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).

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Power-balancing Instantaneous Optimization Energy Management for a Novel Series-parallel Hybrid Electric Bus

Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization（PBIO） energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus（SPHEB） dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.

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.

Energy-efficient methods for production methane from natural gas hydrates

Gas hydrates now are expected to be one of the most important future unconventional energy resources. In this paper, researches on gas hydrate exploitation in laboratory and field were reviewed and discussed from the aspects of energy efficiency. Different exploiting methods and different types of hydrate reservoir were selected to study their effects on energy efficiencies. Both laboratory studies and field tests have shown that the improved technologies can help to increase efficiency for gas hydrate exploitation. And it also showed the trend that gas hydrate exploitation started to change from permafrost to marine. Energy efficiency ratio （EER） and energy return on energy invested （EROI） were introduced as an indicator of efficiency for natural gas hydrate exploitation. An energy-efficient hydrate production process, called ＂Hydrate Chain Energy System （HCES）＂, including treatment of flue gas, replacement of CH4 with CO2, separation of CO2 from CH4, and storage and transportation of CH4 in hydrate form, was proposed for future natural gas hydrate exploitation. In the meanwhile, some problems, such as mechanism of C02 replacement, mechanism of CO2 separation, CH4 storage and transportation are also needed to be solved for increasing the energy efficiency of gas hydrate exploitation.

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.

World Energy Trends, Security and China’s Options

With the heavy dependence of world＇s growing economy on energy supply, the energy issue again comes into the spotlight throughout the world. How to ensure energy security is a great concern to most countries in the world. In this paper, trends in energy supply, energy security mechanism and new challenges are analyzed. As well as, China＇s security strategies are also proposed.

Major biological events and fossil energy formation: On the development of energy science under the earth system framework

In geological history, one major life explosion and five mass extinction events occurred. These major biological and environmental events affected the evolution of the Earth ecosystem and controlled the formation of organic-rich strata. The life explosion occurred in Cambrian and the five mass extinction events happened at the end of Ordovician, Late Devonian, end of Permian, end of Triassic, and end of Cretaceous, respectively. They are corresponded to the formation of multiple suites of organic-rich strata globally, which are crucial to the formation, evolution and distribution of the fossil energy on Earth. China’s energy is characterized by "unlimited new energy, rich in coal but insufficient in oil and gas ", and it is determined to rely on new energy sources to achieve " energy independence " and carbon neutrality. From the perspective of the Earth system evolution, we investigate the multiple relationships between energy and Earth, energy and environment, as well as energy and human beings, and carry out comprehensive research on energy. Energy science refers to the science of studying the various energy sources formation and distribution, evaluation and selection, production and utilization, orderly replacement, development prospects, etc. in temporal and spatial scales based on the evolution of the Earth system. The connotation of energy science includes three core contents:(1) The relationship between the Earth and energy, including the formation of energy in the Earth system and the feedback of energy consumption to the Earth’s climate and environment;(2) The relationship between the Earth environment and the human beings, including the Earth environment breeding human beings and human activities transforming the earth environment;(3) The relationship between the energy and the human beings, including the development of energy technology by human beings and the progress of human society driven by energy utilization. The energy science focuses on the formation and development of fossil energy, development and orderly replacement of new energy, exploration and utilization of energy in deep earth and deep space, and energy development strategy and planning. The proposal of energy science is of great significance for improving the discipline system, promoting energy development, clarifying the development direction of energy transition, driving the carbon neutral geology research and constructing a habitable Earth.

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.

Research on System Control and Energy Management Strategy of Flux-Modulated Compound-Structure Permanent Magnet Synchronous Machine

The flux-modulated compound-structure permanent magnet synchronous machine (CS-PMSM), composed of a brushless double rotor machine (DRM) and a conventional permanent magnet synchronous machine (PMSM), is a power split device for plug-in hybrid electric vehicles. In this paper, its operating principle and mathematical model are introduced. A modified current controller with decoupled state feedback is proposed and verified. The system control strategy is simulated in Matlab, and the feasibility of the control system is proven. To improve fuel economy, an energy management strategy based on fuzzy logic controller is proposed and evaluated by the Urban Dynamometer Driving Schedule (UDDS) drive cycle. The results show that the total energy consumption is similar to that of Prius 2012.

A new method of encryption wireless energy transmission for EV in the smart grid

Wireless charging has played a crucial role in electric vehicle charging market presently.As electric vehicles will be important nodes access to the smart grid in the future,the security flow of energy and information between wireless charging infrastructure and the smart grid will directly affect the security of the smart grid.A novel secure wireless transfer method for energy and information transfer simultaneously has been represented in this paper by designing a reasonable dual-band coil for simultaneous transmission of energy and information,using improved chaotic modulation and a three times handshake protocol for encrypting energy and information between wireless charging infrastructure and the smart grid.Both the simulation and experiments show that the security of energy transmission can be effectively improved by this structure,in the premise of ensuring the power and efficiency of wireless energy transmission.

Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

This paper proposes an algorithm for scheduling Virtual Machines(VM)with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource utilizationfor VM deployment in cloud data centers is modeled by a combination of“VirtualMachine Scheduling using Bayes Theorem”algorithm(VMSBT)and Virtual Machine Migration(VMMIG)algorithm.It is shown that the overall data center’sconsumption of energy is minimized with a combination of VMSBT algorithmand Virtual Machine Migration(VMMIG)algorithm.Virtual machine migrationbetween the active physical servers in the data center is carried out at periodicalintervals as and when a physical server is identified to be under-utilized.In VMscheduling,the optimal data centers are clustered using Bayes Theorem and VMsare scheduled to appropriate data center using the selection policy that identifiesthe cluster with lesser energy consumption.Clustering using Bayes rule minimizesthe number of server choices for the selection policy.Application of Bayestheorem in clustering has enabled the proposed VMSBT algorithm to schedule thevirtual machines on to the physical server with minimal execution time.The proposedalgorithm is compared with other energy aware VM allocations algorithmsviz.“Ant-Colony”optimization-based(ACO)allocation scheme and“min-min”scheduling algorithm.The experimental simulation results prove that the proposedcombination of‘VMSBT’and‘VMMIG’algorithm outperforms othertwo strategies and is highly effective in scheduling VMs with reduced energy consumptionby utilizing the existing resources productively and by minimizing thenumber of active servers at any given point of time.

Energy management strategy based on dynamic programming with durability extension for fuel cell hybrid tramway

This paper proposes an energy management strategy for a fuel cell(FC)hybrid power system based on dynamic programming and state machine strategy,which takes into account the durability of the FC and the hydrogen consumption of the system.The strategy first uses the principle of dynamic programming to solve the optimal power distribution between the FC and supercapacitor(SC),and then uses the optimization results of dynamic programming to update the threshold values in each state of the finite state machine to realize real-time management of the output power of the FC and SC.An FC/SC hybrid tramway simulation platform is established based on RTLAB real-time simulator.The compared results verify that the proposed EMS can improve the durability of the FC,increase its working time in the high-efficiency range,effectively reduce the hydrogen consumption,and keep the state of charge in an ideal range.

A Centralized Energy Coordinator Reliant on BESS and DER-PV Operation

Many existing battery energy storage system (BESS) control schemes focus on mitigating negative impacts resulting from the operation of distributed energy resources-photovoltaic facilities (DER-PV). These include out-of-firm conditions from reverse power flow or extreme variability in the service voltage. Existing control strategies fail to consider how BESS control schemes need to operate in a consecutive day-to-day basis in order for them to be implemented in the field. In this paper, a novel energy management algorithm capable of dispatching a BESS unit upstream of a multi-megawatt DER-PV is introduced. This algorithm referenced as the Master Energy Coordinator (MEC), accepts forecasted DER-PV generation and individual feeder load to create daily charge and discharge rate schedules. Logic is integrated to the cyclic discharging event to sync with the forecasted peak load, even when it will occur during the morning of the next day. To verify the MEC operation, Quasi-Static Time Series (QSTS) simulations are conducted on a 12.47 kV distribution feeder model utilizing historical head-of-feeder and DER-PV analog DSCADA measurements.

ON STRATEGY FOR ENERGY DEVELOPMENT IN CHINA

In 1949, the Chinese energy industry had an annual output of 20 million tons of standard coal equivalent, which was relatively small. The energy industry has developed at a rapid rate since the 1950s. Some statistics show that the annual production of coal in 1990 increased 34 times and the annual production of electricity in 1990 increased 140 times compared to 1949. The annual output of energy reached one billion standard coal equivalent in 1990 and 1.24 billion in 1995. Although China has become a big country for energy production, the energy supply does not yet meet the needs of its economic development. This situation first

Progress and Development Strategy of Biomass Energy Utilization Technologies in China

I. Preface Biomass includes the residues of agriculture, forest and stock breeding, as well as straw, algae and energy crops. In its broad meaning, biomass is a kind of organic matter produced by the photosynthesis of plants, which is not only renewable, but also contains plentiful energy.

A novel predictive braking energy recovery strategy for electric vehicles considering motor thermal protection

Braking energy recovery(BER)aims to recover the vehicle's kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle(EV).To achieve this goal,the motor/generator mode requires frequent switching and prolonged operation during driving.In this case,the motor temperature will unavoidably rise,potentially triggering motor thermal protection(MTP).Activating MTP increases the risk of motor component failure,and the EV typically disables the BER function.Thus,maximizing BER while reducing the risk of motor overheating is a challenging problem.To address this issue,this article proposes a predictive BER strategy with MTP using the non-smooth Pontryagin Minimum Principle(NSPMP)for EVs.Firstly,a Markov long short-term memory(MLSTM)model is designed to obtain future velocity information.Secondly,the BER problem with MTP in the studied EV is embedded in a model predictive control(MPC)framework.Then,under the MPC framework,the NSPMP strategy is proposed to resolve the problem of MTP.Finally,the performance of the proposed strategy is verified through simulation and a hardware-in-loop test.The results show that in two real-world driving cycles,compared to the rule-based strategy,the proposed strategy reduced power consumption by 1.24%and0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.

Cost-Based Research on Energy Management Strategy of Electric Vehicles Using Hybird Energy Storage System

This paper uses the minimization and weighted sum of battery capacity loss and energy consumption under driving cycles as objective functions to improve the economy of Electric Vehicles(EVs)with an hybrid energy storage system composed of power batteries and ultracapacitors.Furthermore,Dynamic Programming(DP)is employed to determine the objective function values under different weight coefficients,the comprehensive cost consisting of battery aging and power consumption costs,and the relationship between the hybrid power distribution.We also evaluate the real-time fuzzy Energy Management Strategy(EMS),fuzzy control strategies,and a strategy based on DP using the World Light vehicle Test Procedure(WLTP)driving cycle and a synthesis driving cycle derived from New European Driving Cycle(NEDC),WLTP,and Urban Dynamometer Driving Schedule(UDDS)as examples.Then,the proposed strategy is compared with the fuzzy control strategy and the strategy based on DP.Compared with fuzzy energy management strategy(namely FZY-EMS),the proposed EMS reduces the battery capacity loss and system energy consumption.The results demonstrate the effectiveness of the proposed EMS in improving EV economy.

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.