The Contribution of Electric Vehicles to the Realization of the Dual-Carbon Goal

1. Introduction

The transportation sector is the main source of environmental pollution in cities, fueling the energy crisis. Governments and the scientific community are committed to seeking alternatives to conventional fuel-powered vehicles and supporting the realization of the dual-carbon goal, including carbon peaking and carbon neutrality. With their high energy efficiency and sustainability, electric vehicles (EVs) are widely recognized as a promising green and low-carbon transportation mode. Accordingly, remarkable progress has been achieved in recent years in EV technologies, charging infrastructure deployment, battery performance, and intelligent operation systems. Rapid developments in artificial intelligence and renewable energy integration have further accelerated the transition from fossil fuel-based transportation to electrified mobility [1]. Driven by this trend, extensive attention has been devoted to investigating key technologies, operational strategies, and planning frameworks for EVs. For instance, existing studies have explored route optimization and charging strategy design for EVs [2,3,4], analyzed the layout planning of public charging infrastructure [5,6,7], and evaluated pathways to improve battery performance [8,9,10]. Meanwhile, several review papers have synthesized advances in operational management and system-level impact analysis for EV systems [11,12]. Nevertheless, critical knowledge gaps still hinder the large-scale and reliable deployment of EVs. For example, battery degradation effects on fleet scheduling are rarely fully considered in optimization models; comprehensive comparisons of EV range prediction methods are insufficient; and synergies among vehicle efficiency, solar energy utilization, and urban low-carbon transport systems lack systematic investigation. Furthermore, the value of big data in supporting integrated EV decision-making has yet to be fully exploited. In practice, key challenges also persist, including limited driving ranges, insufficient and unevenly distributed charging facilities, and lengthy charging processes. These technical and operational bottlenecks restrict user acceptance and hinder the widespread application of EVs in private travel, public transit, and urban freight distribution. Therefore, systematic research is required to provide theoretical guidance and decision support for the extensive adoption of EVs in urban transportation systems, so as to accelerate the green transformation of the transport sector and contribute to the realization of the dual-carbon goal.

This Special Issue collects the latest advances in key technologies and management methods of EVs, aiming to reflect cutting-edge research and practical solutions in this field. A total of twelve research articles and one systematic review are included, covering core themes closely matching the published papers, including, but not limited to, optimal scheduling of electric bus fleets considering battery degradation; breakthroughs in vehicle efficiency to expand solar-powered electric transit; and systematic review of EV range prediction models based on machine learning and mathematical and simulation methods. In addition, the issue also covers route and charging strategy optimization, charging infrastructure planning, battery state estimation, life-cycle cost assessment, advanced charging technology, and big data applications in EV operation. It should be clarified that this Editorial does not intend to elaborate on each paper in detail, but rather to encourage readers to explore these high-quality contributions. We hope this Special Issue can promote academic exchanges and interdisciplinary cooperation, and provide solid theoretical and technical support for the development of EVs and the realization of the dual-carbon goal.

2. An Overview of Published Articles

Amin et al. (Contribution 1) systematically reviewed 80 studies on EV range prediction published between 2013 and 2024, and categorized these approaches into machine learning, simulation modeling, mathematical modeling, and hybrid models. Neural networks, multiple linear regression, and decision trees were the most widely used machine learning techniques, with hybrid models gaining traction for improved accuracy. The study identified unsolved challenges including robust feature selection, real-time data integration and battery degradation modeling. They also recommended that future research focus on multi-method integration, advanced data-driven approaches and reliability improvement.

Li et al. (Contribution 2) investigated battery degradation in electric bus fleet operations by developing an integrated optimization methodology combining capacity degradation and scheduling optimization models. The model incorporates procurement, charging, and battery degradation costs, and has been validated using real-world bus system data. The results from their case study indicate that the model can reduce total operating costs to 92% of the original level, proving its effectiveness in cost reduction for electric bus fleets.

Malozyomov et al. (Contribution 3) constructed a complex mathematical model of an electric truck, including traction battery, inverters, and asynchronous motors, to determine battery discharge depth, charging/discharging currents, and other key parameters under real driving cycles. Having been validated through experimental measurements such as battery charge level and motor temperature, the model provides critical data to support the performance optimization of traction batteries.

Pryalukhin et al. (Contribution 4) analyzed the energy efficiency of electric dump trucks in open-pit mining, evaluating centralized, autonomous, and combined power supply systems. The findings indicate that road gradient and length have minimal influence on energy consumption, whereas regenerative braking systems exhibit low efficiency during load lifting operations. They proposed eliminating regenerative braking to simplify design, increase battery capacity, and extend operating time without recharging.

Cai et al. (Contribution 5) used an evolutionary game model to analyze Macao residents’ EV purchase willingness and government promotion strategies. While tax exemptions and subsidies have improved the appeal of EVs, high upfront costs, limited driving ranges, and prolonged charging times continue to present significant barriers. The study recommended aligning subsidies with market dynamics, expanding charging infrastructure, and boosting residents’ environmental awareness to drive wider EV adoption.

Liao and Wu (Contribution 6) investigated the impact of EV brand greenwashing on consumer purchase intentions, such as subsidy cheating and exaggerated carbon reduction claims. The experimental results reveal that brand greenwashing fosters industry-wide skepticism and diminishes cross-brand purchase intentions. Consumer innovativeness and attitudes toward peer brands act as negative moderators in this relationship, offering valuable implications for ethical marketing practices.

Pino-Servian et al. (Contribution 7) proposed a quality function deployment (QFD)-based methodology to integrate customer needs into EV market deployment. QFD enhances strategic decision-making and market penetration by optimizing product improvements and resource allocation. Furthermore, the integration of EVs with renewable energy, advanced battery technologies, and grid solutions yields further environmental and economic advantages.

Sai et al. (Contribution 8) analyzed the impact of COVID-19 on electric car-sharing (ECS) travel using real-world order data. The results indicate that ECS orders decreased by 55.8% in the post-pandemic period. Meanwhile, significant differences in travel behavior were observed between private car owners and non-owners. The study offered vehicle distribution recommendations to improve ECS operators’ profitability during public health crises.

Qiu et al. (Contribution 9) developed a carbon-efficiency-centered bi-objective optimization framework comparing mobile charging stations, fixed charging stations, and battery swapping stations for port horizontal transportation. Based on real-world port data, the results demonstrate that mobile charging stations can reduce deadheading mileage and infrastructure demand by utilizing idle windows. The trade-off between emissions reduction and profit improvement depends on idle reuse levels, with an effective threshold ranging from 0.5 to 0.75, providing valuable guidance for port electrification and infrastructure planning.

Suppes and Suppes (Contribution 10) introduced ground effect vehicle technology to reduce rolling and aerodynamic resistance by up to 80%, surpassing conventional streamlining. This breakthrough enables EVs to achieve higher speeds at the same power consumption. When combined with batteries and solar panels, it creates competitive advantages over fossil fuel vehicles and helps transform transportation systems.

Hou et al. (Contribution 11) analyzed Norwegian EV accident data from 2020 to 2021. Their study identified rear-end collisions as the most frequent accident type, with high-risk scenarios including medium–low speed roads, good visibility, and dry road surfaces. The findings from Ordered Logit models indicate that accident severity is significantly influenced by time of day, speed limits, and road medians, which provides evidence for the formulation of targeted traffic safety interventions.

Zuo et al. (Contribution 12) developed a personalized EV charging guidance framework based on collaborative filtering to capture multi-dimensional user preferences, covering energy cost, charging time and associated fees. A multi-objective optimization model is constructed and validated using real-world road network data. The model is able to achieve balanced optimal performance across these preferences with controlled differences in energy cost, time consumption, and charging fees, thereby effectively alleviating drivers’ range anxiety.

Quintana et al. (Contribution 13) employed a structured methodology to assess the feasibility of replacing internal combustion vehicles with EVs among Ecuador’s electricity supply enterprises. The methodology encompasses requirement determination, technical comparison, and emission estimation. The results indicate that three categories of internal combustion vehicles can be substituted with EVs, leading to an 85% reduction in carbon dioxide emissions under ideal conditions and a 56% reduction under realistic scenarios that account for market and technical constraints.

3. Conclusions

This compilation of papers devoted to outlining EVs’ contribution to realizing the dual-carbon goal demonstrates a wealth of diverse and cutting-edge research outcomes, demonstrating the academic value and practical significance of this field. The methodologies adopted are also varied, from big data analytics to mine user travel patterns and vehicle operation characteristics to mathematical optimization methods to tackle fleet scheduling and charging infrastructure planning problems. These approaches reflect the interdisciplinary, multi-method, and evidence-based nature of EV research.

In terms of research themes, focusing on the contribution of EVs to realizing the dual-carbon goal, this Special Issue presents a comprehensive and multi-perspective research vision covering technology, market, operation and urban transportation systems. For example, Contributions 1–4 highlight the importance of core technical breakthroughs and performance optimization, investigating EV range prediction, battery degradation modeling, vehicle power system design and energy efficiency improvement. Their findings provide fundamental support for enhancing vehicle reliability, energy utilization and technical feasibility of low-carbon development. Contributions 5–8 analyze EV user purchase intention, consumer behavior, market promotion strategies and car-sharing operation rules from the perspective of market deployment; the discussions center on policy incentives, consumer trust, demand-oriented design and post-pandemic mobility changes, which are conducive to the further popularization and application of EVs and accelerate the achievement of the dual-carbon goal. Contributions 9–12 focus on the operational level, exploring low-carbon-oriented path planning, intelligent charging strategies, safety driving patterns and efficient infrastructure allocation; the results provide decision support for effectively improving EV operation efficiency and reducing energy consumption and carbon emissions. Contribution 13 explores the sustainable urban transport and vehicle electrification transformation, analyzing the feasibility and carbon emission reduction benefits of replacing fuel vehicles with EVs in public utility fleets; the findings offer theoretical support for further clarifying the value orientation of green transport and large-scale electrification transitions.

Although substantial progress has been made in current EV-related research, several challenges remain to be further explored. Future research can be conducted in the following areas: Firstly, it is necessary to strengthen the integrated modeling of battery degradation, renewable energy consumption and real-time traffic big data, so as to improve the accuracy and adaptability of optimization models. Secondly, long-term life-cycle carbon accounting and economic evaluation for EVs under different scenarios should be carried out, which can support scientific policy formulation and infrastructure investment. Thirdly, the synergistic mechanism between EV operations, smart grid connection and urban transportation systems needs to be further explored, in order to realize systematic carbon reductions across the entire transportation chain. The papers included in this Special Issue comprehensively embody the thematic innovation and forward-looking thinking of EV research with regard to the dual-carbon goal. These latest research outcomes not only exemplify the researchers’ exploration achievements, but also serve as a starting point for follow-up studies, encouraging the academic community to initiate in-depth discussions around relevant topics, promote technological innovation and industrial transformation, and enhance the contribution of EVs to carbon peaking and carbon neutrality.