Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency....
The growing demand for internet-of-vehicles (IoV) communication requires compact antennas capable of supporting multiple frequency bands while maintaining stable radiation characteristics. This paper presents the design and validation of a multilayer microstrip patch antenna that achieves dual-band operation through the integration of shorting vias, a coupled ring, and an embedded parasitic patch. Parametric studies confirm that the adopted techniques yield impedance bandwidths of 28% at 1.8 GHz and 6.4% at 2.4 GHz, with a low-profile structure of 0.055λ0. Measured results demonstrate omnidirectional radiation patterns across the intended bands with a maximum gain of 4.46 dBi at 2.57 GHz. Beyond simulated and laboratory verification, field tests were conducted using LTE communication to evaluate the antenna’s quality of service (QoS) under realistic vehicular conditions. To reduce system cost and simplify testing, a low-cost in-house signal meter based on a Raspberry Pi microcontroller was developed and employed to compare the proposed antenna with a commercial monopole. The results confirm that the multilayer patch antenna provides improved bandwidth, gain, and radiation stability, making it a compact and cost-effective candidate for multiband IoV and V2X communication systems....
The rapid integration of connectivity and automation in modern vehicles has significantly expanded the attack surface of in-vehicle networks, particularly the Controller Area Network (CAN) bus, which lacks native security mechanisms. This study investigates machine learning-based intrusion detection for Internet of Vehicles (IoV) environments using the CICIoV2024 dataset. Unlike prior studies that rely on highly redundant traffic traces, this work applies strict de-duplication to eliminate repetitive CAN frames, resulting in a dataset of unique attack signatures. To ensure statistical robustness despite the reduced data size, Stratified K-Fold Cross-Validation was employed. Experimental results reveal that while traditional models like Random Forest (optimized with ANOVA feature selection) maintain stability (F1-Macro ≈ 0.64), Deep Learning models fail to generalize (F1-Macro < 0.55)when denied the massive redundancy they typically require. These findings challenge the “nearperfect” detection rates reported in the literature, suggesting that previous benchmarks may reflect data leakage rather than true anomaly detection capabilities. The study concludes that lightweight models offer superior resilience for resource-constrained vehicular environments when evaluated on realistic, non-redundant data....
Driven by the dual goals of global carbon neutrality and the transformation and upgrading of the automotive industry, electric vehicles have become a strategic core area for addressing climate change and ensuring energy security. This article systematically reviews the research progress in the field of electric vehicles, focusing on five mainstream research directions: power battery technology, optimization of electric drive systems, intelligence and autonomous driving, lightweight and material innovation, charging and swapping, and energy interconnection. It comprehensively analyzes the technological breakthroughs, application status, and existing bottlenecks in various fields. Research has shown that high-energy density solid-state batteries, silicon carbide electric drive modules, Level 3 or above autonomous driving, integrated lightweight technology, and V2G energy interconnection systems are current and future core research hotspots. Finally, based on the needs of industrial development, the future research focus and development path of electric vehicle technology were discussed, providing a reference for academic research and industrial practice....
In applied battery research, use-case-driven prediction is becoming increasingly important, particularly for predicting real-life load profiles. This study proposes techniques to forecast lifetime load profiles for traction batteries, comparing urban- and highway-dominated vehicular use cases. Both charging and discharging scenarios are analyzed. We examine the uncertainty in these profiles and conduct a sensitivity analysis to understand the relationship between load profiles and user behavior. In this study, we introduce a novel methodology that maps behavioral and environmental parameters to battery load clusters, enabling us to identify high-risk aging scenarios. Based on parameter studies, we perform load profile clustering to identify critical use case groups and observe key parameter interactions. We present a case study of an idealized driver under Hungarian environmental conditions to predict outlier battery usage in fleets. This novel approach enables more robust predictions of aging and performance degradation for automotive traction batteries across different user clusters....
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