Search Results (393)

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil fuel vehicles. Additionally, electric vehicles are highly efficient, with an efficiency of around 90%, in contrast to fossil fuel vehicles, which have an efficiency of about 30% to 35%. The higher energy efficiency of electric vehicles contributes to lower operational costs, which, alongside regulatory incentives and shifting consumer preferences, has increased their strategic importance for many vehicle manufacturers. In this paper, we present a thematic literature review on electric vehicles charging station location planning and scheduling. A systematic literature review across various data sources in the area yielded ninety five research papers for the final review. The research results were analyzed thematically, and three key directions were identified, namely charging station deployment and placement, optimal allocation and scheduling of EV parking lots, and V2G and smart charging systems as the top three themes. Each theme was further investigated to identify key topics, ongoing works, and future trends. It has been found that optimization methods followed by simulation and multi-criteria decision-making are most commonly used for EV infrastructure planning. A multistakeholder perspective is often adopted in these decisions to minimize costs and address the range anxiety of users. The future trend is towards the integration of renewable energy in smart grids, uncertainty modeling of user demand, and use of artificial intelligence for service quality improvement. Full article

This research presents a method for improving the regenerative efficiency of interior permanent magnet synchronous motors (IPMSMs) used in traction applications such as electric vehicles. In conventional powertrain control, the maximum torque per ampere (MTPA) strategy is commonly applied in the constant-torque region. However, this approach does not account for the combined losses of both the motor and inverter. In this study, overall system efficiency is investigated, and an improved current combination is proposed to minimize total losses. The single switching method is employed in the inverter due to its simplicity and its ability to reduce inverter losses. Simulations incorporating both motor and inverter losses were performed for two driving conditions around the MTPA current point. The results show that the optimal current combination slightly deviates from the MTPA point and leads to a slight improvement in efficiency. Experimental results under the two steady-state driving torque and angular velocity conditions confirm that the optimized current combination enhances system efficiency. Furthermore, simulations based on the Urban Dynamometer Driving Schedule predict an increase in recovered energy of approximately 1%. The proposed control strategy is simple, easy to implement, and enables the powertrain to operate with highly efficient current references. Full article

Onboard Vehicle-to-Everything (V2X) communication technology is being widely implemented in domains such as intelligent driving, vehicle–road cooperation, and smart transportation. Nevertheless, time synchronization in V2X systems suffers from instability due to the random loss of Global Navigation Satellite System (GNSS) Pulse-Per-Second (PPS) signals. To address this challenge, a model-driven local clock correction approach is proposed. Leveraging probability theory and mathematical statistics, models for the randomly lost GNSS PPS signals are developed. High-order polynomials are used to model local clocks. An optimized Kalman-filter-based time compensation algorithm is then devised to compensate for time errors during PPS signal loss. A software-based task-scheduling solution for precision-time synchronization is developed. An experimental testbed was then built to measure both terminal clocks and PPS signals. The proposed algorithm was integrated into the V2X terminals. Results show that the full-value PPS signals follow an exponential distribution. The onboard clock correction algorithm operates stably across three V2X terminals and accurately predicts clock variations. Furthermore, the virtual clocks achieve an average absolute error of 1.1 μs and a standard deviation of 16 μs, meeting the time synchronization requirements for V2X communication in intelligent connected vehicles. Full article

This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor–critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications. Full article

Hybrid tractors, as an efficient and environmentally friendly power system, are gradually becoming an important technical choice in the agricultural field. Compared to conventional powertrain systems, hybrid electric powertrains can achieve a 15–40% reduction in fuel consumption. By optimizing the engine operating range and incorporating electric-only driving modes, these systems further contribute to a 20–35% decline in CO₂ emissions, along with a significant mitigation of nitrogen oxides (NOx) and particulate matter (PM) emissions. In this paper, the energy management technology of hybrid tractors is reviewed, with emphasis on the energy scheduling between the internal combustion engine and electric motor, the optimization control algorithm, and its practical performance in agricultural applications. Firstly, the basic configuration and working principle of hybrid tractors are introduced, and the cooperative working mode of the internal combustion engine and electric motor is expounded. Secondly, the research progress of energy management strategies is discussed. Then, the application status and challenges of hybrid power systems in agricultural machinery are discussed, and the development trend of hybrid tractors in the fields of intelligence, low carbonization, and high efficiency in the future is prospected. This paper extracts many experiences and methods from the references over the years and provides a comprehensive evaluation. Full article

As Industry 4.0 advances, emerging use cases demand 5G NR networks capable of delivering high uplink throughput and ultra-low downlink latency. This study evaluates a 5G link between a LiDAR-equipped unmanned ground vehicle (UGV) and its control unit using a configurable industrial testbed. Based on 3GPP standards and related literature, we identified latency and uplink throughput as key factors for real-time control. Experiments were conducted across different gNB configurations and attenuation levels. The results show that tuning parameters such as CSI, TRS, and SSB significantly improves performance. In this study, we provide a practical analysis of how these parameters influence key metrics, supported by real-world measurements. Furthermore, adjusting the Scheduling Request period and PDCCH candidate settings enhanced uplink reliability. Several configurations supported high LiDAR uplink traffic while maintaining low control latency, meeting industrial 3GPP standards. The configurations also met throughput requirements specified in UAV-related 3GPP standards. In favorable radio link conditions, selected configurations were sufficient to also enable cooperative driving or even machine control. This work highlights the importance of fine-tuning parameters and performing testbed-based evaluations to bridge the gap between simulation and deployment in Industry 4.0. Full article

Cleaning operations in narrow pipelines are often hindered by limited maneuverability and low efficiency, necessitating the development of a high-performance and highly adaptable robotic solution. To address this challenge, this study proposes a pipeline-cleaning robot specifically designed for the heat-exchange tubes of industrial heat exchangers. The robot features a dual-wheel cross-drive configuration to enhance motion stability and integrates a gear–rack-based alignment mechanism with a cam-based propulsion system to enable autonomous deployment and cleaning via a flexible arm. The robot adopts a modular architecture with a separated body and cleaning arm, allowing for rapid assembly and maintenance through bolted connections. A vision-guided control system is implemented to support accurate positioning and task scheduling within the primary pipeline. Experimental results demonstrate that the robot can stably execute automatic navigation and sub-pipe cleaning, achieving pipe-switching times of less than 30 s. The system operates reliably and significantly improves cleaning efficiency. The proposed robotic system exhibits strong adaptability and generalizability, offering an effective solution for automated cleaning in confined pipeline environments. Full article

Internal short-circuit (ISC) is a critical failure mode in lithium-ion (Li-ion) batteries that can trigger thermal runaway and pose serious risks to both environmental and human safety. Early-stage ISC faults are particularly challenging to detect due to their subtle characteristics and the masking effects of voltage fluctuations. To address these challenges, this study proposes a rapid and accurate ISC diagnosis method based on the connectivity-based outlier factor (COF) algorithm. The key innovation lies in the preprocessing of terminal voltage to amplify fault signatures and suppress natural fluctuations, thereby enhancing sensitivity to early anomalies. The COF algorithm is then applied to identify ISC faults in real time. Validation under urban-dynamometer driving schedule (UDDS) conditions demonstrates the method’s effectiveness: it successfully detects early ISC faults with an equivalent resistance as high as 100 Ω within 207 s of onset. This unsupervised, data-driven approach improves fault detection speed and accuracy, contributing to the advancement of safe, reliable, and sustainable LIB deployment in clean energy and transportation systems. Full article

Functioning as a smart aggregation entity that combines distributed energy resources, energy storage systems, and flexible loads, virtual power plants (VPPs) serve as a pivotal technology in advancing the decarbonization and flexibility enhancement of modern power systems. Initially, we summarize the developmental context, evolutionary trajectory, and conceptual framework of VPPs. The architecture is functionally partitioned into three tiers: the aggregation layer, communication layer, and dispatch optimization layer (central layer). The dispatch optimization layer of VPPs serves as the “intelligent brain” connecting physical resources with electricity markets, whose core lies in achieving “controllable, adjustable, and optimizable” distributed resources through algorithmic and data-driven approaches, driving the energy system transition towards low-carbon, flexible, and efficient directions. Next, we critically examine core technologies in the dispatch optimization layer, particularly the response capacity assessment and optimal resource scheduling. Its content mainly focuses on the latest research on the aggregated resource response capability evaluation, virtual power plant dispatching optimization models, and dispatching strategies. Conclusively, we analyze prevailing technical bottlenecks and summarize significant advancements, concluding with prospective insights into future research frontiers and developmental priorities for VPPs. In the future energy system transition, VPPs will play an increasingly important role. It is foreseeable that the utilization efficiency of renewable energy will be significantly enhanced, and the energy market will become more diverse and vibrant. We look forward to VPPs integrating more quickly and effectively into daily life, transforming lifestyles and helping people collectively step into a low-carbon, green future. Full article

Diversified application scenarios and business models are effective ways to improve the utilization and economic benefits of energy storage systems. In response to the current problems of single application scenarios, high idle rates, and imperfect price formation mechanisms faced by energy storage on the power generation side, a robust two-stage optimization operation strategy for shared energy storage is proposed, taking into account leasing demand and multiple uncertainties, from the perspective of the sharing concept. A multi-scenario application framework for shared energy storage is established to provide leasing services for wind farm clusters, as well as auxiliary services for participating in the electric energy markets and frequency regulation markets, and the participation sequence is streamlined. Based on the operating and opportunity costs of shared energy storage, a pricing mechanism for leasing services is designed to explore the driving forces of wind farm clusters participating in leasing services from the perspective of cost assessment. Considering the uncertainty of wind power output and market electric prices, as well as the market operational characteristics, an optimized operation model for shared energy storage in the day-ahead and real-time stages is constructed. In the day-ahead stage, a Stackelberg game model is introduced to depict the energy sharing between wind farm clusters and shared energy storage, forming leasing prices, leasing capacities, and energy storage pre-scheduling plans at different time periods. In the real-time stage, the real-time prediction results of wind power output and electric prices are integrated with scheduling decisions, and an improved robust optimization model is used to dynamically regulate the pre-scheduling plan for leasing capacity and shared energy storage. Based on actual data from the electricity market in Guangdong Province, effectiveness verification is conducted, and the results showed that diversified application scenarios improve the utilization rate of shared energy storage in the power generation side by 52.87%, increasing economic benefits by CNY 188,700. The proposed optimized operation strategy has high engineering application value. Full article

NAND flash memory has been widely adopted as the primary data storage medium in data centers. However, the inherent characteristic of out-of-place updates in NAND flash necessitates garbage collection (GC) operations on NAND flash-based solid-state drives (SSDs), aimed at reclaiming flash blocks occupied by invalid data. GC processes entail additional read and write operations, which can lead to the blocking of user requests, thereby increasing the tail latency. Moreover, frequent execution of GC operations is prone to induce more pages to be written, further reducing the lifetime of SSDs. In light of these challenges, we introduce an innovative GC scheme, termed SplitGC. This scheme leverages the records of data redundancy gathered during periodic read scrub operations within the SSD. By analyzing these features of data duplication, SplitGC enhances the selection strategy for the victim block. Furthermore, it bifurcates the migration of valid data pages into two phases: non-duplicate pages follow standard relocation procedures, whereas the movement of duplicate pages is scheduled during idle periods of the SSD. The experiment results show that our scheme reduces tail latency induced by GC by 8% to 83% at the 99.99th percentile and significantly decreases the amount of valid page migration by 38% to 67% compared with existing schemes. Full article

This study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though Modes #2, #4, and #5 were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity. Full article

Battery electric vehicles (BEVs) have emerged as a cornerstone of sustainable transportation systems, driving a fundamental transformation in public transport (PT) operations over the past decade. The unique characteristics of BEVs, including range limitations and battery degradation dynamics, necessitate a multi-dimensional optimization framework that simultaneously considers energy supply management, operational efficiency, and battery lifecycle optimization in transit scheduling and timetabling. This paper presents a systematic review of BEV timetabling and scheduling research, structured around three main contributions. First, it comprehensively examines the evolution of electric vehicle timetabling problems, providing a detailed comparative analysis of methodological approaches in this domain. Second, it identifies and critically evaluates key developments in electric vehicle scheduling, including extended research directions (such as the integration with crew scheduling) and their practical implications. Third, it investigates the integration of BEV scheduling and timetabling, synthesizing the strengths and limitations of current methodologies while outlining promising avenues for future research. By offering a comprehensive analysis of the advancements in battery electric public transport scheduling over the past decade, this review serves as both a technical reference and a strategic guide for researchers and practitioners in the field of sustainable transportation systems. Full article

The agricultural sector faces growing environmental and societal pressures to balance natural resource use with food security, particularly within the Water-Energy-Food-Ecosystems Nexus (WEFE). Increasing water demand, competition, and challenges like droughts and desertification are driving the need for innovative irrigation practices. Pressurized irrigation systems, such as sprinkler and micro-irrigation, are gaining prominence due to their automation, labor savings, and increased water application efficiency. To support farmers in designing and managing these systems, the R&D project AGIR developed a computational tool that simulates water application efficiency under site-specific conditions. The tool integrates key parameters, including system design, scheduling, soil properties, topography, meteorological data, and vegetation cover, providing a robust methodological framework with classification criteria for evaluating irrigation options. Validated using data from six case studies, the tool achieved simulated irrigation efficiencies of 73% to 90%, which are consistent with field observations. By simplifying complex irrigation requirement calculations, the model offers a user-friendly alternative while maintaining accuracy at the farm level. This innovative tool enables stakeholders to optimize irrigation systems, reduce water losses, and establish standardized recommendations for design, management, performance, and socio-economic considerations. It represents a significant step forward in supporting sustainable water management and advancing the goals of Agriculture 4.0. Full article