Search Results (797)

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for realistic journeys performed by car, a simulation approach is proposed here, using travel data collected from real vehicles to obtain trip chains for multiple consecutive days. Car travel activities, including stops with the option of charging, were simulated by applying an agent-based approach. Charging operations can be integrated into trip chains for user activities, assuming that they remain unchanged in the event that vehicles switch to electric. The energy consumption of the analyzed trips, disaggregated by vehicle type, was estimated using the average travel speed, which is useful for capturing the main route features (ranging from urban to motorways). Data were recorded for approximately 25,000 vehicles in the Turin Metropolitan Area for six consecutive days. Market segmentation of the vehicles was introduced to take into consideration different energy consumption rates and charging times, given that the electric power, battery size, and consumption rate can be related to the vehicle category. Charging activities carried out using public infrastructure during idle time between consecutive trips, as well as those carried out at home or work, were identified in order to model different needs. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part of new electrification projects, resulting in a significant demand for power. To enable a significant increase in electric transportation without compromising technical compliance for voltage and current at grid systems, the implementation of stationary battery energy storage systems (BESSs) can be essential for new electrification projects. A key challenge for BESSs is the selection of the optimal converter topology for charging their batteries. Ideally, the chosen converter should offer the highest efficiency while minimizing size, weight, and cost. In this context, a modular dual-active-bridge converter, considering its operation as a full-power converter (FPC) and a partial-power converter (PPC) with module-shedding control, is analyzed in terms of operation efficiencies and thermal behavior. The goal is to clarify the advantages, disadvantages, challenges, and trade-offs of both power-processing techniques following future trends in the electric transportation sector. The results indicate that the PPC achieves an efficiency of 98.58% at the full load of 100 kW, which is 1.19% higher than that of FPC. Additionally, higher power density and cost effectiveness are confirmed for the PPC. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

In air-conditioning compressors operating under ultra-low temperature conditions, both the rotational speed and load torque are at high levels, demanding pump motors that offer high efficiency and high power at high speeds. Electrically excited synchronous motors (EESMs) satisfy these operational requirements by leveraging their inherent wide-speed field-weakening capability and superior high-speed performance characteristics. Current research on EESM primarily targets electric vehicle applications, with a high-efficiency design focused on medium and low speeds. Excitation design under constant-power–speed extension remains insufficiently explored. To address it, this paper proposes an EESM design methodology optimized for high-speed efficiency and constant-power excitation control. Key EESM parameters are determined through a dynamic phasor diagram, and design methods for turn number, split ratio, and other parameters are proposed to extend the high-efficiency region into the high-speed range. Additionally, a power output modulation strategy in the field-weakening region is introduced, enabling dynamic high-power regulation at high speed through excitation adjustment. Compared to similarly sized PMSMs, the proposed EESM exhibits consistently superior efficiency beyond 10,000 rpm, delivering 19% and 49% higher power output at 12,000 rpm and 14,000 rpm, respectively, relative to conventional pump-drive PMSMs. Experimental validation via a prototype confirms excellent high-speed efficiency and sustained constant-power performance, in alignment with the design targets. Full article

Urban bus fleets worldwide face urgent decarbonization requirements, with Germany targeting net-zero emissions by 2050. Current electrification research often addresses individual components—energy consumption, scheduling, or charging infrastructure—in isolation, lacking integrated frameworks that capture complex system interactions. This study presents “eflips-X”, a modular, open-source simulation framework that integrates energy consumption modeling, battery-aware block building, depot–block assignment, terminus charger placement, depot operations simulation, and smart charging optimization within a unified workflow. The framework employs empirical energy models, graph-based scheduling algorithms, and integer linear programming for depot assignment and smart charging. Applied to Berlin’s bus network—Germany’s largest—three scenarios were evaluated: maintaining existing blocks with electrification, exclusive depot charging, and small batteries with extensive terminus charging. Electric fleets need 2.1–7.1% additional vehicles compared to diesel operations, with hybrid depot-terminus charging strategies minimizing this increase. Smart charging reduces peak power demand by 49.8% on average, while different charging strategies yield distinct trade-offs between infrastructure requirements, fleet size, and operational efficiency. The framework enables systematic evaluation of electrification pathways, supporting evidence-based planning for zero-emission public transport transitions. Full article

The rapid development of electric transport necessitates efficient energy storage and redistribution in traction systems. A key challenge is the utilization of regenerative braking energy, which is often dissipated in resistors due to network saturation and limited consumption capacity. The paper addresses the problem of inefficient energy utilization in electric rail vehicles due to the absence of effective energy recovery mechanisms. A specific challenge arises when managing energy recuperated during regenerative braking, which is typically lost if not immediately reused. This study proposes the integration of on-board energy storage systems (ESS) based on supercapacitor technology to temporarily store excess braking energy. A mathematical model of a traction drive with a DC motor and supercapacitor-based ESS is developed, accounting for variable load profiles and typical urban driving cycles. Simulation results demonstrate potential energy savings of up to 30%, validating the feasibility of the proposed solution. The model also enables system-level analysis for optimal ESS sizing and placement in electric rail vehicles. Full article

Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller design often leads to suboptimal HEVTOL performance. To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. Evaluated using representative HEVTOL duty cycles, the HM-BEO demonstrates significant improvements in optimization efficiency and solution quality compared to conventional methods. Specifically, it achieves a 5.3% improvement in fuel economy, a 7.4% mitigation in battery SOH degradation, and a 1.7% reduction in system manufacturing cost compared to standard NSGA-III-based optimization. Full article

With the proliferation of electric vehicles (EVs), vehicle-to-vehicle (V2V) energy transfer has emerged as a critical technology for dynamic energy complementarity. This technology addresses “range anxiety”, thereby supporting carbon neutrality goals through the enhanced utilization of renewable-powered EVs. In order to achieve fast, safe V2V charging and improve device portability, it is necessary to optimize the charging mode and simplify the device. Therefore, this paper proposes a hierarchical control strategy for constant power (CP) charging in a V2V device with a dual-active-bridge (DAB) converter topology. First, different from traditional constant voltage (CV) and constant current (CC) charging, a unified nonlinear DAB model integrating CV/CP/CC charging modes is proposed. Furthermore, sensorless current estimation based on finite-time disturbance observers further reduced the size of the device. Finally, a hierarchical control architecture was constructed by combining backstepping control theory, which ensures global stability of multi-stage charging processes through the dynamic adjustment of phase-shift ratios. The effectiveness of the proposed methodology was validated through simulation and hardware-in-the-loop experimental results. Full article

The battery pack is a critical component of electric vehicles, with lithium-ion cells being a frequently preferred choice. Lithium-ion cells are known for long life, high power and energy density, and are reliable for a broad range of temperatures. However, these batteries have a drawback of over-voltage, under-voltage, thermal runaway, and especially, state of charge or voltage imbalance. Among these, the cell imbalance is particularly important because it causes an uneven power dissipation in each cell, resulting in non-uniform temperature distribution. This uneven temperature distribution negatively affects the lifetime and efficiency of a battery pack. Cell imbalance is mitigated by cell balancing techniques, of which several methods have been presented over the last few years. These methods consider different power electronics circuits and control approaches to optimise cell balancing characteristics. This paper reviews basic to advanced cell balancing techniques and compares their circuit designs, costs, switching stresses, complexity, sizes, and control techniques to highlight the recent trends and future directions. This paper also compares the recent trend of machine learning integration with basic cell balancing topologies and provides a critical analysis of the outcomes. Full article

This paper presents a methodology for systematically scaling vehicle powertrain and other models and an approach for using model parameters and scaling variables to perform controller design. The parameter scaling method allows for high degrees of scaling while maintaining the target performance metrics, such as vehicle speed tracking, with minimal changes to the model code by the researcher. A comparison of proportional-integral, Youla parameterization, ${\mathrm{\text{H}}}_{\infty }$, and hybrid Youla-${\mathrm{H}}_{\infty }$ controllers is provided, along with the respective methods for maintaining controller performance metrics across degrees of model scaling factors. The application of the scaling and various control design methods to an existing model of a hydrogen fuel cell and a battery electric vehicle powertrain allows for the development of a representative scale model to be compared with experimental data generated by an actual scale vehicle. The comparison between scaled simulation and experimental data will eventually be used to inform the expected performance of the full-size electric vehicle based on full-size simulation results. Full article

Designing propulsion systems for agricultural drones involves a repetitive process that is both expensive and time-intensive. At the same time, conducting comprehensive experimental tests demands specialized equipment and strict safety protocols. In this work, the design and assessment of the propulsion system (propeller, motor, and battery) for large-sized drones in agricultural applications are conducted using numerical methods. To properly predict and validate the performance of a brushless direct current motor, a three half-bridge inverter circuit, featuring a trapezoidal commutation, is implemented and constructed. First, the propeller is studied using the finite volume method, obtaining a maximum variation of 6.32% for thrust and 10.1% for torque. Additionally, an electromagnetic analysis on a commercial brushless direct current motor (BLDC) using JMAG software from JSOL corporation (JMAG designer 23.2, Cd.Obregón, México) resulted in 4.43% deviation from experimental electrical measurements. The selected propulsion system is implemented in a 30 kg drone, where motor performance is evaluated for two instants of time in a typical agriculture trajectory. The findings demonstrate that numerical methods provide valuable insights in large-sized unmanned aerial vehicle (UAV) design, decreasing the experimental tests conducted and accelerating implementation time. Full article

The adoption of heavy-duty electric vehicles (HDEVs) is key to achieving transportation decarbonization. A major component of this transition is the need for new supporting infrastructure: electric charging stations (CSs). HDEV CSs must be planned considering charging requirements, economic constraints, the rollout plan for HDEVs, and local utility grid conditions. Together, these considerations highly differentiate HDEV CS planning from light-duty CS planning. This paper addresses the challenges of HDEV CS planning by presenting a framework for determining the optimal sizing of multiple HDEV CSs using a multi-period expansion model. The framework uses historical data from depots and applies a mixed-approach optimization solver to determine the optimal sizes of two types of CSs: one that relies entirely on power generated by a PV system with local battery storage, and another that relies entirely on utility grid power supply. A two-layer uncertainty model is proposed to account for variations in PV power generation, HDEV arrival/departure times, and charger failures. The multi-period expansion strategy achieves up to a 78% reduction in total annual costs during the first deployment period, compared to fully expanded CSs. Full article

Rare earth elements (REEs), particularly neodymium (Nd), dysprosium (Dy), and praseodymium (Pr), are critical in the production of neodymium–iron–boron (NdFeB) magnets used in electronic devices, wind turbines, and electric vehicles. Due to the limited availability of these metals, their recovery from waste electronic equipment such as hard disk drives (HDDs) offers a promising solution. The aim of this study was to develop a method to grind NdFeB magnets obtained from the physical recycling of HDD. The recycled magnets were ground using a planetary mill. A review of the literature highlights the limitations of the currently used grinding methods, which require energy-intensive pretreatment processes, specialised conditions, or expensive equipment. This study employed a Fritsch planetary mill, tungsten carbide grinding balls, and ethanol as a grinding medium. NdFeB magnet samples (120 g) were ground for different durations (0.5 h–15 h) at a speed of 300 rpm, using a cyclic operating mode to minimise material heating. The resulting powders were analysed using a laser particle analyser, an optical microscope, and an X-ray diffractometer. The results enable the determination of optimal grinding parameters, achieving an average particle size (d₅₀) below 5 μm, which is essential for further processing and new magnet production. Finally, the economic and environmental aspects of producing the neodymium alloy were analysed. Full article