Search Results (125)

While electric unmanned aerial vehicles (UAVs) offer advantages in noise reduction, safety, and operational efficiency, their endurance is limited by current battery technology. Extending flight autonomy without compromising performance is a critical challenge in UAV system development. Previous studies introduced hybrid micro-turbogenerator architectures, but limitations in control stability and output power constrained their practical implementation. This study aimed to finalize the design and experimental validation of an optimized hybrid power system featuring a micro-turboprop engine mechanically coupled to an upgraded electric generator. A fuzzy logic-based control algorithm was implemented on a single-board computer to enable autonomous voltage regulation. The test bench architecture was reinforced and instrumented to allow stable multi-stage testing across increasing power levels. Results demonstrated stable voltage control at 48 VDC and electrical power outputs up to 3 kW, with an estimated maximum of 3.5 kW at full throttle. Efficiency was calculated at approximately 67%, and analysis of the generator’s KV constant revealed that using a lower KV variant (KV80) could reduce required rotational speed (RPM) and improve performance. These findings underscore the value of adaptive hybridization in UAVs and suggest that tuning generator electromechanical parameters can significantly enhance overall energy efficiency and platform autonomy. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

This study addresses the Plug-in Hybrid Electric Vehicle Routing Problem (PHEVRP), an extension of the classical VRP that incorporates energy mode switching and partial charging strategies. We propose a novel routing model that integrates three energy modes—fuel-only, electric-only, and hybrid—along with partial recharging decisions to enhance energy flexibility and reduce operational costs. To overcome the computational challenges of large-scale instances, a Dantzig–Wolfe decomposition algorithm is designed to efficiently reduce the solution space via column generation. Experimental results demonstrate that the hybrid-mode with partial charging strategy consistently outperforms full-charging and single-mode approaches, especially in clustered customer scenarios. To further evaluate algorithmic performance, an Ant Colony Optimization (ACO) heuristic is introduced for comparison. While the full model fails to solve instances with more than 30 customers, the DW algorithm achieves high-quality solutions with optimality gaps typically below 3%. Compared to ACO, DW consistently provides better solution quality and is faster in most cases, though its computation time may vary due to pricing complexity. Full article

Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LiBs) is critical for ensuring battery reliability and safety in applications such as electric vehicles and energy storage systems. However, existing methods developed for estimating the SOH and RUL of LiBs often rely on full-cycle charging data, which are difficult to obtain in engineering practice. To bridge this gap, this paper proposes a novel data-driven method to estimate the SOH and RUL of LiBs only using partial charging curve features. Key health features are extracted from the constant voltage (CV) charging process and voltage relaxation, validated through Pearson correlation analysis and SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost for SOH estimation and particle swarm optimization-support vector regression (PSO-SVR) for RUL estimation is developed. Experimental validation on public datasets demonstrates superior performance of the methodology described above, with an SOH estimation root mean square error (RMSE) and mean absolute error (MAE) below 1.42% and 0.52% and RUL estimation relative error (RE) under 1.87%. The proposed methodology also exhibits robustness and computational efficiency, making it suitable for battery management systems (BMSs) of LiBs. 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

Hybrid excited machines are strong competitors for application in hybrid/full electric vehicles due to their high torque density and strong air gap field-regulating capability. Similar to armature back-EMF, back-EMF also exists in the field windings of hybrid excited machines. However, the existence of field back-EMF is harmful to the safe and stable operation of machine systems, e.g., lower efficiency, higher torque ripple, reduced control performance, etc. In this paper, the influence of the hybridization ratio k, i.e., the ratio of the field winding slot area to the total field slot area, on the field back-EMF in hybrid excited machines with a switched flux stator is comprehensively investigated. In addition, a comparative study of the field back-EMF ripple in hybrid excited machines and wound field synchronous machines is conducted. It shows that the field back-EMF in flux-enhancing, zero field current, and flux-weakening modes is significantly affected by the hybridization ratio under different conditions. Moreover, the on-load field back-EMF in wound field machines is considerably higher than that in hybrid excited machines due to the mitigated magnetic saturation level in the field winding’s magnetic flux path. Finally, to validate the results predicted using the finite element method, a prototype hybrid excited machine is built and tested. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in both civilian and military applications around the world. There are several types of UAVs with classifications according to several quantities. Medium-Altitude Long-Endurance (MALE) UAVs comprise one of these classifications. Hybrid or electric propulsion systems are another topic that is becoming popular. Implementing electric propulsion systems in vehicles could result in more efficient, environmentally friendly, and improved systems in comparison with conventional systems. This concept can be seen in the automotive sector, and today, it is popular in the aviation sector. Based on a literature review, full-electric concepts are often applied to some classes of UAVs. MALE-class UAVs are often used with conventional propulsion systems, as they need a long endurance during flight. It is known that current battery technologies and weight limitations on board do not allow as long of a flight time as conventional systems. Even knowing this, there could be some advantages to choosing an electric propulsion system in MALE-class UAVs. The effects and performance of electric propulsion in MALE-class UAVs were studied with a newly designed electric machine and a newly created UAV model. Full article

In cold environments, traditional independent thermal management systems heavily rely on inefficient Positive Temperature Coefficient (PTC) heaters, which exacerbate range anxiety in vehicles. In this study, an energy management-based control strategy for an integrated thermal management system (ITMS) designed for hybrid electric vehicles (HEVs) is proposed. By coupling the four thermal flow circuits of the entire vehicle and integrating driving modes with heating demands, this strategy achieves full vehicle-level integrated control. Through optimizing the distribution and utilization of heat within the vehicle, this enhances the heating performance of the air source heat pump. The simulation results demonstrate that the proposed strategy significantly reduces the power consumption of the heat pump and improves heating efficiency for both the battery and the cabin. By utilizing waste heat from the motor and the engine, the ITMS increases the heating capacity of the heat pump, particularly in low-temperature environments. Compared to traditional thermal management systems, the ITMS control strategy achieves substantial improvements in both heating time and energy efficiency. Specifically, the system reduces battery heating time by 55.94% and enhances the overall heating performance of the vehicle. Furthermore, the strategy reduces fuel consumption by 5.18%, demonstrating its potential to improve the energy efficiency of HEVs in cold climates. Full article

Lithium-ion batteries (LIBs) play a critical role in electric vehicles (EVs) and hybrid electric vehicles (HEVs) and degradation of LIBs influences lifetime, reliability, safety and dependability. The ability to assess and quantify degradation enables assessment of LIB’s true state of health. This paper investigates LIB degradation using a pseudo two-dimensional (P2D) model, particularly focusing on the changes to Electrochemical Impedance spectroscopy (EIS) results due to degradation. Three key degradation mechanism are considered and the impact of State-of-Charge (SoC) and temperature on EIS results are discussed. This paper also identifies the need for a more realistic approach to assess degradation. Simulations are conducted considering four repetitive standard drive cycles (viz., HTDDT, HWFET, US06 and OCTBC) for a vehicle travel distance of 150,000 km for each case. The cycle counting method is used to convert partial SoC variations during a drive cycle to an equivalent full cycle count which is then used within the degradation model to modify the parameters to represent the P2D model. This study demonstrates a robust process for analyzing degradation dynamics. The methodology presented here can guide future researchers with experimental data, enabling validation and refinement of model parameters to advance LIB degradation analysis and improve battery life predictions under operational scenarios. Full article

The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions. Full article

The imperative for sustainable development demands innovative solutions to address the environmental impact of transportation, particularly in the context of climate change. This study explores the intersection of renewable energy and sustainability within the automotive industry by examining how restrictions on the sale of gasoline-powered vehicles affect patent filings related to automotive engine technologies. Our approach employs a factorial analysis to capture structural shifts in patent filings, utilizing the logarithmic mean Divisia index for index decomposition. We scrutinize patent trends from 1985 to 2019, with a focus on both non-green (internal combustion engine vehicles) and green technologies (battery electric, hybrid electric, and full cell vehicles), to assess the transition towards sustainable transportation. This study also scrutinizes the patenting activities of four major patent offices—China, Japan, the United States, and Germany—to unveil global trends and disparities in sustainable technology innovation. Our findings underscore how a nation’s green investment strategy is contingent upon its developmental stage, with intellectual property rights and R&D incentives playing pivotal roles in shaping R&D landscapes, especially in emerging economies with nascent intellectual property markets. This study also reveals varying strategies for developing green automotive engines across nations, indicating that the growth of green patents in developed countries is probably spurred by financial incentives and enhanced intellectual property rights to promote specific tech advancements. This research contributes to the discourse on sustainability by highlighting the critical role of policy in fostering green technology development and the importance of aligning patent strategies with environmental goals. Full article

Unmanned Aerial Vehicle (UAV) development has garnered significant attention, yet one of the major challenges in the field is how to rapidly iterate the overall design scheme of UAVs to meet actual needs, thereby shortening development cycles and reducing costs. This study integrates a “Decision Support System” and “Live Virtual Construct (LVC) environment” into the existing Model-Based Systems Engineering framework, proposing a Modified Model-Based Systems Engineering methodology for the full-process development of UAVs. By constructing a decision support system and a hybrid reality space—which includes pure digital modeling and simulation analysis software, semi-physical simulation platforms, real flight environments, and virtual UAVs—we demonstrate this method through the development of the electric vertical take-off and landing fixed-wing UAV DB1. This method allows for rapid, on-demand iteration in a fully digital environment, with feasibility validated by comparing actual flight test results with mission indicators. The study results show that this approach significantly accelerates UAV development while reducing costs, achieving rapid development from “demand side to design side” under the “0 loss” background. The DB1 platform can carry a 2.5 kg payload, achieve over 40 min of flight time, and cover a range of more than 70 km. This work provides valuable references for UAV enterprises aiming to reduce costs and increase efficiency in the rapid commercialization of UAV applications. Full article

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. Full article

Automotive-grade GaN power switches have recently been made available in the market from a growing number of semiconductor suppliers. The exploitation of this technology enables the development of very efficient power converters operating at much higher switching frequencies with respect to components implemented with silicon power devices. Thus, a new generation of automotive power components with an increased power density is expected to replace silicon-based products in the development of higher-performance electric and hybrid vehicles. 650 V GaN-on-silicon power switches are particularly suitable for the development of 3–7 kW on-board battery chargers (OBCs) for electric cars and motorcycles with a 400 V nominal voltage battery pack. This paper describes the design and implementation of a 6.6 kW OBC for electric vehicles using automotive-grade, 650 V, 25 mΩ, discrete GaN switches. The OBC allows bi-directional power flow, since it is composed of a bridgeless, interleaved, totem-pole PFC AC/DC active front end, followed by a dual active bridge (DAB) DC-DC converter. The OBC can operate from a single-phase 90–264 Vrms AC grid to a 200–450 V high-voltage (HV) battery and also integrates an auxiliary 1 kW DC-DC converter to connect the HV battery to the 12 V battery of the vehicle. The auxiliary DC-DC converter is a center-tapped phase-shifted full-bridge (PSFB) converter with synchronous rectification. At the low-voltage side of the auxiliary converter, 100 V GaN power switches are used. The entire OBC is liquid-cooled. The first prototype of the OBC exhibited a 96% efficiency and 2.2 kW/L power density (including the cooling system) at a 60 °C ambient temperature. Full article

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