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.

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 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.

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.

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.

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.

Exploring Sustainable Energy Futures: Assessing the Viability of CanmetENERG’s Hydropower Initiatives in Cameroon

This study examines how hydroelectric power projects, specifically the CanmetENERG initiative in Cameroon, play a crucial part in promoting sustainable development. Hydropower, seen as a cheap, local, and sustainable power source, is given top priority in government funding plans, especially in emerging nations. By making use of the RETScreen Clean Energy Project Analysis Software, the analysis examines the financial feasibility of different choices for CanmetENERG, emphasizing the importance of a holistic strategy that incorporates economic, environmental, and social factors in project development. The importance of factoring in environmental, social, and cultural aspects in project development is highlighted by the substantial influence of tunnel construction on total expenses. The paper suggests harnessing Cameroon’s unused hydroelectric capacity while following sustainable development principles and international standards. It emphasizes the need for a well-rounded strategy that focuses on economic sustainability in addition to environmental and social issues to guarantee Cameroon’s energy market can sustain itself. Moreover, the research emphasizes the continuing need for advancements in hydroelectric power through research and technology, encouraging cooperation between academia, industry, and policymakers. Community involvement and stakeholder engagement are considered essential for inclusive decision-making and socially responsible project development. It is advised to use adaptive management tactics to deal with changing environmental and socio-economic circumstances. The CanmetENERG case study showcases a model for responsible and sustainable hydropower development that boosts the country’s energy security and environmental resilience. Apart from this particular instance, the study provides more extensive perspectives on effective methods for sustainable energy development, emphasizing the difficulty of maintaining a good balance between energy security, economic feasibility, and ecological sustainability. It requires a strategic shift towards self-sufficiency in energy and decreased reliance on imports. The article envisions a collaborative, interdisciplinary approach to shaping the future of hydroelectricity, emphasizing continuous research, technological advancements, and the involvement of key stakeholders. By promoting creativity, eco-friendly practices, and community engagement, hydroelectric projects can act as catalysts for sustainable development, making valuable contributions to society.

Taguchi Full Factorial Design of Experiments Optimisation of Cutting Parameters for Energy Efficiency and Surface Roughness during the Dry Turning of EN19 Material

During metal machining, the satisfactoriness of cost-quality-time matrix convergence effectively depends on the supreme selection of cutting parameters. This study investigated the energy use minimisation and quality surface generation through optimised cutting parameters application, as sustainability enhancement during dry turning of EN19 material. Cutting parameter optimisation is a serious challenge confronting the machining industry as they strive to achieve low energy use and better component quality generation from their operations. The utility material, EN19, is a medium-carbon low alloy steel which typically gets applied in the manufacturing of multiple profiled cylindrical machine tool, rail locomotives and motor vehicle component parts, inter alia. Taguchi Full Factorial experimental plan was used to organise the empirical experiments. ANOVA and the main effects plot signal-to-noise ratio optimisation analysis were utilised in the study to establish the influence of process parameters on the response parameters—surface roughness and energy use. The aim was to investigate and determine the correlation of the machining strategy parameters with the outcome of low energy use and quality surface texture of the components as the cutting parameters were varied, and optimised for minimum surface roughness and energy use. Results of the extensive experimental study, produced optimum cutting speed, rake angle variation and feed rate which respectively influence the response parameters positively for energy use minimisation and improved surface quality. Validation experiments confirmed model findings.

Analysis on energy demand and CO2 emissions in China following the Energy Production and Consumption Revolution Strategy and China Dream target

A quantitative model was applied to analyze the energy demand and CO2 emissions in China following the Energy Production and Consumption Revolution Strategy(2016e2030)and long-term economic and social development target China Dream.Results showed that 1)toward the 2050 China Dream target,total final energy consumption is expected to peak at 3.9 Gtce in 2030 and remain stable until 2050,whereas total primary energy consumption is expected to reach an upper platform by 2040 and around 5.8 Gtce by 2050;2)the proportion of non-fossil fuels is expected to reach approximately 50%and that of natural gas to reach more than 16%by 2050;3)CO2 emissions from energy use are expected to peak at 9.6 Gt by no later than 2030 and then gradually decline to 6.7 Gt by 2050.

Strategies for development of clean energy in China

A development framework of clean energy in China is put forward based on core development strategy, technology support, and policy and laws support. In this framework, the priority development and strategic backup of clean energy are defined, and the technology support and policy and laws support are also presented.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Pediatric obesity prevention:From na?ve examination of energy imbalance towards strategies that influence the competition for nutrient resources among tissues

Current pediatric obesity interventions have collectively yielded relatively unsuccessful results. In this Field of Vision, we present plausible physiologic underpinnings fostering ineffectiveness of conventional strategies grounded in requisite induction of negative energy imbalance. Moreover, such recommendations exacerbate the underlying metabolic dysfunction by further limiting metabolic fuel availability, lowering energy expenditure, and increasing hunger(recapitulating the starvation response amid apparent nutritional adequacy) which precede and promote obesity during growth and development. The qualitative aspects of musculoskeletal system(i.e., endocrine response, muscle functional capacity) are likely to improve metabolic function and increase nutrient delivery and utilization. An intricate and complex system including multiple feedback mechanisms operates to homeostatically regulate energy balance and support optimal body composition trajectories and metabolic health, during growth and development. Thus, ignoring the interdependencies of regulatory growth processes initiates a nuanced understanding of energy regulation and thus misguided attempts at preventive strategies. Importantly, these gains are not dependent upon weight-loss, rather we suggest can be achieved through resistance training. Collectively, optimizing musculoskeletal health via resistance training elicits augmentation of competitive capacity across systems. Further, substantial gains can be achieved in skeletal muscle mass, strength, and functional capacity through resistance training in a relatively short period of time.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

Energy control strategy for parallel hydrostatic transmission hybrid vehicles

Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of configuration for hydrostatic transmission hybrid vehicles(PHHV) and the selection criterion for important components are proposed.Based on the optimization of planet gear transmission ratio and the analysis of optimal energy distribution for the proposed PHHV on a representative urban driving cycle,a fuzzy torque control strategy and a braking energy regeneration strategy are designed and developed to realize the real-time control of energy for the proposed PHHV.Simulation results demonstrate that the energy control strategy effectively improves the fuel economy of PHHV.

China's Global Energy Strategy: from Energy Strength to Energy Power

Since the founding of the People's Republic of China in 1949, China's global energy strategy has experienced three stages: "infancy", "growth" and "maturity". Chinese foreign energy cooperation has also gone through three periods chronologically, which include(1) the period when China got rid of its high dependence on oil imports from the Soviet Union, achieved self-reliance and even became a net exporter;(2) the period when China first focused on "bringing in" and then turned itself into a net importer of crude oil and refined oil; and(3) the period when China "went global" to engage in energy cooperation in diverse areas, gradually shifting into a leading role before launching the Belt and Road Initiative, which marked the full establishment of the Chinese global energy strategy. The objective of the Chinese global energy strategy has gradually changed from merely pursuing "energy strength" to transforming "energy strength" into "energy power"; the mission is mainly to "promote energy cooperation and energy revolution"; to implement this mission, China will provide international public goods to global and regional energy governance through energy diplomacy and services; and the areas covered by the strategy are countries and regions along the "Silk Road Economic Belt".

Fundamental Understanding and Optimization Strategies for Dual‑Ion Batteries:A Review

There has been increasing demand for high-energy density and longcycle life rechargeable batteries to satisfy the ever-growing requirements for nextgeneration energy storage systems.Among all available candidates,dual-ion batteries(DIBs)have drawn tremendous attention in the past few years from both academic and industrial battery communities because of their fascinating advantages of high working voltage,excellent safety,and environmental friendliness.However,the dynamic imbalance between the electrodes and the mismatch of traditional electrolyte systems remain elusive.To fully employ the advantages of DIBs,the overall optimization of anode materials,cathode materials,and compatible electrolyte systems is urgently needed.Here,we review the development history and the reaction mechanisms involved in DIBs.Afterward,the optimization strategies toward DIB materials and electrolytes are highlighted.In addition,their energy-related applications are also provided.Lastly,the research challenges and possible development directions of DIBs are outlined.

Exploitation of Renewable Energy—A Strategic Choice for Sustainable Development of Power Industry

Since China is being faced with the pressure of energy shortage and environmental conservation, the power industry in China has to actively develop the renewable energy for electricity generation while raising the utilization efficiency of conventional energy. In view of such facts, China Guodian Corporation decided on a development strategy of giving priority to green power, such as wind power. Based on the national planning of wind power development, the corporation set out its own target of installing wind power capacity of 1500 MW by the end of 2010, and is adopting appropriate measures including promoting the localization of wind turbines and developing hydropower, thermal power and wind power simultaneously. Moreover, it put some relevant suggestions.