Search Results (168)

With the increasing popularity of electric vehicles (EVs) through purchase subsidy (PS) policies, the personal carbon tax (PCT) policy has been adopted by some countries due to its characteristics of restraining the diffusion of fuel vehicles (FVs) from the consumer side. This paper constructs a three-stage game model consisting of government, manufacturers, and consumers to investigate the impact of basic utility valuation heterogeneity differences on the optimal decisions and to compare the implementation effects of two policies. The results are as follows. First, conventional wisdom suggests that EV consumer surplus under PS policy will exceed that under PCT policy. Surprisingly, our results show that when the basic utility valuation difference is small, the EV consumer surplus under PCT policy exceeds that under PS policy. Second, for manufacturers, it is interesting to note that the sustained impact of PCT policy on promoting the diffusion of the EV market and the profit of the EV manufacturer is related to the basic utility valuation heterogeneity difference. However, compared with PS policy, the implementation of PCT policy has a better restraining effect on the diffusion of the FV market, effectively reducing the demand for FV and the profit of FV manufacturers. Finally, contrary to the common belief that increasing subsidies or raising carbon taxes can increase overall social welfare, this paper shows that subsidies and carbon taxes have a dual impact on overall social welfare, and only when their positive effects outweigh the negative ones can such policies become effective ways of promoting industrial transformation. Full article

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment. Full article

Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies. Full article

Ultrafast-charging (UFC) technology for electric vehicles (EVs) and energy storage devices has brought with it an increase in demand for lithium-ion batteries (LIBs). However, although they pose advantages in driving range and charging time, LIBs face several challenges such as mechanical degradation, lithium dendrite formation, electrolyte decomposition, and concerns about thermal runaway safety. This review evaluates the key challenges and advances in LIB components (anodes, cathodes, electrolytes, separators, and binders), alongside innovations in charging protocols and safety concerns. Material-level solutions such as nanostructuring, doping, and composite architectures are investigated to improve ion diffusion, conductivity, and electrode stability. Electrolyte modifications, separator enhancements, and binder optimizations are discussed in terms of their roles in reducing high-rate degradation. Furthermore, charging protocols are addressed; adjustments can reduce mechanical and electrochemical stress on LIBs, decreasing capacity fade while providing rapid charging. This review highlights the key technological advancements that are enabling ultrafast charging and that are assisting us in overcoming severe limitations, paving the way for the development of next-generation high-performance LIBs. Full article

The increasing demand for sustainable and cost-effective battery technologies in electric vehicles (EVs) has driven research into alternatives to lithium-ion (Li-ion) batteries. This study investigates magnesium-ion (Mg-ion) batteries as a potential solution, focusing on their energy density, cycle stability, safety, and scalability. The research employs a comprehensive methodology, combining electrochemical testing and simulation models, to analyse magnesium-based anodes, sulphur-based cathodes, and advanced electrolytes such as HMDS₂Mg. Key findings reveal that Mg-ion batteries achieve a practical energy density of 500–1000 mAh/g, comparable to high-performance Li-ion systems. With sulphur–graphene cathodes, Mg-ion batteries demonstrated 92% capacity retention after 500 cycles, a 10% improvement over standard configurations. Ionic conductivity reached 1.2 × 10⁻² S/cm using HMDS₂Mg electrolytes, significantly reducing passivation layer growth to 5 nm after 100 cycles, outperforming Grignard-based systems by 30%. However, the research identified a 15% reduction in charge–discharge efficiency compared to Li-ion batteries due to slower ion diffusion kinetics. This study highlights the safety advantage of magnesium-ion batteries, which eliminate dendrite formation and reduce thermal runaway risks by 40%. These findings position Mg-ion batteries as a promising, sustainable alternative for EVs, emphasising the need for further optimisation in scalability and efficiency. Full article

With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering (KE) approaches suffer from limited timeliness, subjectivity, and low predictive accuracy when extracting affective vocabulary and modeling the nonlinear relationship between product form and Kansei imagery. To address these challenges, this study proposes an improved KE-based ESUV styling framework that integrates data mining, machine learning, and generative AI. First, real consumer reviews and front-end styling samples are collected via Python-based web scraping. Next, the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) are used to extract representative Kansei vocabulary. Subsequently, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models are constructed and optimized using the Seagull Optimization Algorithm (SOA) and Particle Swarm Optimization (PSO). Experimental results show that SOA-BPNN achieves superior predictive accuracy. Finally, Stable Diffusion is applied to generate ESUV design schemes, and the optimal model is employed to evaluate their Kansei imagery. The proposed framework offers a systematic and data-driven approach for predicting consumer affective responses in the conceptual styling stage, effectively addressing the limitations of conventional experience-based design. Thus, this study offers both methodological innovation and practical guidance for integrating affective modeling into ESUV styling design. Full article

Nickel-rich cathode materials have emerged as ideal candidates for electric vehicles due to their high energy density; however, polycrystalline materials are prone to microcrack formation and unavoidable side reactions with electrolytes during cycling, leading to structural instability and capacity degradation. Herein, an Sr-doped single-crystalline nickel-rich LiNi_0.88Co_0.05Mn_0.07O₂/Sr cathode material is synthesized, with Sr doping levels controlled at x = 0.3%, 0.5%, 1 mol%. The nickel-rich LiNi_0.88Co_0.05Mn_0.07O₂/Sr cathode features particle sizes of approximately 2 μm, at a relatively low temperature. It inhibits the microcrack formation, prevents electrolyte penetration into the particle interior, and reduce side reactions, thereby enhancing structural stability. This enables the cathode to deliver a high initial discharge capacity of 205.3 mAh g⁻¹at 0.1 C and 170.8 mAh g⁻¹ at 10 C, within the voltage range of 2.7 V–4.3 V, and an outstanding capacity retention of 96.61% at 1 C after 100 cycles. These improvements can be attributed to the Sr-doping, which reduces the single-crystal growth temperature, effectively mitigating Li⁺/Ni²⁺ cation mixing. Moreover, the incorporation of Sr expands the interlayer spacing, thereby facilitating Li⁺ diffusion. The doping strategy employed in this work provides a new insight for low-temperature single-crystal materials synthesis, significantly improving the electrochemical performance of nickel-rich cathode materials. Full article

Ultrasonic welding is characterized by its energy-saving and environmentally friendly nature. Compared to conventional molten welding technology, the intermetallic compounds formed by diffusion during ultrasonic welding are thinner, and material deformation is reduced. This process has become a primary welding technique for assembling lithium batteries in electric vehicles. Aluminum and copper ultrasonic welding has increasingly gained attention as a research hotspot. The research on aluminum and copper ultrasonic welding primarily focuses on the interfacial microstructure evolution, mechanical performance during the welding process, and numerical simulations to investigate macro- and micro-scale physical phenomena. Given the aluminum and copper multi-layer structures used in lithium battery packaging, numerous studies have been conducted on aluminum and copper multi-layer ultrasonic welding. For Al/Cu joints, advancements in understanding the microstructure evolution, joint performance, and finite element modeling of the welding process have been systematically reviewed and summarized. Moreover, significant progress has been made in molecular dynamics simulations of Al/Cu ultrasonic welding and hybrid welding techniques based on Al/Cu ultrasonic welding. Finally, several new research directions for Al/Cu ultrasonic welding and joining have been proposed to guide further in-depth studies. Full article

Car styling is crucial for consumer acceptance and market success. Since vehicle manufacturers produce electric vehicles, they have faced the challenge of maintaining the typicality of their original products and presenting the innovation of new technologies. We propose a method that integrates artificial intelligence (AI)-generated images and image classification technology to help designers effectively balance between typicality and novelty. We collected 118 pictures of electric vehicles and 122 pictures of fuel vehicles in 2024 from the BMW official website. Focusing on seven key visual features of the vehicles, we used the Waikato environment for knowledge analysis (WEKA) to train an image classification model on the dataset through three separate training and testing sessions. First, we used the prompts that described typical BMW design to generate images of new BMW electric vehicles in Stable Diffusion. The images consisted of 21 front views, 20 side views, and 20 rear views. The accuracy of the model of front views trained with the pyramid histogram of oriented gradients filter (PHOG)-Filter and random forest classifier was 78.5%, and the test accuracy reached 95%. The accuracy of the model of rear views trained with BinaryPatternsPyramid-Filter and random forest classifier was 80.5%, and the test accuracy was 90%. However, the accuracy of the model of side views did not reach 70%. That implies the distinction between BMW fuel vehicles and its electric vehicles is mainly based on the front and rear views, rather than the side view. The results of this study showed that integrating image classification and AI-generated images can be used to examine the balance between product typicality and novelty, and the application of machine learning and AI tools to study car style. Full article

The transport sector is responsible for about 60% of emissions in the atmosphere due to the exhaust-polluting gases of internal combustion engine (ICE) vehicles. An effective solution to this issue is the electrification of the transport means, which can significantly reduce pollution, especially in urban areas. Apart from the necessary technological advancements that must improve the battery performances, the diffusion of electric vehicles (EVs) must be further supported and facilitated by new dedicated services and tools for electric vehicle users and operators aiming at improving the travel and charging experience. To this goal, this paper proposes new models based on Timed Colored Petri Nets (TCPN) to simulate and manage the charge demand of the EV fleet. At first, the proposed tool must take into account the charging requests from different EV drivers with different charging need located in different geographical areas. This is possible by knowing input data such as EV current location, battery data, charge points (CPs) availability, and compatibility. In particular, EV drivers are simulated when finding and booking the preferred charge option according to the available infrastructure in the area of interest and the CPs tariff and power rate. The proposed TCPN is designed to model the multi-user charging demand in specific geographic areas, and it is evaluated in several scenarios of a case study to measure its performance in serving multiple EV users. Full article

The key equipment for performing aerodynamic testing of objects, such as road and railway vehicles, aircraft, and wind turbines, as well as stationary objects such as bridges and buildings, are multichannel pressure measurement instruments (pressure scanners). These instruments are typically based on arrays of separate pressure sensors built in an enclosure that also contains temperature sensors used for temperature compensation. However, there are significant limitations to such a construction, especially when increasing requirements in terms of miniaturization, the number of pressure channels, and high measurement performance must be met at the same time. In this paper, we present the development and realization of an innovative MEMS multisensor chip, which is designed with the intention of overcoming these limitations. The chip has four MEMS piezoresistive pressure-sensing elements and two resistive temperature-sensing elements, which are all monolithically integrated, enabling better sensor matching and thermal coupling while providing a high number of pressure channels per unit area. The main steps of chip development are preliminary chip design, numerical simulations of the chip’s mechanical behavior when exposed to the measured pressure, final chip design, fabrication processes (photolithography, thermal oxidation, diffusion, layer deposition, micromachining, anodic bonding, and wafer dicing), and electrical testing. Full article

Rare-earth permanent magnets, such as Nd₂Fe₁₄B, have been widely used in electric vehicle and wind turbine motors due to their high anisotropy field (H_a), saturation magnetization (M_s) and coercivity (H_c). Cerium (Ce) has gained attention as a potential alternative to neodymium (Nd) due to its high abundance and low cost. The relatively poor intrinsic magnetic properties of Ce magnets, however, remain a significant challenge for their industrial applications. In this study, the synthesis of Ce-based RE₂Fe₁₄B (2-14-1) phases was achieved by a modified reduction-diffusion (R-D) process using REFeO₃ (RE = Ce, Nd) as a precursor. The precursor was prepared by a solid-state reaction with CeO₂, Nd₂O₃, Fe₂O₃ and Fe powders, which is a much more suitable process for mass production and cost-effectiveness. Optimal composition and heat treatment conditions enabled the formation of single-phase Ce-based 2-14-1 particles. The as-synthesized single-phase Ce₂Fe₁₄B particles exhibited an M_s value of ~120 emu/g and an intrinsic coercivity (H_ci) value of ~85 Oe, which can be attributed to the large particle size as observed by FE-SEM. Full article

The growing demand for efficient energy storage systems, particularly in portable electronics and electric vehicles, has led to increased interest in supercapacitors, which offer high power density, rapid charge/discharge rates, and long cycle life. However, improving their energy density without compromising performance remains a challenge. In this study, we developed novel 3D-printed reduced graphene oxide (rGO) electrodes coated with polyaniline (PANI) to enhance their electrochemical properties. The rGO 3D-printed electrodes were fabricated using direct ink writing (DIW), which allowed precise control over thickness, ranging from 4 to 24 layers. A unique ink formulation was optimized for the printing process, consisting of rGO, cellulose acetate (CA) as a binder, and acetone as a solvent. The PANI coating was applied via chemical oxidative polymerization (COP) with up to five deposition cycles. Electrochemical testing, including cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), and electrochemical impedance spectroscopy (EIS), revealed that 12-layer electrodes with three PANI deposition cycles achieved the highest areal capacitance of 84.32 mF/cm². While thicker electrodes (16 layers and beyond) experienced diminished performance due to ion diffusion limitations, the composite electrodes demonstrated excellent cycling stability, retaining over 80% of their initial capacitance after 1500 cycles. This work demonstrates the potential of 3D-printed PANI/rGO electrodes for scalable, high-performance supercapacitors with customizable architectures. Full article

Lithium-ion batteries, due to their high energy density, compact size, long lifetime, and low environmental impact, have achieved a dominant position in everyday life. These attributes have made them the preferred choice for powering portable devices such as laptops and smartphones, power tools, and electric vehicles. As technology advances rapidly, the demand for even more efficient energy storage devices continues to rise. In lithium-ion batteries, anodes play a crucial role, with lithium titanate oxide standing out as a highly promising material. This anode is favored for its exceptional cycle stability, safety features, and fast charging capabilities. The impressive cycle stability of lithium titanate oxide is largely due to its zero-strain nature, meaning it undergoes minimal volume changes during lithium-ion insertion and extraction. This stability enhances the anode’s durability, leading to longer battery life. In addition, the lithium titanate oxide anode operates at a voltage of approximately 1.55 V vs. Li⁺/Li, significantly reducing the risk of dendrite formation, a major safety concern that can cause short circuits and fires. The material’s spinel structure, with its large active surface area, further allows fast electron transfer and ion diffusion, facilitating fast charging. This review explores the characteristics of lithium titanate oxide, the various synthesis methods employed, and its integration with carbon materials to enhance cycle stability, coulombic efficiency, and safety. It also proposes strategies for optimizing lithium titanate oxide properties to create sustainable anodes with reduced environmental impact using eco-friendly routes. Full article

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided. Full article