Development Of New Energy Technologies Research Articles

Scenario-Based Analysis of High-End New Energy Vehicle User Demands

With the rise in global environmental awareness and the rapid development of new energy technologies, high-end new energy vehicles (NEV) have become an important direction for the automotive industry. Consumer demand is evolving from merely seeking a means of transportation to desiring a comprehensive experience across various scenarios, emphasizing high quality, high performance, and environmental friendliness. Understanding the characteristics and trends of high-end NEV users' demands is crucial for car manufacturers in formulating market strategies, product design, and service improvement. However, current focus is often on policies and product design, lacking analysis of actual usage scenarios. To address this gap, this article employs scenario research methods to delve into the needs of high-end NEV users, selecting typical scenarios such as urban commuting, long-distance travel, and business operations. The research indicates that high-end NEV users are primarily young, highly educated, middle-to-high income individuals who exhibit rational consumption habits. Their most common usage scenarios include commuting to work, transporting family members, and shopping. In these contexts, there is a pronounced demand for vehicle safety performance, ride comfort, and intelligent features, each showing differentiated characteristics across various user groups. For instance, female users demonstrate a particularly strong preference for comfort-related configurations, whereas married users with children have the highest demand for spacious and convenient features. Based on these insights, this paper offers targeted recommendations from the perspectives of product design and marketing.

A study on power supply restoration strategy considering flexible resource regulation ability

AbstractThe rapid development of new energy technology can make this type of energy the primary power source in the future. However, new energy power generation is considerably affected by the environment, resulting in intermittent and fluctuating characteristics. Given the shortage of power generation in urban distribution networks during extreme weather, a study on power supply restoration strategy considering flexible resource regulation ability is proposed in this study. First, the typical adjustable flexible resources, air conditioners, and electric vehicles in urban distribution networks are modeled, and their adjustment ability is evaluated. Second, on the basis of the evaluation results, the optimal power supply recovery path is solved to ensure the power supply of essential loads in the distribution network. Last, the IEEE 33‐bus system proves that this method can quickly find the optimal path of power supply recovery and effectively reduce the power loss rate of essential loads, thus improving the toughness of urban distribution networks.

Research on Evaluation Indicators of AgriVoltaics

In recent years, with the popularization of environmental protection concepts and the continuous development of new energy technologies, agrivoltaics has attracted increasing attention and become an important direction of new agricultural development as a new type of agricultural planting method and new energy utilization method. This article classifies ground-based agrivoltaic schemes into three categories according to different management and distributions of solar radiation: intensity management, spectrum management, and time management. The article also details the performance of high-quality schemes proposed by our research team for these three categories. In addition, the article proposes four evaluation indicators for agrivoltaic schemes: land equivalent ratio (LER), return on investment (ROI), water-saving performance, and crop-friendliness, and calculates the performance of different schemes proposed by our research team based on these indicators. Finally, we recommend using these four dimensions to evaluate the performance of agrivoltaic schemes.

Technical transformation of heavy/ultra-heavy oil production in China driven by low carbon goals: A review

With the growing global energy demand and limited production of conventional crude oil, the extraction of unconventional heavy and extra-heavy oil is receiving unprecedented attention. China is the fourth largest producer of heavy oil. High viscosity and complex formation environments increase the difficulty of exploiting heavy oil. To alleviate the economic cost and environmental pollution pressure brought by heavy oil production, China has achieved significant advancement in the innovation of heavy oil recovery, including accelerating the transformation of thermal technology to non-thermal technology and the use of new energy technology to achieve the goal of low-carbon economy and green environmental protection. Based on the background of heavy oil in China, this paper recharacterizes the high-viscosity mechanism of heavy oil. From different aspects, including basic principles, main characteristics, applicability, limitations, and challenges, this paper comprehensively reviews existing heavy oil recovery technologies, including thermal recovery, in-situ upgrading, cold recovery, and new energy technologies.Currently, in China, the main target of heavy oil recovery is achieving the “carbon peaking and carbon neutrality” goals, which can be described as low energy consumption, low emission, and low pollution. To facilitate the low-carbon transition in the extraction of heavy/extra-heavy oil and attain environmental sustainability goals, it is essential to increase the proportion of non-thermal factors in thermal recovery technologies, develop efficient in-situ catalysts, adopt clean cold extraction techniques, and advance the development of new energy technologies.

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

Research and Application of New energy Digital System Based on BIM

With the continuous development of new energy technology, the installed scale of new energy is getting bigger and bigger, the market competition is becoming increasingly fierce, and the cost control of investment units, reference units and manufacturers in all aspects of new energy construction is becoming more and more strict. Promoting the application of BIM in new energy engineering projects can effectively reduce the cost in the design and construction stages, and more importantly, improve the efficiency of long-term operation of new energy power stations, and reduce capital investment. This paper focuses on the definition and development of BIM, key technologies and BIM-based new energy digital system, which manages the information model of new energy power generation projects, provides more effective and accurate management, and improves project operation efficiency.

Working cycle condition construction for electric wheel loader based on principal component analysis and cluster analysis

With the rapid development of new energy technology, cycle condition is significant for the design of powertrain system, whole machine economy, endurance, and other performance parameters of pure electric drive machines. Many cycle conditions of the passenger car industry have been defined. While that for the loader which has complex operation modes and working conditions, has less research on this aspect. Cycle conditions suitable for loader have not been constructed. To support the research and development of electric loader power system, the cycle conditions that can reflect the driving characteristics of electric loader are studied in this paper. According to the typical “V” cycle condition of loader, the actual driving condition data are collected and the kinematic segments are divided. The kinematic segments are grouped into five categories by using principal component analysis and K-means clustering algorithm to obtain the clustering center of each category. The working cycle condition is formed by extracting the combination of segments closest to the clustering center. Finally, based on AMESim simulation and whole machine test analysis, the error between the simulation and vehicle test results of the energy consumption is 3.4%. The accuracy and effectiveness of the proposed constructed cycle conditions are verified.

A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling.

Perfect Solar Absorber Based on Four-Step Stacked Metamaterial

Solar absorbers are of great significance in the development of new energy technologies. However, the current approaches are mostly complex and fail to achieve high absorption rates across a wide range of wavelengths. Here, we propose a four-step stacked metamaterial solar absorber that achieves near-perfect absorption. Our four-step stacked absorber (FSSA) boasts an average absorption rate of 96.32% from 499 nm to 2348.3 nm, and a high average absorption rate of 94.96% from 300 nm to 2500 nm. Electromagnetic mode analysis and the impedance matching theory were employed to analyze the designed FSSA, which revealed that the high absorption rates are due to the propagating surface plasmon resonance (PSPR) and localized surface plasmon resonance (LSPR) modes. The FSSA offers broadband, high absorption rates, and high spectrum selectivity. Additionally, the structural parameters are adjusted to optimize the proposed perfect solar absorber. This proposed absorber can have promising applications in the renewable energy industry.

Analysis of Electromagnetic Interference for Anti-Medal UHF RFID Temperature Tag in High Power Electronic Equipment

Being inches from the rapid development of new energy technology, the capacity of high-power power electronic equipment is increasing rapidly, and the requirements for its safe and reliable operation are also rising. As a result, the demand for online temperature monitoring of such equipment is becoming increasingly urgent. RFID temperature measurement technology can be used for real-time monitoring of the temperature of powered operation equipment. However, the operation of high-power electronic equipment generates strong electromagnetic interference, which can seriously affect the normal operation of RFID temperature measurement systems. For applications involving the internal temperature measurement of high-power power electronic equipment, this paper employs an RFID anti-metal temperature tag antenna with a short-circuit cutoff structure. This structure was tested in an excitation switchgear cabinet. During the low-power operation of the cabinet, the temperature tag functioned normally. By combining an RFID antenna model with an electromagnetic interference simulation model of the main circuit of the excitation switchgear cabinet, this paper establishes an electromagnetic interference simulation model for an RFID temperature tag. It analyzes how the tag’s antenna performance parameters change when subjected to interference. Through simulation, the failure mechanism of the RFID temperature tag during the high-power operation of the excitation switchgear cabinet is clarified. The analysis found that there are both conductive electromagnetic interference and radiated electromagnetic interference in the excitation switchgear cabinet, with the conductive electromagnetic interference having a more significant effect. Conductive electromagnetic interference can seriously impact the performance of RFID temperature tags in the excitation switchgear cabinet, significantly degrading their performance. In contrast, the effect of radiated electromagnetic interference on the tags is relatively small. Therefore, this paper employs anti-metal RFID temperature tags and simulates and analyzes their electromagnetic interference characteristics.

Investigation on the heat transfer performance of microchannel with combined ultrasonic and passive structure

With efficient heat dissipation capacity, the microchannel heat sink (MCHS) can be exploited and applied in the development of new energy technologies. With 2.8 MHz high-frequency ultrasonic, the flow and heat transfer performance in different kinds of microchannel was studied. These microchannels included rectangular straight microchannel, 90° fan-shaped and triangular combined cavity microchannel and 90° fan-shaped and triangular combined cavity circular fin microchannel. Furthermore, the comprehensive performance of the microchannel was analyzed and evaluated in detail from different aspects such as flow characteristics, heat transfer characteristics, field synergy and efficiency analysis. At the low Reynolds number, the acoustic streaming effect induced by ultrasonic could destroy the wall boundary layer and improve the heat transfer between the fluid and the wall. However, with the increase of Reynolds number, the flow velocity gradually dominated the heat transfer process. Meanwhile, the ultrasonic mainly acted on improving the synergy between the flow and temperature fields. Moreover, the combination of cavity structure and ultrasonic was conducive to increasing the action depth of acoustic wave in the fluid, which was much easier to induce the acoustic streaming effect. Thus, the effect of ultrasonic enhancement could be greatly strengthened. More importantly, the efficiency of using ultrasonic to enhance heat transfer was higher than that of pump power. This work can contribute to the mechanism and improve the efficiency of active and passive enhancement of microchannel heat transfer by using high-frequency ultrasonic.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health (SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation (Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity (IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

A Novel DC Transmission and Distribution System with 100% New Energy Consumption

With the continuous development of new energy technology and the continuous improvement of people’s green and low-carbon awareness, distributed photovoltaic, wind power, and other new energy are more and more widely used. On the other hand, the development of new industries, such as electric vehicle charging stations and data centers, has increased the DC load significantly, showing the characteristics of the coexistence of AC and DC user loads. Therefore, it is of great practical significance to study how to effectively utilize new energy and realize the direct power supply of AC and DC loads to achieve the dual carbon goal. Combined with the development trend of new energy and DC transmission, this paper proposes a ±10 kV DC transmission and distribution architecture with 100% new energy consumption, which can realize the complete local consumption of new energy and reliable power supply for diversified AC and DC loads, maximizing the reduction of power loss caused by AC-DC conversion. This paper discusses the key devices and key technologies in the above architecture and analyzes the advantages and disadvantages and application scope of various methods in the current research results.

Optimization of Synchronized Scheduling for Dual-Source Trolleybus Network

With the development of new energy technologies, dual-source trolleybuses are widely used in public transportation systems. A synergistic schedule can further improve the service quality of vehicles and optimize the passenger travel experience. This study investigated the synchronized scheduling of dual-source trolleybus networks to reduce the transfer time of passengers and the fleet size problem to reduce the operating cost of enterprises. A bi-objective mixed-integer linear programming model was developed to maximize the total synchronizations and minimize the total fleet size of dual-source trolleybus lines. A two-stage algorithm was designed to obtain multiple sets of Pareto effective solutions. Meanwhile, to demonstrate the effectiveness of the proposed method, the results of numerical examples solved by the two-stage algorithm were compared with those of a genetic algorithm (GA) and a nondominated sorting genetic algorithm (NSGA-II). A real-world case study based on the Beijing dual-source trolleybus network was studied to validate the proposed model. The results show that the model can obtain synchronized schedules. The optimization of the number of synchronizations and fleet size is obvious. The model and algorithm can be applied to a large dual-source trolleybus network.

Research on data mining model of fault operation and maintenance based on electric vehicle charging behavior

In recent years, with the development of new energy technology and the country’s strong support for electric vehicles, there is a lack of effective electric vehicle charging fault analysis and diagnosis methods at this stage. A comprehensive analysis of the working principle of the charging process of electric vehicles, based on the clarification of the failure mechanism of the power battery and charging equipment, analyzes the fault-related factors affecting the power battery and charging equipment from multiple angles, and summarizes the relationship between the power battery and charging equipment. The feature parameters related to equipment failure are discretized by the k-means clustering algorithm. Using the optimized FP-Growth algorithm based on weights, the association rules between the power battery and charging equipment failures and the characteristic parameters of the failure factors are mined, and the correlation of the failures is analyzed based on the association rules, and the correlation between the failure factors and the failures is obtained relevant level.

Design of Isolated Voltage Sampling Circuit Based on HCNR201

With the continuous development of new energy technology, the photovoltaic system plays an extremely important role in people’s production and life, and new energy. As an indispensable part of a photovoltaic system, the battery voltage monitoring circuit plays a very important role. The voltage of the photovoltaic battery pack is usually relatively high. To ensure the safety of users and ensure that the controller is not affected by other adverse factors, this paper designs a voltage sampling circuit based on a high-precision linear optocoupler HCNR201. The detection circuit has strong electrical isolation characteristics and can monitor the voltage of two or more battery groups. In this paper, two groups of battery voltage monitoring using simulation and hardware test results proved that the voltage detection method has good linearity, and high accuracy, while the input and output of electrical isolation ensure user safety.

Research on Optimal Scheduling of Power System Based on Ant Colony Algorithm

With the development of new energy technology, the installation of new energy is becoming more and more. After a large number of new energy sources are connected to the power system, the capacity of peak regulation and frequency modulation is gradually insufficient, resulting in poor stability of the power system. Aiming at the problem of poor operation stability after new energy is connected to power system, a power system optimization scheduling method based on ant colony algorithm is proposed. In the model, the objective function is to minimize the total operating cost. The improved ant colony algorithm is used to solve the optimal scheduling model, and the effectiveness of the method is verified by simulation. The results show that this method has better ability of peak regulation and frequency modulation after new energy is added to the power system network, the frequency value and active power value of the power system are reduced, the load fluctuation value of the grid is reduced obviously, and the operation of the power system is more stable.

The Analysis of China's New Energy Vehicle Policies

The electric vehicle industry has developed rapidly in the past ten years, under the support from the Chinese government policy, not only development of new energy technologies, the preferences of consumers and producers for the market are also increasing. The evaluates, article though different types of policies macro policies, subsidy policies, demonstration policies, tax policies, and facilitation policies, and made suggestion to the areas where the government could still improve in the future.

Design and implementation of real-time simulation module for non-isolated bidirectional half-bridge DC–DC converter

With the rapid development of new energy technology, the number of grid-connected systems and switching control frequency are also increasing. It makes the operation state of the power grid more complicated. Therefore, a small step real-time model is urgently needed to monitor the operation of power grid. Based on the existing computing hardware, FPGA has the characteristics of high concurrency and multi-pipeline computing, which ensures the real-time performance of the simulator. Because non-isolated bidirectional half-bridge DC–DC converter is widely used in power grid system, the real-time simulation design based on FPGA is carried out in this paper. This paper proposes an improved Associated Discrete Circuit (ADC) method and derives the formula of the optimal conductivity parameters. By using the transient initialization method, the accuracy of the ADC modeling method is promoted. The simulation model of the non-isolated bidirectional half-bridge converter is built on FPGA with a time-step of 1μs. Under the premise of real-time performance, the relative error of the proposed simulation module is controlled at about 3%.

The Key Technology of Smart Energy System and Its Disciplinary Teaching Reform Measures

Based on the rapid development of new energy technology, modern information technology, intelligent management technology and different countries’ strategic demand and deployment in the field of energy, the construction of intelligent energy systems is booming with the combination of new energy technology and Internet technology. The Energy Internet is the representative product of intelligent energy systems at the present stage. Its advantages are the effective promotion of energy saving, consumption reduction and optimization of deployment, thus improving the energy system. However, the large-scale construction of the Energy Internet requires a large number of professionals. In order to meet the needs of Energy Internet construction, the talent training mode of higher education is facing new challenges. To cultivate talents in Energy Internet construction, an effective measure is to reform the teaching system based on the current electrical engineering major in universities. This paper investigates the development and construction of the Energy Internet and the current situation of the electrical engineering discipline and puts forward teaching reform measures to transform the traditional electrical engineering discipline into an Energy Internet engineering discipline, considering course structure design, examination form, teacher allocation and teaching mode. This is important for promoting the large-scale construction of the Energy Internet and improving the competitiveness of graduates in the electrical engineering field.