Search Results (174)

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

With the rapid advancement of integrated thermal management systems (ITMS) for new energy vehicles (NEVs), flow losses and hydrodynamic characteristics within multi-way valves have become critical determinants of system performance. In this study, a three-dimensional computational fluid dynamics model is established for a multi-way valve used in a representative NEV ITMS, where PAG46 coolant is employed as the working fluid. The steady-state pressure-loss characteristics under three typical operating modes—cooling, heating, and waste heat recovery—are investigated, together with the transient hydrodynamic response during mode switching. The steady-state results indicate that pressure losses are primarily concentrated in regions with abrupt changes in flow direction and sudden variations in cross-sectional area, and that the cooling mode generally exhibits the highest overall pressure loss due to the involvement of all flow channels and stronger flow curvature. Furthermore, a parametric analysis of the valve body corner chamfers and valve spool fillets reveals a non-monotonic dependence of pressure drop on chamfer radius, highlighting a trade-off between streamline smoothness and the effective flow cross-sectional area. Transient analysis, exemplified by the transition from heating to waste heat recovery mode, demonstrates that dynamic changes in channel opening induce a significant reconstruction of the internal velocity and pressure fields. Local high-velocity zones, transient pressure peaks, and pronounced fluctuations of hydraulic torque on the valve spool emerge during the switching process, imposing higher requirements on the torque output and motion stability of the actuator mechanism. Consequently, this study provides a theoretical basis and engineering guidance for the structural optimization and actuator matching of multi-way valves in NEV thermal management systems. Full article

To achieve China’s carbon peak and carbon neutrality goals, it is essential to increase the market penetration of New Energy Vehicles (NEVs) and understand consumer attitudes. Based on a big data set of over 20,000 online user reviews, this study employs the Latent Dirichlet Allocation (LDA) model to extract themes, popular brands, and focal points across different time windows. The research constructs a data-driven threshold filtering mechanism that integrates topic probability, frequency, keyword weight, and cross-temporal topic similarity to quantify consumer reviews, enabling an in-depth analysis of the dynamic evolution of attitudes in the NEV market. The findings reveal a dual shift in consumer sentiment: first, a transition in focus from basic configurations and aesthetics toward quality experience; and second, a shift in purchasing decisions toward a socially driven model dominated by word-of-mouth and family collaboration. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

This study explores the core competencies required for sales and marketing roles in the rapidly evolving NEV sector. Adopting an exploratory sequential mixed-methods design, it employs a big data-driven approach to construct a competency framework: web crawlers collected NEV-related recruitment data across over 20 major Chinese cities, the Latent Dirichlet Allocation (LDA) model identified core competency items, and a multi-dimensional consensus scoring process via the Nominal Group Technique (NGT) refined the framework. The resulting validated model comprises nine thematic clusters, reflecting a shift from internal combustion engine (ICE) vehicle sales’ traditional skill set. Beyond enriching conventional competencies (customer reception, sales service, CRM, sales support), it highlights emerging capabilities: live-streaming/short-video marketing, digital media operations, and ecosystem-oriented resource collaboration. Further, NGT-based multi-dimensional evaluations (frequency, importance, difficulty) generated a four-quadrant matrix, offering actionable guidance for vocational education and corporate training (VET) curriculum design. Theoretically, this study redefines digital-era automotive sales roles: not mere product sellers, but core actors in user experience co-creation and ecological value integration, which enriches discourse on sales role evolution. Full article

This study systematically investigates the structure–performance relationship of PMA (PolyMethacrylate) viscosity index improvers in new energy vehicle (NEV) transmission fluids. We developed an integrated analytical framework combining spectroscopic and chromatographic techniques to simultaneously characterize its side chain length distribution, molecular weight polydispersity, and branching architecture. Key findings reveal that the kinematic viscosity of formulated oils positively correlates with PMA molecular weight, low-temperature performance is governed by side-chain length (≥C14 fatty alcohols), shear stability is predominantly determined by molecular weight, and nitrogen-modified PMA enhances oxidation resistance by mitigating kinematic viscosity increase. These insights provide actionable guidance for the molecular design of viscosity index improvers and the formulation optimization of advanced lubricants to meet the stringent demands of electric vehicle transmission systems. Full article

With worsening energy and environmental issues, new energy vehicles (NEVs) have emerged as the future of the automotive industry, as they aim to address the high energy consumption and carbon emissions of traditional fuel vehicles. However, due to the industry’s short development history, limited available data, and incomplete supporting systems, most existing NEV research focuses on theoretical analysis, which hinders the achievement of accurate sales predictions. Today, online reviews influence consumer decisions and thus provide a new perspective for sales forecasting. Based on consumer behavior theory and neural network principles, our research selects factors influencing NEV sales (covering economics, technological, policy, and consumer dimensions, including preprocessed crawled online reviews), constructs an index system screened via grey relational analysis, and establishes five models (SARIMA, GRU, Seq2Seq, Attention-GRU, Attention-Seq2Seq) for training and testing. The study supports the use of online reviews in NEV sales prediction and proves that the model based on cutting-edge technology of Attention-Seq2Seq can outperform the other four methods presented above. Through this, the current contributions advance marketing innovation by helping NEV stakeholders understand relevant information using a predictive model from online reviews, which leads to precise product improvement and optimal distribution of resources as well as precise adoption of marketing strategies. Full article

This study employs an industry-specific patent classification methodology (ISPCM) and conducts complex network analysis across temporal, industrial, and spatial dimensions to examine China’s new energy vehicle (NEV) patent collaboration network and to uncover the mechanisms underlying China’s global rise in the NEV sector. The results demonstrate the effectiveness of the ISPCM and reveal a three-phase growth pattern that is driven by policy initiatives and market expansion. Domestic entities dominate the patent landscape, with a noticeable shift from invention patents to utility model patents, which reflects a focus on application-oriented innovation. The collaboration network exhibits a heavy-tailed characteristic, and it forms an oligopolistic structure in which state-owned enterprises (SOEs) act as “innovation orchestrators,” while private firms concentrate on specialized R&D. Across the industrial chain, the component segment forms the largest network, the complete vehicle segment comprises the smallest network, and the aftermarket is clustered around battery recycling. A clear divide between domestic and foreign entities suggests potential decoupling risks. The findings reveal a dual-circulation innovation model that combines state-led coordinated research with market-driven independent research, offering valuable insights for sustainable industrial transformation. Full article

With the rapid growth of new energy vehicles (NEVs), the number of NEV-related traffic accidents has risen sharply. To address the challenge of low accuracy in real-time risk assessment caused by the coupling of multi-source heterogeneous data, this paper proposes a real-time risk prediction method for NEV operations based on multi-source feature fusion. First, considering issues such as signal loss and bias in NEV operation data and accident records, a fused accident operation dataset is constructed through data matching, imputation, and Kalman smoothing. Then, this study analyzes the influence of external factors (e.g., weather, road type, and lighting) and internal factors (e.g., speed, acceleration, and driving duration) on accident risk and develops a normalized representation method for NEV accident risk features. Based on the coupling of internal and external parameters, a real-time accident risk prediction model is established based on the XGBoost algorithm, enabling accurate prediction of NEV accidents. Vehicle data tests show that the proposed method achieves an average accident risk prediction accuracy of 69.60%, outperforming the traditional Analytic Hierarchy Process and Support Vector Machine models. Finally, application effect demonstrates that the method reduces the NEV accident rate to 0.83%, effectively assisting traffic management departments in identifying and warning high-risk vehicles, thereby improving road traffic safety. Full article

The core objective of Active Vibration and Noise Control (AVNC) is to enhance system performance by generating real-time counter-phase signals of equal amplitude to cancel out vibration and noise interference from mechanical or structural systems. As the demand for low-noise, low-vibration environments grows in fields such as new energy vehicles (NEVs), aerospace, and high-precision manufacturing, traditional AVNC methods—which rely on precise linear models and have poor adaptability to nonlinear and time-varying conditions—struggle to meet the dynamic requirements of complex engineering scenarios. However, with advancements in artificial intelligence (AI) technology, AI-driven Active Vibration and Noise Control (AI-AVNC) technology has garnered significant attention from both industry and academia. Based on a thorough investigation into the state-of-the-art AI-AVNC methods, this survey has made the following contributions: (1) Introducing the theoretical foundations of AVNC and its historical development; (2) Classifying existing AI-AVNC methods from the perspective of control strategies; (3) Analyzing engineering applications of AI-AVNC; (4) Discussing current technical challenges and future development trends of AI-AVNC. Full article

Driven by the global energy transition and the pursuit of dual carbon goals (carbon peaking and carbon neutrality), the innovation network of the new energy vehicle (NEV) industry, composed of enterprises, universities, and research institutes, has become a key driver of sustainable industrial development. The evolution of this network is jointly shaped by both supply chain networks (SCNs) and executive networks (ENs), representing formal and informal relational structures, respectively. To systematically explore these dynamics, this study analyzes panel data from Chinese A-share-listed NEV firms covering the period 2003–2024. Employing social network analysis (SNA) and Quadratic Assignment Procedure (QAP) regression, we investigate how SCNs and ENs influence the formation and structural evolution of innovation networks. The results reveal that although all three networks exhibit sparse connectivity, they differ substantially in their structural characteristics. Moreover, both SCNs and ENs have statistically significant positive effects on innovation network development. Building on these findings, we propose an integrative policy framework to strategically enhance the innovation ecosystem of China’s NEV industry. This study not only provides practical guidance for fostering collaborative innovation but also offers theoretical insights by integrating formal and informal network perspectives, thereby advancing the understanding of multi-network interactions in complex industrial systems. Full article

To gain a comprehensive understanding of the current research landscape in the field of new energy vehicle (NEV) fires and to explore its knowledge base and emerging trends, bibliometric methods—such as co-occurrence, clustering, and co-citation analyses—were employed to examine the relevant literature. A research knowledge framework was established, encompassing four primary themes: thermal management and performance optimization of power batteries, battery materials and their safety characteristics, thermal runaway (TR) and fire risk assessment, and fire prevention and control strategies. The key research frontiers in this domain could be classified into five categories: mechanisms and propagation of TR, development of high-safety battery materials and flame-retardant technologies, thermal management and thermal safety control, intelligent early warning and fault diagnosis, and fire suppression and firefighting techniques. The focus of research has gradually shifted from passive identification of causes and failure mechanisms to proactive approaches involving thermal control, predictive alerts, and integrated system-level fire safety solutions. As the field advances, increasing complexity and interdisciplinary integration have emerged as defining trends. Future research is expected to benefit from broader cross-disciplinary collaboration. These findings provide a valuable reference for researchers seeking a rapid overview of the evolving landscape of NEV fire-related studies. Full article

The successful implementation of lane change and overtaking maneuvers, as well as the technological advancements in new energy vehicles in China, are outcomes influenced by multiple factors. Among these factors, the responsiveness of local policies plays a crucial role and serves as a pivotal element in ensuring the effective execution of central policies. Nevertheless, there is a dearth of systematic research within the academic community regarding the innovative impacts of local policy responses. We utilize industrial policy and patent data from China’s NEV sector, employing text analysis to measure local policy response in terms of intensity, velocity, and degree. Regression analysis is conducted to investigate the impact of local policy responses on technological innovation. The findings reveal an inverted U-shaped correlation between policy issuance frequency, adoption speed, policy reproduction degree, and technological innovation. Regional disparities play a moderating role in the local policy response impact, with the eastern region exhibiting superior policy response compared to the central and western regions. Notably, an inverted U-shaped relationship is observed between adoption speed and policy reproduction degree in the eastern region, as well as between policy issuance frequency in the central region and technological innovation. Conversely, no significant policy response effect is detected in the western region. These outcomes underscore the necessity for effective local policy response, emphasizing the need for local governments to adapt and customize central policies in alignment with local contexts while navigating the balance between central coherence and local diversity, as well as policy adjustments and temporal constraints. This article contributes to the existing literature on policy implementation and innovative governance, offering empirical insights to enhance the optimization of regionally tailored policy frameworks and to bolster the coherence and efficacy of central and local policies. Full article

The transformation of the transportation sector is critical for achieving Sustainable Development Goal 7 (SDG 7). As the world’s largest auto market, China has implemented various policies to promote sustainable transportation, particularly through the adoption of new energy vehicles (NEVs), thereby increasing the share of renewables in energy consumption and improving energy efficiency. Among these policies, the NEV driving restrictions exemption (NEV-DRE) policy has emerged as a key non-financial incentive to stimulate NEV demand. This study focuses on how the NEV-DRE policy affects the demand side of NEVs in the transportation sector. Employing a difference-in-differences design on a comprehensive dataset of vehicle transactions across 82 prefecture-level pilot cities from 2011 to 2019, this study provides robust causal evidence that the NEV-DRE policy significantly increases NEV sales. Furthermore, this study finds that this growth in demand is primarily driven by an increased consumer preference for domestic pure electric sedans. The policy proves more effective in cities with general driving restrictions, purchasing restrictions, and greater environmental awareness. Our findings demonstrate how innovative traffic management measures can be transformed into effective industrial policy tools, accelerating the adoption of renewable energy in the transportation sector. This study offers valuable insights for policymakers in China and elsewhere on how to design non-financial incentives to promote sustainable transportation, thereby promoting sustainable energy transitions and contributing to the achievement of SDG 7. Full article

With the surge in global maritime trade of new energy vehicles (NEVs), the roll-on/roll-off (Ro-Ro) shipping market faces a severe supply–demand imbalance, pushing shipping rates to persistently high levels. To tackle this challenge, NEV manufacturers and other automakers have begun establishing their own Ro-Ro fleets, creating an urgent need for optimized operational scheduling of these proprietary fleets. Against this context, this study focuses on optimizing the operational scheduling of automakers’ self-owned Ro-Ro fleets. Under the premise of deterministic automobile export transportation demands, a mixed-integer programming model is developed to minimize total fleet operational costs, with decision variables covering vessel port call sequence/selection, port loading and unloading quantities, and voyage speeds. A genetic algorithm is designed to solve the model, and the effectiveness of the proposed approach is validated through a real-world case study. The results demonstrate that the optimization method generates clear, actionable scheduling schemes for self-owned Ro-Ro fleets, effectively helping automakers refine their maritime logistics strategies for proprietary fleets. This study contributes to the field by focusing on automaker-owned Ro-Ro fleets and filling the research gap in cargo-owner-centric scheduling, providing a practical tool for automakers’ overseas logistics operations. Full article