Operational Scenarios Research Articles

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neuro-controller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability.

The LLCL filter-based synchronverter with adaptive fuzzy logic controller

Global warming has emerged as a significant issue, primarily due to the use of fossil fuels for power generation. Renewable energy adoption has surged. However, stability issues arise when integrating the primary source and inertia-less nature of converter-interfaced renewable energy sources (RESs) into the traditional power system, due to its intermittent and uncertain nature. The proposed solution involves controlling converter-interfaced RES to emulate the characteristics of a conventional synchronous generator (SG), commonly known as a virtual synchronous generator or synchronverter. As it fundamentally functions as a power converter, it is crucial to consider the appropriate filter design for the synchronverter. Additionally, by emulating the characteristics of SG, the values of “virtual” inertia and droop coefficients must be chosen accurately to provide a robust response under different operating scenarios of the synchronverter. Hence, this paper proposes a novel LLCL filter-based synchronverter system along with an adaptive control strategy using a fuzzy logic controller (FLC). The system is simulated under different operating scenarios in MATLAB/Simulink. The proposed system demonstrated improved frequency response and reduced total harmonic distortion compared to the conventional synchronverter model for all the different operating scenarios, thus enhancing grid stability and power quality.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

Initial design concepts for solid boron injection in ITER

As part of ITER’s consideration to change its first wall material from beryllium to tungsten, the ITER Organization has proposed studying the feasibility of real-time solid boron injection (SBI) into the plasma to coat the walls and divertor to supplement glow discharge boronization (GDB) [1]. Boron deposits getter oxygen and reduce sputtering of tungsten from plasma facing components (PFCs). Particularly in areas with significant plasma wall interactions, boron coatings are expected to be short-lived under high performance plasma conditions. The proposed SBI system aims to maintain boron layers in these areas to avoid excessive radiation from tungsten in the plasma as a risk mitigation to ensure ITER will be able to reach and sustain Q = 10 conditions. The system will be used sparingly, as redeposition of boron can lead to significant tritium retention, which must be minimized in ITER to comply with nuclear safety concerns. SBI is proposed to limit and precisely control the amount of boron injected in real-time during plasma operation. Here, some of the design requirements and initial concepts for an SBI system in ITER are presented based on previous results carried out with SBI systems on a number of tokamaks and stellarators around the world [2–18].Previous results using SBI systems installed by PPPL have injected boron particles from 5 µm–2 mm diameter at calibrated rates of 2–200 mg/s in real-time during plasma operation on AUG [4], DIII-D [5], EAST [6], KSTAR [7], LHD [8], TFTR [9], WEST [10], and W7-X [11,12], leading to improved wall conditions with reduced plasma impurity concentrations and radiated power and improved plasma performance. The boron is ionized in the plasma edge and then deposited on plasma-wetted surfaces. On AUG [4], EAST [6] and WEST [10] reduced tungsten sputtering sources were observed following several discharges with SBI. Extrapolation of these SBI results are presented to estimate the amount of boron needed for wall conditioning in ITER. Real-time SBI control requirements and plasma operation scenarios for ITER are also described.

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Integrated model and automatically designed solver for power system restoration

Power system restoration strategies schedule the power plants to re-energize the power network and loads after a blackout. These strategies are of great significance to ensure a resilient power system. Most power system restoration models consider either certain restoration stage(s) or all stages, with each or some of the stages managed independently. They destroy the essential characteristics of the interaction among the restoration stages and lead to sub-optimal solutions. To address this issue, in this paper, we first propose an improved integrated power system restoration (IPSR) model covering the entire restoration process without decoupling and sectionalization. We then develop both mathematical and metaheuristic solvers for the proposed model. In particular, we introduce the automated solver design paradigm to the highly non-linear constrained restoration problem-solving. By the paradigm, we automatically design the metaheuristic solver that gains enhanced performance beyond handcrafted solvers without algorithmic expertise. It is suggested that the automatically designed metaheuristic solver is a promising new option in variable and complicated power system operation scenarios without algorithmic expertise. Finally, a comprehensive case study demonstrates the proposed model’s significance and the proposed solver’s efficiency. Numerical tests show that the proposed IPSR model and solver obtained over 20% lower restoration time than current restoration methods, demonstrating IPSR’s and the proposed solver’s efficiency and effectiveness.

Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships, car carriers, oil tankers, bulk carriers, and container ships, under six different operational scenarios, such as varying cargo loads and weather conditions. Traditional fuels, like marine gas oil (MGO) and intermediate fuel oil (IFO), challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm, which dynamically adjusts engine parameters, demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL, fuel efficiency increased by up to 10%, while EEXI values decreased by 8% to 15%, and CII ratings improved by 10% to 30% across different scenarios. Specifically, under heavy cargo loads, the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally, the DRL approach consistently outperformed traditional optimization methods, demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels, integrating additional machine learning techniques to enhance optimization.

Mechanical Characterization and Modeling of Large-Format Lithium-Ion Battery Cell Electrodes and Separators for Real Operating Scenarios

This study presents a novel application-oriented approach to the mechanical characterization and subsequent modeling of porous electrodes and separators in lithium-ion cells to gain a better understanding of their real mechanical operating behavior. An experimental study was conducted on the non-linear stiffness of LiNi0.8Co0.15Al0.05O2 and graphite electrodes as well as PE separators, harvested from large-format lithium-ion cells, using compression tests. The mechanical response of the components was determined for different operating conditions, including nominal stress levels, mechanical loading rates, and mechanical cycles. The presented work describes the test procedure, the experimental setup, and an objective evaluation method, allowing for a detailed summary of the observed mechanical behavior. A distinct nominal stress level and mechanical cycle dependency of the non-linear stiffnesses of the porous materials were found. However, no clear dependency on compression rate was observed. Based on the experimental data, a poroelastic mechanical model was utilized to predict the non-linear behavior of these porous materials under real mechanical operating scenarios with a normalized root-mean-squared error less than 5.5%. The results provide essential new insights into the mechanical behavior of porous electrodes and separators in lithium-ion cells under real operating conditions, enabling the accelerated development of high-performing and safe batteries for various applications.

Analysis of Resistance in Magnetic Flux Leakage (MFL) Detectors for Natural Gas Pipelines

This study systematically explores the sources and influencing factors of resistance encountered by magnetic flux leakage (MFL) detectors in natural gas pipelines through a theoretical analysis, experimental investigation, and numerical simulation. The research methodology involves the development of a fluid–structure interaction model using ABAQUS 2023 finite element software, complemented by the design and implementation of a pull-testing platform for MFL detectors. This platform simulates detector operation under various interference conditions and quantifies the resulting frictional resistance. The findings reveal that the primary source of frictional resistance is the contact interaction between the MFL detector and the pipeline wall. Key factors influencing the magnitude of this resistance include the detector’s mass, the structural design and materials of the sealing cups and support plates, as well as the surface roughness of the pipeline. Both experimental results and numerical simulations demonstrate a pronounced increase in frictional resistance with heightened interference levels. The theoretical model exhibits strong agreement with experimental data, though deviations are observed under conditions of severe interference. This study provides a detailed understanding of frictional resistance patterns under diverse structural and operational scenarios, offering both theoretical guidance and practical recommendations for the design of low-resistance MFL detectors.

Study on two-stage robust optimal configuration of integrated energy system considering CCUS and electric hydrogen production

Under the background of complementary and cascade utilization of multiple types of energy, how to carry out the capacity planning of economic, efficient, and low-carbon integrated energy system to meet the diversified needs of cooling, heating, electricity, and hydrogen loads is a challenging problem. This study conducted a two-stage robust optimization (RO) configuration of integrated energy system considering Carbon Capture Utilization and Storage (CCUS) and electric hydrogen production. In order to adapt the configuration scheme to the potential extreme operational scenarios, the uncertainty sets of new energy and load are constructed based on Latin hypercubic sampling. The efficacy is validated through the design of several scenarios and a comparison with alternative methods. The results indicate: 1) Strengthening of carbon emission constraints facilitates the cleaner and electrified transformation of energy. In the shift from “low carbon” to “zero carbon” scenario, the proportion of new energy capacity in power generation equipment increases from 45.1% to 79.8%; the proportion of electric chillers in cooling equipment increases from 1.3% to 7.2%, and reaches 60% under “zero carbon” scenario. 2) With a 36% reduction in the investment cost of new energy, the spatiotemporal transfer value of energy is realized, which increases the investment capacity of energy storage by about 7 times. 3) The configuration of hydrogen production and storage equipment to meet hydrogen demand reduces costs by 19% compared to direct purchasing in “low carbon” scenario. 4) Compared to other methods, the proposed method in this study is able to reduce the solution time and configuration cost.

DC microgrid for EV charging station with EV control by using STSM controllers

Abstract This study explores innovative control strategies for electric vehicle (EV) charging stations in a DC Microgrid powered by solar and wind energy. A new methodology regulates the dc-link voltage through various converters while a modified vector control method enhances the performance of switched reluctance motors (SRMs). The implementation of super twisting sliding mode (STSM) controllers show superior performance compared to traditional PI and Fuzzy controllers. The design of an asymmetrical converter with four battery banks also minimizes charging durations. The real-time test system (RTS) effectively managed power generation within a DC microgrid, demonstrating a stable voltage at the DC bus despite variations in total generation from photovoltaic (PVS) and wind systems. In Case 1, the controller successfully maintained power balance while charging electric vehicles and managing DC loads. During load torque adjustments, the system maintained a steady motor speed of 320 RPM even with a load torque increase from 5 Nm to 50 Nm at 3 s, showcasing its robust vector control strategy. Notably, the system facilitated a reverse operation at 10 km h−1 (80 RPM) by seamlessly adjusting the engine’s reference speed from 80 RPM to −80 RPM at t = 4.0 s. The vector control causes the engine speed heading to be opposite in a natural manner., indicating its innovative capability to handle diverse operational scenarios. The MATLAB/Simulink package serves as the foundation for the proposed model, which is then integrated into OPAL-RT modules to create a Hardware-in-the-Loop (HIL) system for showcasing diverse outcomes. Different outcomes are deliberated with validated justifications of the suggested approach. The research is linked to Sustainable Development Goals 7 (Affordable and clean energy) and 13 (Climate action).

The nexus of intelligent transportation: A lightweight Bi-input fusion detection model for autonomous-rail rapid transit

Autonomous-rail Rapid Transit (ART) possesses various advantages in intelligent transportation, but it does not effectively recognize road conditions caused solely by deploying single-modal cameras. In this paper, a lightweight fusion-based object detection neural network was designed with multi-modal sensors for the ART. Firstly, the Light Detection and Ranging (LiDAR) applied additional encoding and preprocessing to the point cloud. Secondly, a backbone and a detection head of the network structure were proposed through re-parameterization and pruning techniques. Furthermore, a fusion module was designed with a selective soft attention mechanism to fuse the extracted features. The proposed model was tested on the open autonomous driving dataset; it achieved a 7.38% improvement in mean average precision (mAP) compared to the original you only look once (YOLO) as well as other state-of-the-art (SOTA) models. Finally, practical experiments were conducted in the maintenance center of ART to simulate the operational scenarios and validate the feasibility of the proposed method in this study. By fully utilizing the information in different modalities and addressing the limitations of single-modal recognition, efforts were made to improve the robustness of road object detection for ART under different road conditions. Consequently, our method provides effective solutions which benefit intelligent transportation with advanced algorithms and strategies.

Transient and Steady-State Evaluation of Distributed Generation in Medium-Voltage Distribution Networks

As power generation systems with increasingly higher capacities are interconnected with distribution networks, a pressing need arises for a thorough analysis of their integration and the subsequent impacts on medium-voltage lines. This study conducts a comprehensive evaluation, encompassing both steady-state and transient behaviours, leading to a holistic assessment of a real-world biogas generation system integrated into a medium-voltage network. Although the methodology does not introduce revolutionary concepts, its detailed application on a real feeder under various operating conditions adds practical value to the existing body of knowledge. The methodology explores various aspects, including voltage profiles, load capacity, power losses, short-circuit currents, and protection coordination in steady-state conditions. Additionally, a transient analysis is performed to examine the system’s response under fault conditions. This systematic approach provides a deep understanding of the system’s behaviour across diverse operational scenarios, enriching the field with practical insights. The key contributions of this study include identifying the effects of distributed generation systems (DGSs) on short-circuit currents, protection coordination, and defining voltage levels that briefly exceed the CBEMA quality curve. The benefits of incorporating a generation system into a distribution network are discussed from various technical perspectives. In a peak demand scenario, with a 1.72 MW generation capacity, the phase current experiences a notable reduction of 35.78%. Concurrently, the minimum peak demand voltage increases from 12.62 to 12.83 kV compared to a nominal voltage of 12.7 kV. Furthermore, the contribution of the generation system to the short-circuit current remains minimal, staying below 4% even under the most adverse conditions. However, our findings reveal that voltage levels exceed the upper limit of the CBEMA quality curve briefly during a single-phase fault with generation, which could potentially damage electronic equipment connected to the grid. Nonetheless, the likelihood of encountering a single-phase grounding fault with zero resistance remains low.

A review of the current status and common key technologies for agricultural field robots

The arrival of the Industrial Revolution 4.0 and the quick advancement of artificial intelligence (AI), cloud computing, and internet of things (IoT) have led to the beginning of a new technological revolution in agriculture. As an important branch of agricultural robots, field robots have an irreplaceable role in guaranteeing food security, seizing the global agricultural high ground, and realizing sustainable agricultural development. In this study, we first clarified the definition of field robots and review the different development stages of agricultural robots. Secondly, we searched and screened 678 relevant literature from electronic databases such as Science Direct, Web of Science, etc., and quantitatively analyzed all the literature in terms of three dimensions: year of publication, country, and keywords. Then, we systematically provided the current research status of ploughing-seeding, weeding, monitoring, harvesting, and information-gathering, crop protection and agricultural fly robots. Furthermore, the global development of field robots shows a trend of rapid growth and technology upgrading, and we proposed the common key technologies of field robots, including intelligent sensing, smart decision-making and control, dexterous arm and hand operation, autonomous and stable walking, and Cloud-Edge-End fusion intelligent operation. Finally, we summarized the challenges faced by robots, such as low sensing accuracy, poor operability, and low operating efficiency. Therefore, we attempted to provide new opportunities for the future development of field robots, such as new technologies such as unstructured information analysis, 5G, AI, digital twins, robots swarms collaboration, and other technologies, which enable the realization of field operation scenarios with fully autonomous perception.

Advanced Distribution System Optimization: Utilizing Flexible Power Buses and Network Reconfiguration

The increasing integration of distributed generation (DG) and the rise of microgrids have reshaped the operation of distribution systems, introducing both challenges and opportunities for optimization. This study presents a methodology that combines network reconfiguration with the integration of buses with flexible power in order to improve the efficiency and cost-effectiveness of distribution networks. Flexible buses, which aggregate multiple microgrids or controllable distributed resources, function as control points that can dynamically adjust active and reactive power within predefined limits. This capability allows for more precise management of power flows, enabling the system to respond to fluctuations in generation and demand. The proposed optimization framework aims to minimize the total operational costs, including power losses and the use of flexible power, while adhering to system constraints. The methodology is evaluated through case studies on two distribution systems: the Kumamoto and IEEE-33 systems. The results indicate a 43.9% reduction in power losses for the Kumamoto system and a 66.6% reduction for the IEEE-33 system, along with notable cost savings in both cases. These outcomes demonstrate the potential benefits of incorporating flexible power buses in modern radial distribution networks, showing their role in adapting to various operational scenarios and supporting the integration of distributed generation and microgrids.

Study of Radio Reconnaissance Receivers Designed on the Basis of Solid-state Filters

Radio intelligence systems are vital technological resources that contribute significantly to contemporary security, defense, and intelligence efforts. They enable the monitoring of adversaries' communication signals, the decryption of encrypted information, and the acquisition of strategic advantages. These systems are essential for military operations, counter-terrorism efforts, national security, and strategic planning. As radio intelligence continues to evolve, it is anticipated that more effective and multifunctional systems will emerge in the future.In modern electronic warfare, radio reconnaissance stations are essential for effective signal detection, classification, and geolocation of enemy communication and radar signals. Current advancements in radio reconnaissance technology focus on enhancing measurement accuracy and reliability, crucial for precise threat identification and situational awareness. Key improvements include implementing high-resolution digital signal processing algorithms, advanced filtering techniques, and real-time data fusion to increase detection precision across complex environments. Furthermore, there is a strategic emphasis on reducing the physical dimensions and weight of these stations to improve mobility and operational efficiency in diverse field conditions. Innovations in miniaturized components, lightweight materials, and compact antenna arrays are central to these design objectives, facilitating deployment in both manned and unmanned platforms. This approach not only optimizes the performance of reconnaissance stations but also broadens their applicability across various operational scenarios, making them integral to modern defense systems. In the article, the application of solid-state filters in the spectrum analyzers of radio reconnaissance receivers was considered in order to increase the measurement accuracy and reliability of radio reconnaissance stations, and to reduce their size and weight. In the article, the modeling of load-connected filter structures was conducted to calculate quasi-statistical potential diagrams, which allows for the optimization of their parameters and reduces the design time of devices based on these filters. The research work was carried out in military unit No. N.

Study on Path Planning in Cotton Fields Based on Prior Navigation Information

Aiming at the operation scenario of existing crop coverage and the need for precise row alignment, the sowing prior navigation information of cotton fields in Xinjiang was used as the basis for the study of path planning for subsequent operations to improve the planning quality and operation accuracy. Firstly, the characteristics of typical turnaround methods were analyzed, the turnaround strategy for dividing planning units was proposed, and the horizontal and vertical operation connection methods were put forward. Secondly, the obstacle avoidance strategies were determined according to the traits of obstacles. The circular arc–linear and cubic spline curve obstacle avoidance path generation methods were proposed. Considering the dual attributes of walking and the operation of agricultural machinery, four kinds of operation semantic points were embedded into the path. Finally, path generation software was designed. The simulation and field test results indicated that the operation coverage ratio CR ≥ 98.21% positively correlated with the plot area and the operation distance ratio DR ≥ 86.89% when non-essential reversing and obstacles were ignored. CR and DR were negatively correlated with the number of obstacles when considering obstacles. When considering non-essential reversing, the full coverage of operating rows could be achieved, but DR would be reduced correspondingly.

Study on the Whole Lifecycle Energy Consumption Scenarios of Expressway

This paper constructs a set of energy consumption scenarios for typical expressway construction, operation and maintenance phases, and analyses the energy demand under each scenario. Firstly, a comprehensive lifecycle energy consumption framework for expressway is established, detailing the energy consumption scenarios during construction, operation, and maintenance phases. Secondly, based on literature review and scenario assumptions, the study examines the energy consumption levels under different scenarios and analyses the proportion of energy consumption in each scenario. The results show that the ratio of energy consumption during the construction, operation and maintenance phases of expressway is about 5:10:3. The energy consumption during the construction phase is the highest for the construction of expressway trunk lines, accounting for about 80.6% of the total energy consumption. During the operation phase, fuelling and refuelling stations consume the most energy, accounting for about 71.4%. The energy consumption of expressway trunk lines is the highest during maintenance phase, which accounting for about 70%. This analysis facilitates a deeper understanding of the characteristics of the integration between transportation and energy networks, and assists the transportation sector in achieving carbon peak and carbon neutrality goals in a scientifically sound manner.