Search Results (88)

Auxiliary power demand in battery electric vehicles continues to increase as manufacturers transition toward multi-low-voltage architectures that combine 48 V and 12 V buses to improve load distribution flexibility and overall system efficiency. This paper evaluates several auxiliary power module (APM) architectures in terms of scalability, efficiency, complexity, size, and cost for supplying two low-voltage buses (e.g., 48 V and 12 V) from the high-voltage battery. Based on this assessment, a cascaded APM configuration is adopted, consisting of an isolated dual active bridge (DAB) converter followed by a non-isolated synchronous buck converter. A multi-objective optimization framework based on the NSGA-II algorithm is developed for the DAB stage to maximize efficiency and power density while minimizing cost. The optimized 13 kW DAB stage achieves a peak efficiency of 95% and a power density of 4.1 kW/L. For the 48 V/12 V buck stage, a 2 kW commercial GaN-based converter with a mass of 0.5 kg is used as the reference design, achieving a peak efficiency of 96.5%. Dedicated PI controllers are designed for both the DAB and buck stages using their respective small-signal models to ensure tight regulation of the two LV buses. The overall system stability is verified through impedance-based analysis. Experimental validation using a DAB prototype integrated with a multi-phase buck converter confirms the accuracy of the DAB loss modeling used in the design optimization framework as well as the control design implemented for the cascaded converters. Full article

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring. Full article

This literature review addresses a major research gap in electromobility by providing a comprehensive synthesis of machine learning (ML) and deep learning (DL) applications for forecasting energy consumption, managing battery state of charge (SoC), and integrating electric vehicles (EVs) with charging infrastructure and smart grids, including vehicle-to-grid (V2G) systems. Despite the rapid increase in publications between 2016 and 2025, few comparative studies systematically evaluate ML/DL approaches, their effectiveness in specific applications, and their limitations under real-world conditions. To bridge this gap, this review analyzes 95 publications, covering methods from ensemble learners (e.g., Random Forest, XGBoost) to advanced hybrids (e.g., LSTM + MPC). Key influencing factors such as driving style, topography, and weather are considered. This review identifies persistent challenges, including the lack of standardized datasets, limited model generalization, and high computational demands. It also outlines research directions, such as adaptive online learning and integration with V2X technologies. By consolidating current knowledge, this review supports engineers, EV system designers, and policymakers in planning effective energy management and charging strategies, thereby contributing to the sustainable development of electromobility. Full article

VDI/VDE 2206 introduces the “V-Model”, a standard in the field of automotive development that uses systems engineering to derive requirements for (sub-)systems and components based on vehicle characteristics. These characteristics, which are directly experienced by drivers, are crucial in the concept phase, where virtual methods are increasingly applied. Regarding the battery electric vehicle’s energy storage, commonly a lithium-ion battery, vehicle metrics, especially for charging, range, and longitudinal dynamics, are of particular relevance. This publication will demonstrate a method to derive the requirements for the battery system based on those metrics. The core of the method is a static battery model, which considers the needed effects and dependencies in order to adequately represent the defined vehicle metrics, e.g., the battery’s open-circuit voltage and internal resistance. This paper also discusses the necessity of the relevant effects and dependencies and also why some of them can be ignored at this particular vehicle development stage. The result is a consistent method for requirement definition, from vehicle level to battery system level, applicable in the concept phase of the vehicle development process. Full article

Polymer-dispersed liquid crystal (PDLC) smart windows hold significant potential for energy-efficient buildings and vehicles, offering a promising pathway toward carbon neutrality. However, their widespread applications are hindered by critical limitations, including high driving voltages and the inability to achieve programmable patterning or multi-region addressable control. To address these challenges, we propose a pre-orientation strategy via low-voltage electric field (5 V, 1 kHz), which optimizes liquid crystal molecular alignment during the phase separation process. Vertically aligned liquid crystal molecules in the polymer network with enlarged pore structures reduce anchoring energy barriers for LC molecular reorientation, causing a 61.2% reduction in threshold voltage (V_th) from 20.6 V to 8.0 V. Crucially, a programmable patterned PDLC film is successfully fabricated by utilizing cost-effective photomasks. Due to the different V_th of the corresponding regions, the patterned PDLC film exhibits stepwise control modes of light transmission: patterned scattering state, patterned transparent state and total transparent state, driven by incremental voltages. Our method can achieve not only energy-efficient tunable patterns for esthetic designs (e.g., logos or images) but also a scalable platform for multi-level optical modulation, which will advance PDLC technology toward low-voltage adaptive smart windows and open avenues for intelligent architectures and broadening their application scenarios. Full article

The potential health risks of DC charging piles to human health were investigated by quantifying the internal electromagnetic exposure level. In this study, the transformer in the DC/DC circuit of a DC charging pile was selected as the radiation source, and two realistic human models (adult and child) were used as exposure subjects. A simulation model, including the vehicle body, charging pile, and transformer, was established using COMSOL(COMSOL Multiphysics 6.2) Multiphysics software to calculate the magnetic induction intensity (B-field) and electric field intensity (E-field) in various organs at distances of 0.1 m, 0.3 m, and 0.6 m from the charging pile. The results show that at 0.1 m, the peak B-field (1.91 µT) and E-field (447 mV/m) in the adult body were 1.91% and 2.07% of the ICNIRP occupational exposure limits, respectively, and 7.07% and 4.14% of the public exposure limits. For the child model, the peak electromagnetic exposure levels (2.31 µT and 259 mV/m) were only 8.56% and 2.40% of the public limits. Further evaluation of exposure levels for in-vehicle occupants during charging showed that the peak B-field and E-field for an adult driver and a child in the front passenger seat were 0.0225 × 10⁻² µT, 0.0237 × 10⁻² µT, 5.81 mV/m, and 5.82 mV/m, respectively, far below the ICNIRP public limits. Additionally, analyses at multiple frequency bands (85 kHz, 90 kHz, and 95 kHz) under a typical scenario (adult at 0.1 m from the charging pile) revealed that the B-field in the human body decreased with increasing frequency, while the E-field showed minimal variation due to shielding effects. All electromagnetic exposure levels were below both ICNIRP public and occupational limits, indicating the broad applicability of the results. Under normal operating conditions of DC charging piles, the electromagnetic exposure from the DC/DC transformer fully complies with safety standards and poses no threat to human health. This study provides a scientific basis for alleviating public concerns about the health risks of electromagnetic radiation from DC charging piles for electric vehicles. Full article

Among other uses, DC-DC converters are employed in the auxiliary power modules (APMs) of electric vehicles (EVs), connecting the high-voltage traction battery to the low-voltage auxiliary system (AS). Traditionally, the APM is an isolated two-port, two-level (2L) DC-DC converter, and the auxiliary loads are fed at a fixed voltage level, e.g., 12 V in passenger cars. Dual-active-bridge (DAB) converters are commonly used for this application, as they provide galvanic isolation, high power density and efficiency, and bidirectional power flow capability. However, the auxiliary loads do not present a uniform optimum supply voltage, hindering overall efficiency. Thus, a more flexible approach, providing multiple supply voltages, would be more suitable for this application. Multiport DC-DC converters capable of feeding auxiliary loads at different voltage levels are a promising alternative. Multilevel neutral-point-clamped (NPC) DAB converters offer several advantages compared to conventional two-level (2L) ones, such as greater efficiency, reduced voltage stress, and enhanced scalability. The series connection of the NPC DC-link capacitors enables a multiport configuration without additional conversion stages. Moreover, the modular nature of the ML NPC DAB converter enables scalability while using semiconductors with the same voltage rating and without requiring additional passive components, thereby enhancing the converter’s power density and efficiency. This paper proposes a modulation strategy and decoupled closed-loop control strategy for the generalized multiport 2L-NL NPC DAB converter interfacing the EV traction battery with the AS, and its performance is validated through hardware-in-the-loop testing and simulations. The proposed modulation strategy minimizes conduction losses in the converter, and the control strategy effectively regulates the LV battery modules’ states of charge (SoC) by varying the required SoC and the power sunk by the LV loads, with the system stabilizing in less than 0.5 s in both scenarios. Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. 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

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

Electric buses (E-buses) are gaining popularity in urban transportation due to their environmental benefits and operational efficiency. However, large-scale integration of E-buses and Vehicle-to-Grid (V2G) technology introduces scheduling complexities for charging and discharging operations arising from uncertainties in energy consumption and load reduction requests. While prior studies have explored electric vehicle scheduling, few have considered robust optimization for E-bus fleets under uncertain parameters such as trip energy consumption and load reduction requests. This paper proposes a robust optimization approach for the charging and discharging scheduling problem at E-bus depots equipped with V2G. The problem is formulated as a robust mixed-integer linear program (MILP), incorporating real-world operational constraints including dual-port chargers, emergency charging, and demand response. A budgeted uncertainty set is used to model uncertainty in energy consumptions and discharging requests, providing a balance between robustness and conservatism. To ensure tractability, the robust counterpart is reformulated into a solvable MILP using duality theory. The effectiveness of the proposed model is validated through extensive computational experiments, including simulation-based performance assessments and out-of-sample tests. Experiment results demonstrate superior profitability and reliability compared to deterministic and box-uncertainty models, highlighting the practical effectiveness of the proposed approach. Full article

The advent of the lithium-ion batteries (LIBs) has transformed the energy storage field, leading to significant advances in electronics and electric vehicles, which continuously demand more and more performant devices. However, commercial LIB systems are still far from satisfying applications operating in arduous conditions, such as temperatures exceeding 100 °C. For instance, safety issues, materials degradation, and toxic stem development, related to volatile, flammable organic electrolytes, and thermally unstable salts (LiPF₆), limit the operative temperature of conventional lithium-ion batteries, which only occasionally can exceed 50–60 °C. To overcome this highly challenging drawback, the present study proposes advanced electrolyte technologies based on innovative, safer fluids such as ionic liquids (ILs). Among the IL families, we have selected ionic liquids based on tetrabutylphosphonium and 1-ethyl-3-methyl-imidazolium cations, coupled with per(fluoroalkylsulfonyl)imide anions, for standing out because of their remarkable thermal robustness. The thermal behaviour as well as the ion transport properties and electrochemical stability were investigated even in the presence of the lithium bis(trifluoromethylsulfonyl)imide salt. Conductivity measurements revealed very interesting ion transport properties already at 50 °C, with ion conduction values ranging from 10⁻³ and 10⁻² S cm⁻¹ levelled at 100 °C. Thermal robustness exceeding 150 °C was detected, in combination with anodic stability above 4.5 V at 100 °C. Preliminary cycling tests run on Li/LiFePO₄ cells at 100 °C revealed promising performance, i.e., more than 94% of the theoretical capacity was delivered at a current rate of 0.5C. The obtained results make these innovative electrolyte formulations very promising candidates for high-temperature LIB applications and advanced energy storage systems. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution. Full article