) renders V2G unprofitable. Sensitivity analysis quantifies critical break-even price differentials, varying by EV model and annual mileage (e.g., 0.12 USD/kWh minimum for Tesla Model Y). Crucially, calendar aging emerged as the dominant degradation cost (67% at 10,000 km/year), indicating significant battery underutilization potential. Policy insights emphasize the necessity of targeted interventions, such as Chengdu’s discharge incentives (0.69 USD/kWh), to bridge profitability gaps. This research provides actionable guidance for policymakers, grid operators, and EV owners by quantifying the trade-offs between V2G revenue and battery longevity, enabling optimized deployment strategies. Full article