Search Results (494)

This paper presents a novel approach for the recycling of spent anodic plates from lead-acid batteries through the melt quenching method using iron and calcium oxides and iron powder. The resulting recycled samples, with a 3CaO·5Fe₂O₃·xFe·(92 − x)Pb composition, where x = 0, 1, 3, 5, 8, 10, 15, and 25% mol Fe, were characterized and analyzed in terms of their electrochemical performance. X-ray diffractograms show vitroceramic structures with varied crystalline phases. Analysis of the IR (infrared spectra) data shows a decrease of sulphate units due to doping with iron content. The ultraviolet–visible (UV-Vis) and electron spin resonance (ESR) data reveal the presence of Fe³⁺ ions with varied coordination geometries. Cyclic and linear sweep voltammograms demonstrate that the samples with 8 and 10% Fe exhibit superior electrochemical performance compared to other vitroceramics. The electrochemical impedance spectroscopy measurements indicate that the sample with 8% Fe had lower resistance compared to other analogues and had enhanced electrical conductivity. Full article

While the speed of the transition to battery electric vehicles (BEVs) depends on real-world driving behaviors and socioeconomic conditions, relevant predictions are often not based on real trip data. This study analyzes over 200,000 private car trips, tracked via onboard telematics across Italy, in order to assess the feasibility of replacing internal combustion engine vehicles (ICEVs) with BEVs. Given that drivers are resistant to changing their habits, we introduce the E-Private Mobility Index, which quantifies the percentage of traditional cars at present that are functionally compatible with a medium BEV, assuming home charging. Nationwide, this index reaches 30%, but only 15% of car owners would also see financial benefits. By quantifying both the potential to replace traditional cars with electric ones and the associated economic impacts, our analysis supports sustainable mobility by offering insights into the rate of penetration of sustainable and green mobility, in line with the objectives of the European Green Deal. With its unprecedented statistical significance, the study not only provides a data-driven upper threshold of BEV penetration but also offers a flexible framework for shaping future policies, allowing the adaptation of parameters and assumptions to guide a scalable transition to electric private mobility. Full article

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios. Full article

This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation. Full article

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses. Full article

Battery energy storage systems (BESSs) are often used in partial-state-of-charge (PSOC) operation due to the desire for flexibility of charge and discharge. Lead–acid batteries are a good candidate to be used in battery energy storage due to their safety, recyclability, and long cycle life; however, the correct battery, cell, and regime should be chosen to ensure effective use. Manufacturers rarely publish data on PSOC performance of their batteries. During PSOC use, the charge acceptance of lead–acid batteries reduces both reversibly and, sometimes, irreversibly as the battery is cycled. Typical dynamic charge acceptance tests target the performance required in car batteries and do not adequately demonstrate the charge acceptance expected in BESS use. This paper demonstrates a representative charge acceptance degradation test which far more closely replicates the charge acceptance degradation seen in real-world PSOC BESS use using partial state of charge, coulomb control, and a charge-factor-controlled full charge. Full charges are shown to reverse the internal resistance associated with partial-state-of-charge operation. This is the case in the Leoch lead–carbon cells and 12 V battery tested. This shows that partial-state-of-charge operation degrades the charge acceptance and increases the internal resistance of a lead–acid battery, although with a charge-factor-based full-charge approach, the charge acceptance could be reset to baseline. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. Full article

The global adoption of battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) as a substitute for internal combustion engine cars (ICEs) in various nations offers a substantial opportunity to reduce carbon dioxide (CO₂) emissions from land transportation. EVs are fitted with an energy conversion system that efficiently converts stored energy into propulsion, referred to as “tank-to-wheel (TTW) conversion”. Battery-electric vehicles have a significant advantage in that their exhaust system does not produce any pollutants. This hypothesis is equally relevant to public transport. Despite their higher upfront cost, electric buses contribute significantly to environmental sustainability during their operation. This study aimed to evaluate the environmental sustainability of electric buses during their operational phase by utilizing the life cycle assessment (LCA) technique. This paper used the MATLAB R2021b code to ascertain the mean load of the buses during their operation. The energy consumption of battery electric and hybrid electric buses was evaluated using the WLTP Class 2 standard, which refers to vehicles with a power-to-mass ratio between 22 and 34 W/kg, overing four speed phases (low, medium, high, extra high) with speeds up to 131.3 km/h. The code was used to calculate the energy consumption levels for the complete test cycle. The code adopts an idealized rectangular blind box model, disregarding the intricate design of contemporary buses to streamline the computational procedure. Simulating realistic test periods of 1800 s resulted in an average consumption of 1.451 kWh per km for electric buses and an average of 25.3 L per 100 km for hybrid buses. Finally, through an examination of the structure of the Hungarian power system utilization, it was demonstrated that electrification is a more appropriate method for achieving the emission reduction goals during the utilization phase. Full article

The present paper addresses dynamic risks in automotive industry factories, specifically in the car lead-acid battery manufacturing area. The main analyzed risk is fire risk. The battery manufacturing process is described and analyzed from this perspective, and the hazard areas are identified. The investigation methodology uses case studies for different lead-acid battery formation processes, combined with 3D simulations using the PyroSim platform, and it is based on our practical experience in the battery manufacturing field. The results of the case studies are compared using the same inputs but specific process conditions, and conclusions are formulated. To avoid fires and mitigate the risk, a series of actions are proposed in the discussion section. As a general conclusion, the current research demonstrates that the complex and dynamic risks in the automotive industry, associated with Industry 5.0 technologies, must be analyzed using combined methods, both quantitative and qualitative, including 3D simulations. Full article

The increasing complexity of purchasing an electric car, influenced by technical specifications and expert reviews, requires advanced Natural Language Processing techniques to extract meaningful insights. This study enhances Aspect Sentiment Triplet Extraction (ASTE) by integrating Large Language Models (LLMs) to identify key aspects, opinions, and sentiments in expert reviews, including technical data traditionally classified as neutral, such as horsepower and battery range. A semantic extension of Backus–Naur Form (BNF) structures input queries for syntactic and semantic accuracy, while a 2-tuple fuzzy linguistic model refines sentiment representation, ensuring interpretability. The proposed model addresses limitations in existing ASTE techniques by incorporating formal grammar structures and linguistic modeling, eliminating the need for complex preprocessing. Applied to expert YouTube reviews of electric cars, the method leverages Google’s Gemini model via Python and the Gemini API to rank the top-selling electric cars in the United States. The results confirm the model’s effectiveness in aligning technical data with sentiment analysis, making it accessible to non-specialists in Natural Language Processing. This framework enhances decision support in electric car purchases by providing a structured, interpretable, and contextually rich sentiment analysis approach. Full article

Electric motorcycles feature a smaller size and lower weight than electric cars, meaning they have greater manoeuvrability and energy efficiency, which translate to a dynamic riding experience and reduced environmental footprint. From a thermal management perspective, one major challenge is how to maximise the heat dissipation efficiency of the battery system within the limited space available onboard since the battery system represents one of the largest thermal loads onboard. This paper investigates electric motorcycle modelling to facilitate prototype development, emphasising a compact, integrated cooling system for high-voltage powertrain components, including the battery, inverter, and motor. Particularly, the proposed battery model is structured across the pack–module–cell hierarchy, which makes it capable of distinguishing the thermal state of each individual cell and the cell-to-cell performance variations resulting from temperature effects. The integrated cooling system and multi-scale battery modelling method proposed in this paper allow for a quick comparison of performances between different battery module thermal designs, which is specifically suited for early-stage investigation of different concepts. A series and a parallel battery module thermal design are proposed and compared, with a focus on evaluating their impacts on system-level and component-level thermal performances as well as cell-level performance variations, including but not limited to temperature, state of charge, voltage, and state of power. Specifically, the serial thermal design provides better overall cooling efficiency and lower battery pack temperatures, while the parallel design significantly reduces cell-to-cell variations. Full article

Flying cars are envisioned as key components of the Future Comprehensive Transport Network System. Current flying car designs struggle to balance ground maneuverability with aerial agility, which means they cannot operate on standard roads (3.5 m width). Additionally, the low energy density of existing aviation batteries limits their operational range. Therefore, a high lift-to-drag ratio (L/D) improves efficiency by reducing drag and extending the operational range. This leads to more economical and efficient flight performance, making it particularly beneficial for flying cars. This paper addresses the challenges of the land–air amphibious design and high-L/D configuration design of flying cars, and Computational Fluid Dynamics (CFD) simulations were conducted to optimize the overall configuration of a flying car, followed by creating a 1:4-scale model and validating its aerial posture. The results confirmed the structural integrity of the tilting and folding wing design for amphibious flying cars, achieving a fixed-wing mode L/D of 11. This design effectively addresses the traditional flying car issue of neglecting ground travel requirements by focusing solely on the flight capabilities of simulated aircraft or drones. Full article

BEVs (Battery Electric Vehicles) have received widespread attention from various countries for their potential in combating global warming, the energy crisis, and environmental pollution. The driving range and energy consumption of BEVs vary significantly under different driving cycles, which often results in discrepancies between the values reported by manufacturers and real-world data. To address this issue, this paper establishes a modular simulation model of a BEV on the Matlab/Simulink platform and conducts simulation experiments and analyses of driving range and energy consumption under three different standard driving cycles, namely, the NEDC (New European Driving Cycle), WLTC (World Light Vehicle Test Cycle), and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger Car), and compares the results with data from vehicle manufacturers and consumers. The results of the study show that the NEDC conditions are more ideal, the CLTC-P conditions are the most intense vehicle driving, and the WLTC conditions require the highest overall vehicle performance. Compared with other standard cycles, the WLTC conditions show better alignment with real-world driving range data. The two main factors affecting the energy consumption in each condition are driving range and acceleration. The energy recovery strategy, braking frequency, and average deceleration speed of the driving cycle conditions are important factors affecting the braking energy recovery. This study provides a theoretical basis for driving range and energy consumption testing and driving cycle condition improvement of BEVs. Full article

One of the new areas that requires extensive study of the structure and properties of welded joints is the heat-affected zone (HAZ). This issue is particularly important for new constructions made of aluminium alloys intended for battery housing for powering electric car engines. Modern welding methods, such as EBW and FSW, meet the requirements related to the high precision of the process and the quality of the welded joint itself. This article presents the results of an analysis of the structure and strengthening of the HAZ of chemically modified AlMgSi(Cu) alloys via EBW and FSW. Microstructural observation was performed via SEM for each welded joint to determine the morphology of the precipitates. In the HAZ, β-Mg₂Si, Q-Al,MgCu,Si and α-Al,Fe,Si (Mn,Cu) phases with larger sizes and rounded shapes were visible than they were directly in the weld made via the EBW method. The joints produced by the FSW method were characterised by a wide weld area and an irregular weld line. Analysis of the crystallographic orientation via EBSD and grain orientation spread (GOS) revealed differences in the shape of the grains and the degree of recrystallisation in the weld area between the FSW and EBW methods. The distributions of HB (FSW) hardness and HV (EBW) microhardness measurements revealed a slight decrease in hardening in the HAZ. In joints welded by both methods, the hardness of the welds for alloys with increased copper and chromium contents increased by approximately 5%. Full article

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles. Full article