Energy Control Strategy Research Articles

MACHINE LEARNING BASED IDENTIFICATION OF ENERGY EFFICIENCY MEASURES FOR MACHINE TOOLS USING LOAD PROFILES AND MACHINE SPECIFIC META DATA

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

MACHINE LEARNING BASED IDENTIFICATION OF ENERGY EFFICIENCY MEASURES FOR MACHINE TOOLS USING LOAD PROFILES AND MACHINE SPECIFIC META DATA

Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.

An energy-economic analysis of real-world hybrid building energy systems

A coordinated operation of decentralised micro-scale hybrid energy systems within a locally managed network such as a district or neighbourhood will play a significant role in the sector-coupled energy grid of the future. A quantitative analysis of the effects of the primary energy factors, energy conversion efficiencies, load profiles, and control strategies on their energy-economic balance can aid in identifying important trends concerning their deployment within such a network. In this contribution, an analysis of the operational data from five energy laboratories in the trinational Upper-Rhine region is evaluated and a comparison to a conventional reference system is presented. Ten exemplary data-sets representing typical operation conditions for the laboratories in different seasons and the latest information on their national energy strategies are used to evaluate the primary energy consumption, CO2 emissions, and demand-related costs. Various conclusions on the ecologic and economic feasibility of hybrid building energy systems are drawn to provide a toe-hold to the engineering community in their planning and development.

Research on energy control of low voltage PV storage microgrid

Abstract Renewable energy sources cannot guarantee long-term and stable output supply when they are given energy consumption at the load end. At the same time, traditional solar and utility power supply systems cannot guarantee the priority of using solar energy to generate electricity, which has certain limitations. In this regard, an energy control strategy for achieving smooth and no backflow switching and prioritizing the use of clean energy solar energy under a joint power supply system of a solar power grid and a municipal power grid is proposed, which is used for a conventional combined power supply system of a contactor switching circuit. A topology of low voltage power grid is proposed, which effectively reduces the impact on the solar power grid and the municipal power grid during the system switching process combined control mode for controlling the current phase and repetitive control at the output end of the solar power grid in the combined power supply mode, and adding a feedforward control method for the grid voltage is adopted to ensure that the solar grid and the municipal power prioritize the use of clean energy solar energy and, at the same time, ensure the maximum utilization of solar energy. This paper chose the combined power supply system with the solar power grid and the municipal power grid as the object of this research. The topology structure and controlling strategy of the power supply system were comprehensively and deeply researched, and on this basis, the combined power supply system prototype was designed on the foundation of DSP28335 and CPLD. Moreover, hardware parameters are designed in the system, and an energy management optimization algorithm based on the system is developed on the corresponding experimental platform. The experimental results show that the proposed energy control strategy can effectively reduce the impact on the solar power grid and the municipal power grid during the system switching process, ensure that the solar power grid and the municipal power grid have no backflow combined power supply and give priority to the use of clean energy solar energy.

Control strategy for energy saving of refrigerating station systems in public buildings

In this work, a global energy saving optimal control strategy is proposed to address the high energy consumption of chilled water pumps caused by the small temperature difference between supply and return water, and the low efficiency of the chillers. The developed control strategy consists of two parts: the group control scheme and an optimal controller for variable water temperature/volume control. The group control scheme coordinates each chiller's operating time based on its running time and the chiller load rate. The optimal controller regulates the chilled water pump frequency according to the chilled water temperature difference and the valve opening at important terminal air conditioning units, and calculates the chilled water supply temperature under optimal comprehensive energy consumption of chillers and pumps. The proposed control strategy is easy to be embedded into the existing control systems of refrigerating stations. This scheme is then applied to the air conditioning system of a large public building in Beijing, China. Experimental results show that the proposed strategy can save up to 63% of energy consumption from the chilled water pumps and 6% from the chillers compared to the original control scheme.

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

The control of lithium‐ion batteries and supercapacitors in hybrid energy storage systems for electric vehicles: A review

This article discusses control solutions for hybrid energy systems composed of lithium-ion batteries and supercapacitors for electric vehicles. The advantages and disadvantages of the respective systems of lithium-ion batteries and supercapacitors as well as hybrid systems are discussed. This article summarizes the research on behavior modeling, optimal configuration, energy management, and so on from the two levels of energy storage components and energy storage systems, and provides theoretical and methodological support for the application and management of hybrid energy storage systems for electric vehicles. First, it summarizes the research progress of the hybrid energy system of lithium-ion batteries and supercapacitors and its research significance for the development of electric vehicles. Then the circuit models of lithium-ion batteries and supercapacitors are analyzed, and the control results of the respective systems and hybrid systems under different research methods are introduced to optimize resource allocation. Then based on the energy management and control strategy, the deficiencies of the respective research methods are analyzed. Finally, we emphasized and summarized some key issues for future research in this field. Therefore, the control optimization of hybrid systems has become the focus of the long-term development of electric vehicles. An overview of the lithium battery-supercapacitor hybrid system. Analyze the optimization strategy of lithium battery-supercapacitor hybrid system from energy management. Summarize the circuit research of the hybrid system.

Electric Vehicles Charging Stations’ Architectures, Criteria, Power Converters, and Control Strategies in Microgrids

The usage of electric vehicles (EV) has been increasing over the last few years due to a rise in fossil fuel prices and the rate of increasing carbon dioxide (CO2) emissions. EV-charging stations are powered by existing utility power grid systems, increasing the stress on the utility grid and the load demand at the distribution side. DC grid-based EV charging is more efficient than AC distribution because of its higher reliability, power conversion efficiency, simple interfacing with renewable energy sources (RESs), and integration of energy storage units (ESU). RES-generated power storage in local ESU is an alternative solution for managing the utility grid demand. In addition, to maintain the EV charging demand at the microgrid levels, energy management and control strategies must carefully power the EV battery charging unit. In addition, charging stations require dedicated converter topologies, control strategies, and need to follow set levels and standards. Based on EV, ESU, and RES accessibility, different types of microgrid architecture and control strategies are used to ensure optimum operation at the EV-charging point. Based on the above said merits, this review paper presents different RES-connected architecture and control strategies used in EV-charging stations. It highlights the importance of different charging station architectures with current power converter topologies proposed in the literature. In addition, a comparison of microgrid-based charging station architecture with its energy management, control strategies, and charging converter controls are also presented. The different levels and types of charging stations used for EV charging, in addition to controls and connectors used, are also discussed. An experiment-based energy management strategy was developed to control power flow among the available sources and charging terminals for the effective utilization of generated renewable power. The main motive of the EMS and its control is to maximize the usage of RES consumption. This review also provides the challenges and opportunities in EV-charging, and parameters in selecting appropriate charging stations.

Adaptive energy control strategy of PHEV based on the Pontryagin’s minimum principle algorithm

The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.

Research on Fuzzy Control Strategy and Genetic Algorithm Optimization for Parallel Hybrid Electric Vehicle

In this paper, the fuzzy control strategy for parallel hybrid electric vehicle(HEV) is proposed. Based on fuzzy logic theory, SOC and torque demand scale factor are taken as the input, a fuzzy logic controller is designed. Combining with the rule-based control strategy(RBCS), the engine and motor output torque are gotten, so the whole energy control strategy is proposed. Then the genetic algorithm(GA) is used to get the optimal results: fuel consumption and emission level, to verify the accuracy of fuzzy control strategy.

Design and Implementation of a New-energy Self-purification Device for Domestic Sewage

The self-purification MBR system for domestic sewage is commonly used in living areas, scenic spots, and industrial sites, for remote areas and other regions where energy supply is inconvenient. The new energy system provides energy to the sewage treatment system, and achieves remote monitoring, which is of substantial significance for saving energy to reduce pollution. In this paper, three modes of power generation, namely, photovoltaic, wind and temperature difference, are employed for domestic sewage purification systems, and a reasonable energy supply and energy control strategy is proposed according to the conventional water treatment; and then a new-energy self-purification system for domestic sewage is designed and experimented with supplying and consuming quantity, tracking the system-consumed energy and water quality data for one year, so as to present that the equipment has good application prospects.

Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview

Energy storage technology plays a role in improving new energy consumption capacities, ensuring the stable and economic operation of power systems, and promoting the widespread application of renewable energy technologies. Several new developments, ideas, approaches, and technologies have been introduced into this area from fields including materials, knowledge manage, electricity, control, and artificial intelligence. Based on the technical characteristics of renewable energy, this study reviews the roles, classifications, design optimisation methods, and applications of energy storage systems in power systems. First, we introduce the different types of energy storage technologies and applications, e.g. for utility-based power generation, transportation, heating, and cooling. Second, we briefly introduce the states of an energy storage system, along with its operation processes and energy storage capacity. Third, a comprehensive review is conducted on artificial intelligence applications in regards to optimisation system configuration, and energy control strategy, along with the applicability of different energy storage technologies. Finally, several issues and insights are discussed, offering new inspiration and concepts for the future study of integrated energy storage systems.

Optimization of group control strategy and analysis of energy saving in refrigeration plant

This study proposes a group control system optimization strategy coupled with a refrigeration plant model for a primary pump variable flow system, in order to improve the automation level of the refrigeration plant and maximize the energy saving potential. First, the control variables, optimization objectives, and operational constraints of the entire system were analyzed. Then, by collecting the operational data for each component and combining these data with theoretical analysis, the sub models were designed and the input parameters, output parameters, and optimization variables of each sub-model were defined. Next, the sub-models were coupled and the control variables of the operational combination, leading to the lowest overall system energy consumption, were obtained using a particle swarm optimization algorithm. Finally, considering a medical building in North China as an example, the application effectiveness of the optimal control strategy of the refrigeration plant was analyzed. The results showed that the energy-savings of the group control system after the optimization of the refrigerator, chilled water pump, cooling water pump, and cooling tower could reach 9.42, 8.04, 5.67, and 14.64%, respectively. This is a remarkable energy-savings benefit. The research described in this study also provides some reference for the design of group control systems in refrigeration plants.

Wind-Solar-Hydrogen Hybrid Energy Control Strategy Considering Delayed Power of Hydrogen Production

In order to meet the load demand of power system, BP based on genetic algorithm is applied to the typical daily load forecasting in summer. The demand change of summer load is analysed. Simulation results show the accuracy of the algorithm. In terms of power supply, the reserves of fossil energy are drying up. According to the prediction of authoritative organizations, the world's coal can be mined for 216 years. As a renewable energy, wind power has no carbon emissions compared with traditional fossil energy. At present, it is generally believed that wind energy and solar energy are green power in the full sense, and they are inexhaustible clean power. The model of wind power solar hydrogen hybrid energy system is established. The control strategy of battery power compensation for delayed power of hydrogen production is adopted, and different operation modes are divided. The simulation results show that the system considering the control strategy can well meet the load demand. Battery energy storage system is difficult to respond to short-term peak power fluctuations. Super capacitor is used to suppress it. This paper studies the battery supercapacitor complementary energy storage system and its control strategy. When the line impedance of each generation unit in power grid is not equal, its output reactive power will be affected by the line impedance and distributed unevenly. A droop coefficient selection method of reactive power sharing is proposed. Energy storage device is needed to balance power and maintain DC voltage stability in the DC side of microgrid. Therefore, a new droop control strategy is proposed. By detecting the DC voltage, dynamically translating the droop characteristic curve, adjusting the output power, maintaining the DC voltage in a reasonable range, reducing the capacity of the DC side energy storage device. Photovoltaic grid connected inverter chooses the new droop control strategy.

Cascade Utilization Battery Energy Storage System Architecture and Control Strategy

Cascade Utilization Battery Energy Storage System Architecture and Control Strategy

Study on control strategy of large-scale renewable energy connect to LCC-HVDC system

One of the important issues facing large-scale renewable energy development is to ensure the maximum consumption of large-scale renewable energy bases and the safe and stable operation of the power grid after connect. The research results show that when large-scale renewable energy generation is connected to the power grid, its volatility, randomness and intermittentness will have a certain impact on the system power balancing, reactive voltage and transient stability of the power grid. Renewable energy connect to LCC-HVDC is one of the commonly used long-distance large-scale power transmission methods. On the one hand, in order to ensure stable operation, the LCC-HVDC system needs to provide sufficient reactive power support for itself and the system. On the other hand, for large-scale renewable energy power stations, the collection lines are often long, and each renewable energy generator usually uses its own optimal operation as the control target, which causes reactive voltage control problems and harmonic amplification in the power station problem. Therefore, in order to coordinate the reactive power coordination and voltage regulation after the renewable energy is connected to the LCC-HVDC system, this paper studies the large-scale renewable energy power generation LCC-HVDC transmission connection and the supporting technology required for the development of renewable energy power generation. And the use of guidance is of great significance to help increase the accommodation space of large-scale renewable energy generation.

Coordinated Torque, Energy and Clutch Control Strategy for Downshifts in P2 Parallel xHEV Powertrains

<div class="section abstract"><div class="htmlview paragraph">This paper describes a methodology for investigating the controls coordination of clutch and propulsion torque sources relative to clutch energy, electrification energy consumption and output torque profile for offgoing controlled downshifts in P2 parallel xHEV powertrain configurations. The focus is on an 8 speed planetary automatic transmission, but the approach is equally applicable to any powerflow design with clutch-to-clutch shifting. The modeling technique is for an overall control strategy relative to achieving a targeted transmission input speed profile. A reduced order model of the transmission system is presented that accounts for input shaft acceleration and compensation of inertial contributions to offgoing clutch torque and transmission output torque. The coordinated control of offgoing clutch, engine and P2 electric motor torques are explored in the context of power on and off downshifts for clutch energy, P2 energy consumption and output torque trajectory that directly translates to responsiveness and/or perceived shift quality. The presence of a torque converter and lockup clutch and the influence of the lockup clutch state control on downshifting coordination and torque disturbance is also incorporated in the analysis. Potential improvements in downshift response, clutch controllability and output torque trajectory are provided for the particular 8 speed transmission powerflow considered. The methodology presented can easily be applied to any clutch-to-clutch transmission architecture and powerflow, whether planetary, dual clutch or other.</div></div>

Research on energy control strategy of PHEV

According to the characteristics of Plug-in hybrid electric vehicle, PHEV optimal fuzzy control is proposed. The engine operating points are controlled on optimal fuel consumption curve. Based on the power request and SOC, the fuzzy controller can distribute the engine given power and the condition of SOC drop with insufficient of energy recovery is improved to realize constant SOC control Furthermore. ADVISOR is developed by Fuzzy control model embedding, and make simulation experiment under different driving cycle. The studies on case prove that PHEV optimal fuzzy control can realize the constant SOC control under road conditions, so that it has the well robustness.

Control strategy of a fuel-cell power module for electric forklift

Fuel cell-battery hybrid systems for the powertrain, which have the advantage of emission-free power generation and adapt to material transport and emission reduction, are investigated. Based on the characteristics of the fuel cell system and the characteristics of the electric forklift truck powertrain system, this work defines the design principle of the control strategy to improve overall performance and economy. A simulation platform for fuel cell and electric vehicles has been established. The optimal performance of the fuel cell stack and the battery capacity were defined for the specific application. An energy control strategy was defined for different operating cycles and operating conditions. Model validation involved comparing simulation results with experimental data obtained during VDI60 test protocol. The main parameters that influence the forklift performance were defined and evaluated, such as energy loss, fuel cell operating conditions and different battery charging cycles. The optimal size of the fuel cell stack of 11 kW and the battery of 10 Ah was determined for the specific load profile with the proposed control strategy. The results obtained in this work forms the basis for an in-depth study of the energy management of fuel cell battery drive trains for forklift trucks.

Idle Duration Prediction for Manufacturing System Using a Gaussian Mixture Model Integrated Neural Network for Energy Efficiency Improvement

Manufacturing activities dominate the energy consumption and greenhouse emissions of the industrial sector. With the increasing concerns of greenhouse gas (GHG) emissions and climate change in recent years, the significance of the performance in terms of sustainability of manufacturing has been gradually recognized by both academia and industry. Various researches have been implemented to analyze, model, and reduce the energy consumption of manufacturing activities toward sustainable manufacturing. In a typical manufacturing system with multiple machines and buffers, the state of a certain machine is not only determined by the machine itself, but also the states of the adjacent machines and buffers. Therefore, machines may be in idle states due to nonincoming part from the upstream section of the manufacturing system or noncapacity to hold the delivered part to the downstream section of the manufacturing system. Those idle machines consume energy without production if there is no appropriate energy control strategy. In this article, we focus on the reduction of the energy waste for those idle machines in a typical multi-machine and multi-buffer manufacturing system. A Gaussian mixture model (GMM) integrated neural network is proposed to predict the duration of the idle periods for the idle machines, during which optimal energy control action can be identified and implemented under the constraint of production throughput of the manufacturing system. A manufacturing system simulator is built to provide the training dataset including the information, such as production throughput, energy consumption, buffer content, and failure rate, to the proposed neural network. A numerical case study for a five-machine-and-four-buffer manufacturing system is conducted to validate the effectiveness of the proposed prediction model in terms of the energy waste reduction for the idle machines. Note to Practitioners —This article proposes a prediction model to forecast the idle duration of the manufacturing machines in a typical multi-machine and multi-buffer manufacturing system. With this predicted result, two concerns in energy control for the idle machine, i.e., throughput protection and energy consumption reduction, can be more accurately modeled in decision-making procedure. Optimal energy control actions under the constraints of throughput maintaining and energy saving can be identified and implemented considering different warmup energy consumption and warmup time of the machines to reduce the energy waste for those machines in idle states without any production and thus, improve the energy efficiency of the entire manufacturing system.