World Electric Vehicle Journal

18 pages, 7121 KiB

Open AccessArticle

Comparative Study of Fuel and Greenhouse Gas Consumption of a Hybrid Vehicle Compared to Spark Ignition Vehicles

by Edgar Vicente Rojas-Reinoso, Michael Anacleto-Fernández, Jonathan Utreras-Alomoto, Carlos Carranco-Quiñonez and Carmen Mata

World Electr. Veh. J. 2025, 16(1), 4; https://doi.org/10.3390/wevj16010004 - 26 Dec 2024

Abstract

This study aims to determine the type of vehicle with the lowest fuel consumption and greenhouse gas emissions by comparing spark ignition commercial vehicles against hybrid vehicles. The data were obtained through the OBD Link MX+ interface under traffic conditions in the Metropolitan District of Quito to determine the consumption and emissions delivered by each studied vehicle. Measurements were made while driving on two high-traffic routes during peak hours, with a duration of 2 to 3 h of stalling, and the engine fuel consumption parameters of each vehicle were obtained using 85 octane gasoline. Five measurements were generated per route and for each vehicle tested to reduce uncertainty and strengthen the prediction model with a factor of less than 10%. Statistical analysis was implemented to obtain a numerical model that allowed to analyse the estimate of the variation in fuel economy in each vehicle. The numerical model compared the values of fuel consumption measured with those calculated on all the routes with the highest traffic, finally indicating which vehicle with the smallest cylinder capacity is optimal, with an average consumption of 14 km/l on each route compared to a hybrid vehicle with an average consumption of 8.5 km/l per route, for better fuel performance within the Metropolitan District of Quito, in heavy traffic conditions. This study conducts a comparison of the consumption between a hybrid vehicle and spark ignition vehicles through the real driving cycle on routes considered to be of greater influx, to determine which vehicle has lower consumption and, therefore, greater energy efficiency in Quito City. Full article

(This article belongs to the Special Issue Power and Energy Systems for E-mobility)

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21 pages, 728 KiB

Open AccessArticle

Optimizing Long-Distance Electric Vehicle Routes Based on Passenger Satisfaction Model

by Mohammed Alatiyyah

World Electr. Veh. J. 2025, 16(1), 3; https://doi.org/10.3390/wevj16010003 - 26 Dec 2024

Abstract

This study presents a novel approach to optimizing long-distance electric vehicle (EV) routes by prioritizing passenger happiness—a vital yet often overlooked aspect in route planning for sustainable transportation. Traditional EV route optimization models focus primarily on technical considerations such as energy efficiency, charging station availability, and minimizing travel time, yet they rarely account for the human-centric factors that shape travel satisfaction. This research introduces a comprehensive framework that integrates qualitative aspects of the travel experience, including scenic route preferences, comfort during travel, and enriching activities at charging stops. The employed methodology combines data analytics and psychological assessment to develop an EV route optimization model that aligns technical efficiency with passenger well-being. Computational experiments conducted across varied travel scenarios reveal that routes optimized for passenger happiness not only enhance the overall travel experience but also demonstrate potential to encourage broader EV adoption for long-distance journeys. The results underscore the importance of balancing technical efficiency with human-centric factors in EV route planning and highlight critical areas for infrastructure improvements, such as the strategic placement of high-power charging stations. Full article

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27 pages, 596 KiB

Open AccessArticle

Autonomous Vehicle Acceptance in China: TAM-Based Comparison of Civilian and Military Contexts

by Dan Wan and Ling Peng

World Electr. Veh. J. 2025, 16(1), 2; https://doi.org/10.3390/wevj16010002 - 24 Dec 2024

Abstract

In this study, a comparative analysis is conducted on the public acceptance of autonomous vehicles (AVs) in civilian and military contexts among the Chinese public. In order to identify the key factors influencing AV adoption under different scenarios, a Technology Acceptance Model (TAM) framework was applied in combination with an extended variable of perceived risk. Also, a structured questionnaire was designed, with 1004 valid responses received from a sample comprising mainly members of the Chinese public aged 31–50. Data analysis was conducted through reliability and validity tests, correlation analysis, and Structural Equation Modeling (SEM). Despite some slight variations in acceptance level between civilian and military fields, overall public attitudes are relatively consistent, according to the analytical results. Specifically, the average behavioral intention is slightly stronger and more consistent among the public in the civilian context, with higher scores achieved with respect to perceived usefulness and perceived risk in the military context, indicating a stronger emphasis on functionality and safety in military applications. As confirmed by SEM path analysis, there are significant influences exerted on behavioral intention by perceived usefulness, perceived ease of use, and satisfaction. These results demonstrate a high level of public acceptance of AV technology among the Chinese public in the context of policy support and technological innovation, providing empirical insights into the development of scenario-specific promotion strategies for the effective application of AV in various settings. Full article

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13 pages, 3378 KiB

Open AccessArticle

Research on Improved YOLOv7 for Traffic Obstacle Detection

by Yifan Yang, Song Cui, Xuan Xiang, Yuxing Bai, Liguo Zang and Hongshan Ding

World Electr. Veh. J. 2025, 16(1), 1; https://doi.org/10.3390/wevj16010001 - 24 Dec 2024

Abstract

Object detection and recognition algorithms are widely used in applications such as real-time monitoring and autonomous driving. However, there is limited research on traffic obstacle detection in complex scenarios involving road construction and sudden accidents. This gap results in low accuracy and difficulties in recognizing occluded targets, thereby hindering the further development and widespread adoption of intelligent transportation systems. To address these issues, this paper proposes an improved algorithm based on YOLOv7, incorporating a lightweight coordinate attention mechanism to focus on small objects at long distances and capture target location information. The use of a high receptive field enhances the feature hierarchy within the detection network. Additionally, we introduce the focal efficient intersection over union loss function to address sample imbalance, which accelerates the model’s convergence speed, reduces loss values, and improves overall model stability. Our model achieved a detection accuracy of 98.1%, reflecting a 1.4% increase, while also enhancing detection speed and minimizing missed detections. These advancements significantly bolster the model’s performance, demonstrating advantages for real-world applications. Full article

(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)

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World Electr. Veh. J., Volume 16, Issue 1 (January 2025) – 4 articles

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