Under the background of the energy transformation promoted by “double carbon”, the smart grid faces two major problems: renewable energy grid connection fluctuates large, and the load peak and valley difference are large.This paper takes the integration of smart grid and V2G technology as the research core, first sorts out the core concepts, technical characteristics and fusion mechanism of the two, and clarifies the V2G The working principle of the technology and the synergy with the smart grid; then from the three dimensions of power grid load adjustment, distributed energy storage system construction, electric vehicle popularization and energy structure transformation, V2G is analyzed The core development potential of the technology; then analyze the challenges of the incomplete standard system faced by its large-scale landing, imbalance of investment income, and lack of enthusiasm for participating in multiple parties. Studies show that V2G technology can effectively enhance grid resilience and accommodate renewable energy, making it highly valuable for smart grids.This paper constructs a theoretical framework for the deep integration of V2G technology and smart grid, provides practical guidance for the industrial application of V2G technology, and helps the realization of energy transformation and “dual carbon” goals.
Additive Manufacturing is a way of producing that has become an innovative approach in contemporary mechanical engineering. However, its application in practice has some well-defined intrinsic limits. In this paper, the authors present an exhaustive, methodical analysis of the kind of operability of the seven different AM techniques, in which the basic advantages and intrinsic limits of the seven techniques, when compared to usual production, are seen, as well as the effectiveness and the still present problems of the recent technological advances. The investigation also collects the gist of the principles of the successful industrial implementation in a value-oriented way. The research results show that, given the present technological capacities and economic considerations, the AM works the best for tangles, high performances, low number of pieces, with a big added value, instead of simple, mass-produced pieces with little value addition. The technological developments, which are still ongoing, are continuously changing the limits of the applicability of the integrated hybrid manufacturing systems.
With the rapid development of the global energy background, the situation that traditional energy sources and emerging energy sources are independent and do not interfere with each other has long been unable to maintain the world's energy supply. The oil system is regarded as a representative of the traditional energy field, while the power grid system represents the emerging energy giants. The two complement each other, and the realization of coupled and coordinated development will undoubtedly become one of the common goals of countries around the world in the future. This article summarizes the overview of the coupling system of “oil and gas storage and transportation and intelligent power grid”. This article first introduces the scheduling and development of the two systems of oil and gas storage and transportation and intelligent power grid respectively; secondly, it introduces the coupling status of the two energy systems, points out the difficult problems of the coupling system based on the current situation, and finally summarizes the research conclusions, points out the significance of the coupling and coordination of the oil and gas storage and transportation system and the intelligent power grid system. There is a prospect of collaborative development in the future.
Oil and gas are still the cornerstones of industrial civilization, and despite increasing resource depletion and carbon emission pressures, more than 60% of the world's energy consumption still depends on fossil fuels. The global energy system is currently in a critical phase of “transition and dependence”, characterized by a contradiction between the indispensability of fossil fuels in the short term and the urgency of decarbonization in the long term. This study examines the global oil and gas supply and demand patterns, analyzing resource concentration patterns, “impossible triangle” challenges (energy security, economic affordability, environmental sustainability), and transition paths. The results show that global oil and gas demand will peak at 43 billion tons around 2030, and then gradually decline to 32 billion tons by 2050, while the proportion of zero-carbon energy in the global energy mix will rise from 25% to 55% during the same period. The study points out that carbon capture, utilization and storage (CCUS) technology, hydrogen integration, and industry-specific alternative strategies are key transformation mechanisms. In this transformation process, strategic reserves and supply chain diversification have become key to ensuring energy security.
Soft robots made of low Young's modulus flexible materials have advantages over traditional rigid robots in terms of adaptability and safety in unstructured environments, and show great application potential in fields such as industrial grasping, medical assistance, and special operations. However, the inherent nonlinearity, viscoelasticity, and infinite degrees of freedom of soft robots make precise control extremely challenging, becoming a major obstacle to their practical application. This paper reviews the mainstream control strategies in the field of soft robotics, analyzes in detail the principles, advantages and limitations of four core control methods based on motion models, machine learning, morphological computation and sensor feedback, and point out the key technical difficulties such as underactuated system dynamics, nonlinear hysteresis effect and flexible sensor integration. The single control method is insufficient to fully address existing challenges. In the future, soft robot control will develop towards a hybrid strategy that integrates physical modeling accuracy, machine learning data processing capabilities, and passive adaptation through morphological computation.
This paper summarizes the development process from traditional topology optimization to the application of generative artificial intelligence (AI) technology in the lightweight design of drone structures. Firstly, it introduces the achievements and existing problems of traditional topology optimization methods in drone structure design, such as the density method, level set method, evolutionary structural optimization, etc. Then, it introduces the research progress of combining machine learning technology with topology optimization, mainly focusing on new methods for structural optimization, such as deep neural networks, reinforcement learning, and genetic algorithms. Next, it discusses generative AI technology, especially the application of generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models in drone structure design, analyzing their advantages in design space exploration, multi-objective optimization, and constraint handling. By comparing different methods, it can be seen that generative AI has significant advantages over traditional methods in terms of design speed, novelty, and complex structures, achieving better lightweight effects and mechanical properties. At the same time, this paper also analyzes the problems faced by AI-assisted design, such as data quality, model interpretability, and engineering applicability, and provides solutions.
This paper, based on the Lagrange method, regards the active torque of the manipulator as a known disturbance to make up for the defect that the UAV system was underactuated before, and the UAV with an arm is transformed into a fully driven, strong-coupling system to establish the dynamic model of the UAV. Based on the dynamic model, the outer loop position sliding mode controller and the inner loop attitude sliding mode controller are designed, respectively, and the joint torque and trajectory of the manipulator are used as feedforward in the inner and outer loop control law to form a double loop feedforward sliding mode controller. Then MATLAB is used to simulate the UAV hover+robot arm forward swing, UAV hover+robot arm periodic swing, UAV circular motion+UAV periodic swing. The stability performance of the controller designed in this paper and the PID controller with feedforward is compared. The simulation results show that the performance of the dual loop feedforward sliding mode controller designed in this paper is significantly better than that of the feedforward PID controller.
Quadruped robots possess outstanding terrain adaptability and boast extensive application prospects in scenarios such as search and rescue, field exploration, and more. Nevertheless, conventional model-based motion control methods suffer from cumbersome modeling processes and poor generalization performance, making them ill-suited for unstructured complex environments. To address these limitations, this paper presents a comprehensive review of deep reinforcement learning-based motion control technologies for quadruped robots. It first organizes the fundamental theories concerning robot kinematics and reinforcement learning, then categorizes and summarizes research advances across three core research branches: gait generation, autonomous navigation, and adaptive gait transition. Furthermore, this paper analyzes prevailing challenges and corresponding countermeasures regarding hardware deployment, sample efficiency, and model generalization capacity. It points out that further integration of multi-algorithms, optimization of sim-to-real transformation and overall strategy design will be the main trends in this field. By identifying current technical bottlenecks and forecasting future development trends, this work offers valuable references for practical technical implementation and subsequent research within this field.
As a revolutionary additive manufacturing technology, 3D printing is widely applied in aerospace, automotive and biomedical fields to produce complex customized parts. Nevertheless, its layer-by-layer forming process is prone to various defects including nozzle blockage, porosity, layer separation and thermal distortion, which greatly degrade product quality and manufacturing stability. Traditional offline inspection methods are inefficient and costly, and cannot achieve on-site real-time process adjustment. Against this backdrop, multi-sensor fusion that integrates acoustic, thermal, visual and vibration signals has turned into a viable monitoring solution. This article systematically investigates intelligent monitoring and data analysis for 3D printing based on multi-modal sensor fusion. It sorts out prevailing sensing modes, explains classic data fusion structures and intelligent detection algorithms, and discusses existing technical hurdles and prospective development directions. Related studies prove that multi-source fusion monitoring achieves higher precision and better stability compared with single-sensor schemes, offering solid theoretical and technical foundations for data-based intelligent optimization and precise control in 3D printing manufacturing.
Autonomous navigation of robots is a key technology for intelligent transport, industrial automation, and service robotics. This paper reviews its core technical framework and typical applications, focusing on five closely connected modules: perception, localisation and mapping, path planning, obstacle avoidance, and decision-making control. It first explains how vision sensors, LiDAR, millimetre-wave radar, IMU, and other sensing devices support environmental perception across different scenarios, and why multi-sensor fusion is necessary to improve robustness. Then, it discusses major localisation and mapping methods, including SLAM-based approaches, as well as path planning and control algorithms such as A*, DWA, TEB, MPC, and reinforcement learning. The paper further compares the application characteristics of autonomous navigation in intelligent transportation, industrial logistics, and medical service robots, showing that different scenarios require different balances between robustness, efficiency, accuracy, safety, and energy consumption. Finally, it points out future trends, including cloud-edge-device collaboration, end-to-end learning, multimodal large models, embodied intelligence, and cooperative navigation, which may further promote intelligent and sustainable robot mobility.
In recent years, with the development of technology, unmanned aerial vehicle (UAV) remote sensing has higher efficiency, flexibility, and resolution than manual monitoring. Combining with deep learning technology has become the mainstream direction for the intelligent identification of rice pests and diseases. This article uses drone image recognition to monitor the research status of rice field diseases and pests, summarizes the research progress of scholars, and summarizes the differences of the YOLO series, Convolutional Neural Network (CNN), U-shape Network (U-Net), and other models in rice disease and pest identification. The research points out the current challenges in datasets, normalization, model generalization, and cross-regional applications. In response to the above issues and challenges, this article proposes future development directions: jointly building standardized open source datasets for multiple regions, multiple time periods, and multiple categories, and uniformly labeling them with specifications; Implement lightweight deployment of models through techniques such as model pruning, quantification, and knowledge distillation; Combining multi-scale feature fusion and weakly supervised learning to enhance early disease and pest identification capabilities; Integrate multiple sources of data such as RGB, multispectral, meteorological, and crop growth period to build an integrated closed-loop system for monitoring, early warning, and precise prevention and control.
3D printed concrete is an important emerging technology in intelligent construction because it connects digital design, automated execution, material control, and process monitoring within a continuous construction workflow. This paper reviews the role of 3D printed concrete in smart buildings and intelligent construction, focusing on its technical principles, workflow, application scenarios, advantages, limitations, and future development directions. The study first explains how digital modelling, slicing, path generation, automated extrusion, monitoring, and post-processing work together to transform design information into machine-executable construction actions. It then discusses typical applications, including printed housing, bridge components, prefabricated production, customised structures, and emergency construction. Compared with traditional concrete construction, 3D printed concrete can reduce formwork use, decrease labour dependence, improve material utilisation, and support complex or personalised structural forms. However, wider engineering applications are still constrained by material rheology, interlayer bonding, reinforcement integration, equipment cost, construction-site uncertainty, and incomplete standards. The paper concludes that 3D printed concrete should be understood not only as a faster construction method, but also as a key pathway toward digital, automated, and controllable intelligent construction.
Water based zinc ion batteries have become a promising alternative to lithium-ion batteries due to their higher safety and environmental friendliness. Although manganese dioxide positive electrode has advantages in voltage, cost, and ecological friendliness, its cycling stability, actual capacity, and conductivity still have shortcomings. In addition, the impact of positive electrode material loading on capacity is also a key challenge in the actual research and development of zinc ion batteries. Research has shown that increasing the positive electrode load to improve surface capacity often leads to a decrease in specific capacity due to ion diffusion delay and electron transport obstruction, particularly under high-rate conditions. This study compared manganese-based cathodes with different loading levels (10.864 mg/cm ²and 15.880 mg/cm ²) and found that low loading electrodes maintained excellent rate performance (such as a capacity of 60 mAh/g at a current density of 1 A/g), while high loading electrodes experienced capacity degradation due to transmission bottlenecks. This result indicates that optimizing electrode design to balance load and kinetic efficiency will strongly promote the development of high-energy density zinc ion batteries.
Additive manufacturing (AM) enables the fabrication of complex components with high design freedom, but its layer-by-layer processing nature often leads to defects such as porosity, cracking, warping, residual stress, and dimensional inaccuracy. To improve process stability and part quality, this paper reviews in-situ monitoring techniques and closed-loop control strategies in AM. The study first summarises major AM processes and explains why real-time observation is necessary during material deposition, melting, and solidification. It then compares three main monitoring modalities: optical monitoring for surface morphology and geometric deviation, thermal monitoring for melt-pool temperature and heat distribution, and acoustic monitoring for process instability and defect-related events. The paper further discusses how machine learning methods can support defect detection, process-state classification, and quality assessment. More importantly, it emphasises that effective closed-loop control requires transforming monitoring signals into interpretable control variables, such as melt-pool temperature, cooling rate, layer height, or acoustic features. Finally, the paper identifies future directions, including multi-modal sensing, control-oriented feature extraction, physics-data hybrid modelling, and real-time edge computing.
In the context of global climate change, achieving a carbon peak and carbon neutrality has become a global consensus. The transition to clean and low-carbon energy is an extremely urgent matter. This article systematically compares the core differences between new energy sources and traditional fossil energy, analyzes the current technological development and application bottlenecks of solar energy, wind energy, and hydropower. It also explored the future innovation directions of new energy technologies as well as the development potential of the integration of transportation and energy, and explained the cost evolution characteristics and mitigation paths during the transition to a new power system. The research results indicate that, due to their renewable and low-emission characteristics, new energy sources are the core force for achieving the “carbon peaking and carbon neutrality” goals. The related technologies and the integration of transportation and energy provide multiple paths for the energy transition. China has obvious advantages in areas such as hydropower and new energy equipment manufacturing, and its transportation infrastructure has huge potential for generating clean energy. This research provides a theoretical basis for the innovation of new energy technologies and the upgrading of industries.
With combination of distributed energy, including power of wind and solar power, modern power systems have increasing challenges in terms of flexibility, stability, and reliability. Traditional centralized power grids are hard to adapt new features brought by renewable energy. Under this background, energy storage has become an important technology for the development of distributed energy system and smart grids. This paper shows technologies concerning energy storage and their applications in distributed energy systems and smart grids. First, varied types of energy storage technologies are introduced and compared based on their main characteristics. The key roles of energy storage in distributed energy systems are then discussed. Furthermore, the applications of energy storage in smart grids are reviewed from the perspectives of smart dispatching systems, demand response, load management, and ancillary services. International and China case studies are introduced to illustrate practice and operational experience. Finally, the paper will analyze the main challenges. Future trends of energy storage in new power systems are discussed. The goals of this review are to provide understanding of the current development status and future potential of energy storage in supporting reliable and sustainable power systems.
Additive Manufacturing is receiving increasing attention in the modern mechanical engineering field due to its capability to fabricate complex geometry, lightweight structures, or customised components. Compared with traditional manufacturing techniques, AM constructs parts layer by layer based on digital models, has more flexibility in design and better utilisation of materials. From the perspective of mechanical engineering applications, this paper gives a review of the core principles, advantages and limitations, as well as technological development of AM. It is found that AM is more suitable for certain mechanical engineering issues, such as structurally complex problems, functional integration, and high-value small-batch production. But the above-mentioned problems would also restrict the wide range of applications in the industry, such as material anisotropy, low production power, high costs and poor surface quality. The results show that AM should be seen, rather than as a universal method, as an important manufactured way, and its future development depends heavily on improving reliability and combining with common manufacturing technologies.
To study the current status of advanced semiconductor lasers, this paper reviews circuits of temperature-tuned semiconductor lasers, compact temperature control circuits, and high-power semiconductor laser circuits. The integration and engineering application of related technologies will directly drive the leapfrog development of frontier fields such as lidar, optical fiber communication and precision medical instruments towards high precision and miniaturization. The temperature-tuning circuit mainly relies on the improved temperature-controlled H bridge to achieve precise control of the operating temperature and enhance tuning linearity. The miniaturisation approach relies on dedicated driver chips, reducing circuit components while balancing size and precise temperature control. The high-power direction is to use a microcontroller to issue commands, store charge in the capacitor, and then use a high-speed analogue switch to achieve high-power pulsed laser. The article summarises the key technologies and typical schemes of current semiconductor laser circuit design, providing a reference for the optimisation and engineering application of semiconductor laser circuits.
Under the background of the global energy structure transformation, photovoltaic power generation and electric vehicle charging have become the core directions in the new energy field. The former has intermittent output and anti-peaking characteristics, while the latter faces problems such as tight charging resources and unregulated charging affecting grid stability. This article conducts research on the collaborative application of the two, elaborating technical principles and system composition for photovoltaic power generation and electric vehicle charging while analyzing integration modes such as off-grid operation and grid-connected priority. Optimizing operational modes under SCADA (Supervisory Control and Data Acquisition) and PMU (Phasor Measurement Unit) data coordination alongside V2G (Vehicle-to-Grid) technology, proposing a photovoltaic power forecasting method integrating FCM (Fuzzy C-Means)-WOA (Whale Optimization Algorithm)-LSSVM (Least Squares Support Vector Machine)-NPKDE (Non-parametric Kernel Density Estimation) and an EVLS (Electric Vehicle Learning Static Model). The above-mentioned technologies can effectively alleviate the fluctuations in photovoltaic output and the imbalance between charging supply and demand, providing technical support to transform energy structure and achieve dual carbon goals.
This paper reviews the combustion characteristics and application potential of hydrogen ammonia mixed fuel in internal combustion engines. With the promotion of the carbon neutrality goal, hydrogen and ammonia have received wide attention as carbon-free energy sources. Among them, ammonia has the advantages of easy storage and transportation, and can be used as a hydrogen carrier, but its high ignition energy and slow combustion speed limit the application in engines. Research shows that hydrogen can effectively improve the ignition performance and combustion stability of ammonia fuel, but at the same time, it will affect NOx emissions. This paper focuses on analyzing the impact of hydrogen doping ratio and equivality ratio on combustion stability and NOx generation, and points out that a balance between combustion performance and emissions can be achieved under the condition of appropriate hydrogen ammonia ratio and equivance ratio, which provides reference for further research and application of hydrogen ammonia fuel engines.
