Search Results (6,976)

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

Effective communication between electric autonomous vehicles (EAVs) and pedestrians is critical for safety, yet the absence of a driver removes traditional cues such as eye contact or gestures. While external human–machine interfaces (eHMIs) have been proposed, few studies have systematically compared visual formats across demographic groups and validated findings in both simulation and real-world settings. This study addresses this gap by evaluating various eHMI designs using combinations of textual cues (“WALK” and “CROSS”), symbolic indicators (pedestrian and arrow icons), and display colors (white and green). Twenty simulated scenarios were developed in the CARLA simulator, where 100 participants observed an EAV equipped with eHMIs and responded by pressing a button upon understanding the vehicle’s intention. The results showed that green displays facilitated faster comprehension than white, “WALK” was understood more quickly than “CROSS,” and pedestrian symbols outperformed arrows in clarity. The fastest overall comprehension occurred with the green pedestrian symbol paired with the word “WALK.” A subsequent field experiment using a Level 3 autonomous vehicle with a smaller participant group and differing speed/distance conditions provided preliminary support for the consistency of these observed trends. The novelty of this work lies in combining simulation with preliminary field validation, using comprehension time as the primary metric, and comparing results across four age groups to derive evidence-based eHMI design recommendations. These findings offer practical guidance for enhancing pedestrian safety, comprehension, and trust in EAV–pedestrian interactions. Full article

Lithium-ion batteries are pivotal components in battery electric vehicles, significantly influencing vehicle design and performance. This study investigates the interactions between cell properties and battery pack characteristics through statistical correlation analysis of datasets derived from industry-leading benchmarking platforms. Findings indicate that energy densities are comparable across cell formats at the pack level. While NMC and NCA chemistries outperform LFP in energy density at both cell and pack levels, LFP’s favorable cell-to-pack factors mitigate these differences. Analysis of cell properties suggests that increases in cell-level volumetric and gravimetric energy density result in proportionally smaller gains at the pack level due to the growing proportion of required passive components. The impact of cell chemistry and format on the z-dimension of a battery pack is analyzed in order to identify dependencies and influences between nominal cell properties and the geometry of the battery pack. The analysis suggests no significant influence of the used cell chemistry on the vertical dimension of a battery pack. The consideration of cell formats shows a dependency between the battery pack z-dimension and cell geometry, with prismatic cells reaching the highest pack heights and cylindrical cells being observed in packs of smaller vertical dimensions. The study also investigates the emerging sodium-ion battery technology and assesses pack-level energy densities derived from cell-level properties. The insights of this study contribute to the understanding of cell-to-pack relationships, guiding R&D toward improved energy storage solutions for electric vehicles. Full article

The growing concern about climate change and increased carbon emissions has promoted the electric vehicle market. Lithium-Ion Batteries (LIBs) are now the prevailing technology in electromobility, and large amounts will soon reach their end-of-life (EoL). Most counties have not designed sustainable reverse logistics networks to collect, remanufacture and recycle EoL electric vehicle batteries (EVBs). This study is focused on estimating the future EoL LIBs generation through dynamic material flow analysis using a three parameter Weibull distribution function under two scenarios for battery lifetime and then designing a reverse logistics network for the region of Attica (Greece), based on a generalizable modeling framework, to handle the discarded batteries up to 2040. The methodology considers three different battery handling strategies such as recycling, remanufacturing, and disposal. According to the estimated LIB waste generation in Attica, the designed network would annually manage between 5300 and 9600 tons of EoL EVBs by 2040. The optimal location for the collection and recycling centers considers fixed costs, processing costs, transportation costs, carbon emission tax and the number of EoL EVBs. The economic feasibility of the network is also examined through projected revenues from the sale of remanufactured batteries and recovered materials. The resulting discounted payback period ranges from 6.7 to 8.6 years, indicating strong financial viability. This research underscores the importance of circular economy principles and the management of EoL LIBs, which is a prerequisite for the sustainable promotion of the electric vehicle industry. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

The use of the Industrial Internet of Things within the domain of electric vehicles signifies a paradigm shift toward advanced, integrated, and optimized transport systems. This study thoroughly investigates the pivotal role of the Industrial Internet of Things in elevating various features of electric vehicle technology, comprising predictive maintenance, vehicle connectivity, personalized user management, energy and fleet optimization, and independent functionalities. Key IIoT applications, such as Vehicle-to-Grid integration and advanced driver-assistance systems, are examined alongside case studies highlighting real-world implementations. The findings demonstrate that IIoT-enabled advanced charging stations lower charging time, while grid stabilization lowers electricity demand, boosting functional sustainability. Battery Management Systems (BMSs) prolong battery lifespan and minimize maintenance intervals. The integration of the IIoT with artificial intelligence (AI) optimizes route planning, driving behavior, and energy consumption, resulting in safer and more efficient autonomous EV operations. Various issues, such as cybersecurity, connectivity, and integration with outdated systems, are also tackled in this study, while emerging trends powered by artificial intelligence, machine learning, and emerging IIoT technologies are also deliberated. This study emphasizes the capacity for IIoT to speed up the worldwide shift to eco-friendly and smart transportation solutions by evaluating the overlap of IIoT and EVs. Full article

Advanced transportation, especially electric transportation, plays an increasingly significant role in the reduction of CO₂ emissions in urban traffic. A life-cycle CO₂ emission model in which traditional fossil fuels and electricity are considered is a key method to analyze the potential of transportation emission reduction. In this study, the life-cycle CO₂ emissions of gasoline, diesel, natural gas, and electricity generated during the production, transportation, and consumption were modeled and calculated. The influence of coal power generation, coal combustion, seasonal energy consumption, and travel patterns on the CO₂ emissions of electric vehicles was discussed. The analysis results show that the life-cycle CO₂ emissions of automobile fuels in the process of combustion, processing, mining, and transportation are from the largest to the smallest. If the proportion of coal power generation is reduced to 50% by replacing gasoline vehicles with electric vehicles, emissions can be reduced by about 48.2%. At the same time, the scale of traffic in different months and in different periods of time of the day causes seasonal energy consumption fluctuations and regular fuel consumption variations of electric vehicles. The cyclical carbon reduction effect can be amplified if measures such as replacing fuel cars in spring and fall, and during peak hours, are used. Full article

Hydrogen-fueled internal combustion engines present a promising pathway for reducing carbon emissions in urban transportation by allowing for the reuse of existing vehicle platforms while eliminating carbon dioxide emissions from the exhaust. However, operating these engines with lean air–fuel mixtures—necessary to reduce nitrogen oxide emissions and improve thermal efficiency—leads to significant reductions in power output due to the low energy content of hydrogen per unit volume and slower flame propagation. This study investigates whether integrating a mild hybrid electric system, operating at 48 volts, can mitigate the performance losses associated with lean hydrogen combustion in a small passenger vehicle. A complete simulation was carried out using a validated one-dimensional engine model and a full zero-dimensional vehicle model. A Design of Experiments approach was employed to vary the electric motor size (from 1 to 15 kW) and battery capacity (0.5 to 5 kWh) while maintaining a fixed system voltage, optimizing both the component sizing and control strategy. Results showed that the best lean hydrogen hybrid configuration achieved reductions of 18.6% in energy consumption in the New European Driving Cycle and 5.5% in the Worldwide Harmonized Light Vehicles Test Cycle, putting its performance on par with the gasoline hybrid benchmark. On average, the lean H₂ hybrid consumed 41.2 kWh/100 km, nearly matching the 41.0 kWh/100 km of the gasoline P0 configuration. Engine usage analysis demonstrated that the mild hybrid system kept the hydrogen engine operating predominantly within its high-efficiency region. These findings confirm that lean hydrogen combustion, when supported by appropriately scaled mild hybridization, is a viable near-zero-emission solution for urban mobility—delivering competitive efficiency while avoiding tailpipe CO₂ and significantly reducing NO_x emissions, all with reduced reliance on large battery packs. Full article

The reliability of the EDS (Electric Drive System) in electric vehicles is crucial to overall vehicle performance. This study addresses the challenge of acquiring high-fidelity internal load data in the early development phase due to the absence of prototypes, overcoming the limitations of traditional road tests, which are costly, time-consuming, and unable to measure gear meshing forces. A method based on a VPG (Virtual Proving Ground) is proposed to acquire internal loads of a dual-motor EDS, analyze the impact of typical virtual fatigue durability road conditions on critical components, and generate load spectra for test bench experiments. Through point cloud data-based road modeling and rigid-flexible coupled simulation, dynamic loads are accurately extracted, with pseudo-damage contributions from eight intensified road conditions quantified using pseudo-damage calculations, and equivalent sinusoidal load spectra generated using the rainflow counting method and linear cumulative damage theory. Compared to the limitations of existing VPG methods that rely on simplified models, this study enhances the accuracy of internal load extraction, providing technical support for EDS durability testing. Building on existing research, it focuses on high-fidelity acquisition of EDS loads and load spectrum generation, improving applicability and addressing deficiencies in simulation accuracy. This study represents a novel application of VPG technology in electric drive system development, resolving the issue of insufficient early-stage load spectra. It provides data support for durability optimization and bench testing, with future validation planned using real vehicle data. Full article

This paper aims to explore Virtual Power Plants (VPPs) in combination with Demand Response (DR) concepts, integrating solar power generation, Electric Vehicle (EV) charging and discharging, and user loads to establish an optimal energy management scheduling system. Willingness curves for load curtailment are derived based on the consumption patterns of industrial, commercial, and residential users, enabling VPPs to design DR mechanisms under Time-of-Use (TOU), two-stage, and critical peak pricing periods. An energy management model for a VPP is developed by integrating DR, EV charging and discharging, and user loads. To solve this model and optimize economic benefits, this paper proposes an Improved Wolf Pack Search Algorithm (IWPSA). Based on the original Wolf Pack Search Algorithm (WPSA), the Improved Wolf Pack Search Algorithm (IWPSA) enhances the key behaviors of detection and encirclement. By reinforcing the attack strategy, the algorithm achieves better search performance and improved stability. IWPSA provides a parameter optimization mechanism with global search capability, enhancing searching efficiency and increasing the likelihood of finding optimal solutions. It is used to simulate and analyze the maximum profit of the VPP under various scenarios, such as different seasons, incentive prices, and DR periods. The verification analysis in this paper demonstrates that the proposed method can not only assist decision makers in improving the operation and scheduling of VPPs, but also serve as a valuable reference for system architecture planning and more effectively evaluating the performance of VPP operation management. Full article

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO₂ emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

To meet the demands for weight reduction and cost efficiency, the design of interior permanent magnet synchronous motors (IPMSMs) for electric vehicles is inevitably evolving toward high-speed operation, compactness, and improved efficiency. This paper proposes and analyzes a novel hybrid winding design that combines hair-pin coils and litz wire coils. The objective of this hybrid approach is to leverage the high-fill-factor advantage of hair-pin windings while mitigating the increased high-frequency copper losses caused by skin effect—an area where litz wire excels. Finite element analysis (FEA) is used to evaluate and compare the performance of the proposed hybrid stator design against conventional winding configurations, including round wire windings and pure rectangular windings. Key factors such as fill factor and skin effect are thoroughly considered in the analysis. Additionally, a system-level evaluation is conducted based on assumed electric vehicle parameters, providing a comprehensive assessment of efficiency and energy losses under real-world operating conditions. Full article

Hybrid electric vehicles (HEVs) have gained significant attention for their superior energy efficiency and are becoming a predominant mode of urban transportation. The DC/DC converter plays a critical role in HEV energy management systems, especially in matching the voltage levels between the battery and DC bus. This paper proposes a novel high-gain DC/DC converter with a wide input voltage range based on coupled inductors. The innovation lies in the integration of a resonant cavity and the simultaneous realization of zero-voltage switching (ZVS) and zero-current switching (ZCS), effectively reducing both voltage/current stresses on the power switches and switching losses. Compared with conventional topologies, the proposed design achieves higher voltage gain without extreme duty cycles, improved conversion efficiency, and enhanced reliability. Detailed operating principles are analyzed, and design conditions for voltage stress reduction, gain extension, and soft switching are derived. The simulation model has been conducted in a PSIM environment, and a 300 W experimental prototype, implemented using a dsPIC33FJ64GS606 digital controller, has been established and demonstrates 93% peak efficiency at a 10 times voltage gain. The performance and practical feasibility of the proposed topology have been evaluated by both simulation and experiments. Full article

Aerospace engines and hypersonic vehicles, among other high-temperature components, often operate in environments characterized by temperatures exceeding 1000 °C and high-speed airflow impacts, resulting in severe thermal erosion conditions. Coaxial thermocouples (CTs), with their unique self-eroding characteristic, are particularly well suited for use in such extreme environments. However, fabricating high-temperature electrical insulation layers for coaxial thermocouples remains challenging. Inspired by the self-healing mechanism of pine trees, we designed a composite electrical insulation layer with a similar self-healing function. This composite layer exhibits excellent high-temperature insulation properties (insulation resistance of 14.5 kΩ at 1200 °C). Applied as the insulation layer in K-type coaxial thermocouples via dip-coating, the thermocouples were tested for temperature and heat flux. Temperature tests showed an accuracy of 1.72% in the range of 200–1200 °C, a drift rate better than 0.474%/h at 1200 °C, and hysteresis better than 0.246%. The temperature response time was 1.08 ms. Heat flux tests demonstrated a measurable range of 0–41.32 MW/m² with an accuracy better than 6.511% and a heat flux response time of 7.6 ms. In simulated extreme environments, the K-type coaxial thermocouple withstood 70 s of 900 °C flame impact and 50 cycles of high-power laser thermal shock. Full article