A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Strategies for Introducing Energy Saving Idea into Residential Planning and Design in Urban and Rural Areas of China

Currently,energy saving design has been conducted on single building but not on the whole residential community in urban and rural areas.So,the paper has proposed energy saving measures for residential planning from the perspective of site selection and layout of buildings.Specific measures are as follows.Firstly,buildings should be constructed on the sunny side and leeside;secondly,buildings on the south should be lower than those on the north;the east side of the building should be open while the west side should be closed;thirdly,climate protection unit should be set;fourthly,buildings should be of northsouth direction primarily,and the main room should be set on the east side and the assistant rooms or passage on the west side in the buildings of east-west direction;fifthly,it should select compact and wellarranged households and the units should not be combined in point and dislocation and jointing.

Optimal energy saving in DC railway system withon-board energy storage system by using peak demand cutting strategy

A problem of peak power in DC-electrified railway systems is mainly caused by train power demand during acceleration.If this power is reduced,substation peak power will be significantly decreased.This paper presents a study on optimal energy saving in DC-electrified railway with on-board energy storage system(OBESS) by using peak demand cutting strategy under different trip time controls.The proposed strategy uses OBESS to store recovered braking energy and find an appropriated time to deliver the stored energy back to the power network in such a way that peak power of every substations is reduced.Bangkok Mass Transit System(BTS)-Silom Line in Thailand is used to test and verify the proposed strategy.The results show that substation peak power is reduced by63.49% and net energy consumption is reduced by 15.56%using coasting and deceleration trip time control.

CROSS LAYER COORDINATED ENERGY SAVING STRATEGY IN MANET

Mobile Ad hoc NETwork (MANET) consists of a set of mobile hosts which can operate independently without infrastructure base stations. Energy saving is a critical issue for MANET since most mobile hosts will operate on battery powers. A cross layer coordinated framework for energy saving is proposed in this letter. On-demand power management, physical layer and medium access control layer dialogue based multi-packet reception, mobile agent based topology discovery and topology control based transmit power-aware and battery power-aware dynamic source routing are some of new ideas in this framework.

A review:Multi-hierarchy design strategy of electrocatalysts for energy molecule conversion

Under the new energy resource structure,electrocatalysts are key materials for the development of proton membrane fuel cells,electrolysis of aquatic hydrogen devices,and carbon dioxide reduction equipment,to address energy shortages and even environmental pollution issues.Although controlling the morphology or doping with heteroatoms for catalyst active centers have accelerated the reaction rate,it is difficult to solve the problems of multiple by-products,and poor stability of catalytic sites.From this,it will be seen that single regulation of metal active centers is difficult to comprehensively solve application problems.Orderly assembly and coordination of catalyst multi-hierarchy structures at the mesoscale above the nanometer level probably be more reasonable strategies,and numerous studies in thermal catalysis have supported this viewpoint.This article reviews the multi-hierarchy design of electrocatalyst active centers,high-energy supports,and peripheral structures in recent years,providing unconventional inspiration about electrocatalyst creation,which perhaps serves as a simple tutorial of electrocatalysis exploration for abecedarian.

Optimization of Finishing Process and Energy Savings in Denim Textile Facility

Denim is widely accepted among exported textile products due to its aesthetics, appearance, and fashion. Practitioners employed several physical or chemical treatments to improve denim qualities in denim finishing operations. So, several treatment processes, including enzymatic, bleaching, singeing, heat set, and ozone finish, are used, which made this processing more energy consumption and time-consuming. Therefore, it is significant to investigate how changing the chemicals and raw ingredients could improve the finishing process, which is environmentally and economically beneficial for sustainable production practices in the denim finishing process. This study’s research design comprises an experimental investigation in a denim plant in Bangladesh. Two different fabrics were chosen to analyze, determining the potential savings of finishing on the denim fabrics’ performance characteristics. By deducting singeing and heat-set processes, the researchers ran an experimental process by maintaining the same length of fabric. Then, the impacts of finishing process optimization on the mechanical, thermal, and comfort parameters of drape, stiffness, and tear strength were examined. The study’s findings demonstrated that this experiment increased productivity and reduced the finishing unit’s energy consumption without compromising the denim fabrics’ quality. This study significantly impacts environmental sustainability by preserving limited energy resources and manufacturing denim finishing processes.

Research on Energy-Saving Performance of Intermittent Heating for Rooms in Hot Summer&ColdWinter Zone

In the hot summer&cold winter zone in China,intermittent heating space for rooms is widely used.However,in comparison with continuous space heating,the energy-saving performance of intermittent space heating has not been sufficiently investigated.This paper studied the factors influencing the energy performance of intermittent heating for the representativeoffice inhot summer&coldwinter zone.Basedon theheatbalancemethod,adynamic thermalmodel of the intermittent heating roomwas built and tested by experiments.And then,it analyzed the total space heating load,the amount of energy saving and energy saving ratio of the intermittent heating under different preheating hours,occupation hours,required roomtemperatures,air change rates,overall heat transfer coefficients(U-value)of windows and wall materials.If the adjacent rooms were not heated,for a typical room occupied about 10 h a day,the energy-saving ratio of intermittent heating was about 30%compared with continuous heating.But the preheating power was higher than two times of continuous heating.The results also indicated that the occupation hours had a significant effect on energy saving amount and ratio,it should be noted that the energy saving ratio by intermittent heating was much lower than the unoccupied period ratio.Relative to other factors,the heating temperatures,room air change rates and U-value of windows,and room envelope materials had little effect on energy efficiency.If the adjacent rooms were heated in the same manner as the roomin question,the energy-saving ratio of the total load of intermittent heating was heavily reduced to 8.46%.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

ENERGY-SAVING MATCHING STRATEGY AND EXPERIMENTAL STUDY OF PUMPING SYSTEM FOR TRUCK-MOUNTED CONCRETE PUMP

Aiming at the high fuel consumption and use-cost of truck-mounted concrete pump , an energy-saving matching strategy of pumping system is presented and the experimental study is conducted.Since pumping system occupies most resources of engine , the matching strategy between engine and main pump is analyzed to meet the load demand and reduce the engine rational speed drop.The testing method is established to measure the fuel consumption of engine under various working conditions , and the experimental data are analyzed to find the law of the fuel consumption of engine.The system performance can be improved by adjusting the system input value.Finally , the energy-saving matching strategy is established to reduce the fuel consumption of truck for unit workload , which provides a new approach for the energy-saving of truck-mounted concrete pump.

Research on the cooperative train control method in the metro system for energy saving

Purpose–To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure,energy-saving performance of the whole metro system cannot be guaranteed.Design/methodology/approach–A cooperative train control framework is formulated to regulate a novel train operation mode.The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train.An improved brute force(BF)algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.Findings–Case studies on the actual metro line in Guangzhou,China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters.The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.Originality/value–Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process,which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation.This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea,where energy-efficient train operation can be realised once train running time is determined,thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

An Energy-Saving Control Strategy on Central Air-Conditioning System

An energy-saving control strategy based on predictive control for central air-conditioning systems is proposed in this paper. The cold load model is developed to describe the dynamic characteristics of temperature control systems, and then parameters in the cold load model and in the central air-conditioning system model are estimated. Generalized predictive control (GPC) is used to establish an optimization model to minimize the consumption of energy and the control error of temperature. The simulated annealing (SA) algorithm, combined with quadratic programming, is adopted to solve the optimal problem. Contrasted with the simulation of traditional PID control, the results prove the effectiveness of this proposed strategy.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Enhanced energy density and fast-charging ability via directional particle configuration

The limited energy density of lithium-ion capacitors poses a significant obstacle to their widespread application,primarily stemming from the inability of the electrodes to simultaneously fulfill both high energy density and rapid charging requirements.Experimental data demonstrate that a directional particle configuration can enhance charging speed while maintaining high-capacity density,but it is rarely discussed.Here,we have developed a particle-level electrochemical model capable of reconstructing an electrode with a directional particle configuration.By employing this method,an investigation was conducted to explore how the spatial morphology characteristics of particle configuration impact the energy storage characteristics of electrodes.Results demonstrate that rational particle configuration can effectively enhance the transport of lithium ions and create additional space for lithium-ion storage.With the same particle size distribution,the best electrode can increase the discharge capacity by up to132.4% and increase the charging SOC by 11.3% compared to the ordinary electrode under the condition of 6 C.These findings provide a further understanding of the energy storage mechanism inside the anisotropic particle distribution electrode,which is important for developing high-performance lithium-ion capacitors.

Application of Variable Strategies in the Low-cost and Energy-saving Rural Residences in Transitional Areas

Changeful and complex rural family structure and climatic features of transitional areas in China make the application of variable strategy in energy-saving rural residence designs possible.Aiming at the low cost,several effective and reasonable variable strategies were proposed for the design of interior spaces,main bedroom,sunshine room,staircase,west wall,door and window design to satisfy changing structure of a family during different periods and their different thermo-technical requirements in winter and summer.In this way,thermal comfort of rural indoor spaces will be improved,more energy saved,useful experience and thoughts provided for the energy-saving residence design in cold regions and regions hot in summer and cold in winter.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Enhancing Autonomy Capability in Regional Power Grids:A Strategic Planning Approach with Multiple Autonomous Evaluation Indexes

After the integration of large-scale DistributedGeneration(DG)into the distribution network,the randomness and volatility of its output result in a reduction of spatiotemporal alignment between power generation and demand in the distribution network,exacerbating the phenomenon of wind and solar power wastage.As a novel power system model,the fundamental concept of Regional Autonomous Power Grids(RAPGs)is to achieve localized management and energy autonomy,thereby facilitating the effective consumption of DGs.Therefore,this paper proposes a distributed resource planning strategy that enhances the autonomy capabilities of regional power grids by considering multiple evaluation indexes for autonomy.First,a regional Energy Storage(ES)configuration strategy is proposed.This strategy can select a suitable reference value for the upper limit of ES configuration based on the regional load andDGoutput to maximize the elimination of source load deviations in the region as the upper limit constraint of ES capacity.Then,a control strategy for regional ES is proposed,the charging and discharging reference line of ES is set,and multiple autonomy and economic indexes are used as objective functions to select different proportions of ES to control the distributed resources of the regional power grid and establish evaluation indexes of the internal regional generation and load power ratio,the proportion of power supply matching hours,new energy consumption rate and tie line power imbalance outside the region to evaluate changes in the regional autonomy capabilities.The final simulation results showthat in the real regional grid example,the planning method in the planning year in the region of the overall power supply matching hour ratio and new energy consumption rate increased by 3.9%and 4.8%on average,and the power imbalance of the tie line decreased by 7.8%on average.The proposed planning approach enables the maximization of regional autonomy while effectively smoothing the fluctuation of power exchange between the regional grid and the higher-level grid.This presents a rational and effective planning solution for the regional grid,facilitating the coordinated development between the region and the distribution network.

Research of Supercapacitor Voltage Equalization Strategy on Rubber-Tyred Gantry Crane Energy Saving System

A model for supercapacitor voltage equalization strategy is analyzed, and on this basis a supercapacitor voltage equalization method for Rubber Tyred Gantry Crane (RTG) energy saving system is proposed, namely active voltage equalization method based on Buck-Boost converter. The equalizing speed of the proposed method is fast. Firstly, the working principle and process of the voltage equalization circuit is analyzed in detail. In addition, design of active voltage equalization circuit parameters and control strategy are given. Finally, simulation analysis of the series connection of supercapacitors module is performed. Results show that this method for equalizing voltage can avoid over-voltage of each cell and possess practicable and high value for supercapacitor RTG energy saving system.

Pooled Sputum for Xpert MTB/RIF Testing: A Cost-Saving Strategy in Mwanza, Tanzania

Background: Tuberculosis (TB) diagnostics, such as Xpert MTB/RIF, is still limited by cost. Testing of pooled samples from presumptive TB patients has been thought as a cost-saving strategy to diagnose TB. We assessed the utility and cost-saving of pooled Xpert MTB/RIF testing strategy for the diagnosis of TB in Mwanza, Tanzania. Methods: Sputum samples from Presumptive TB patients were submitted to TB laboratory for routine diagnosis of TB using Xpert MTB/RIF. The TB results from the individual sputum samples were used as the reference standard and were concealed to the investigating laboratory technicians. The remainder of samples were collected serially and were pooled (5 samples per pool) for testing. The agreement of the results between individual sample testing against pooled sample testing and cost-savings was assessed. Results: A total of 250 sputum samples from presumptive TB patients were analyzed and 50 pools were made with each pool containing 5 samples. Of the 50 sputum pools made, Mycobacterium tuberculosis (MTB) was detected in 17 (34.0%) pools. Results from the individual sputum samples MTB/RIF testing were retrieved for all 250 samples and there were 28 (11.2%) samples in which MTB was detected whereas 222 (88.8%) samples had no MTB detected. Following re-analysis of positive pools, all 28 (100%) individual positive MTB samples were detected within the 17 positive pools, with 1 to 3 individual MTB positive samples per pool. The individual sputum samples were correctly identified by pooled sputum on Xpert MTB/RIF testing, with the sensitivity of 100% and specificity of 100%. Using pooling of sputum samples strategy, we saved 46.0% (115/250) of the cartridges. Conclusion: The pooled sputum testing strategy reduced cartridge costs by 46.0% and has the potential to increase the affordability of Xpert MTB/RIF testing in countries with limited resources, such as Tanzania.