Energy-saving Control Strategy Research Articles

Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems

The automatic control device is a critical component of absorption refrigeration systems. Its functional enhancement can reduce operating costs, improve energy efficiency, and ensure long-term stable unit operation. Given that absorption refrigeration systems operate under various dynamic conditions, the rational design of control strategies is particularly important. This study analyzes the influence of changes in the cooling water and heat source water flow rates on the outlet temperature of chilled water in the unit based on the open-loop response characteristics of absorption refrigeration systems. It proposes a dual-loop energy-saving control strategy for single-effect hot water lithium bromide absorption refrigeration systems based on the setpoint comprehensive optimization algorithm. Considering the multiple variables, strong coupling, large inertia, long time delay, and nonlinear characteristics of absorption refrigeration systems, as well as the difficulties in modeling these systems, this study applies a model-free adaptive control algorithm to the system’s control. It derives both SISO and MIMO model-free control algorithms with time-delay components. Through simulations comparing MFAC, improved MFAC, and traditional PID control, the dual-loop energy-saving control strategy is demonstrated to effectively reduce system heat consumption by approximately 20%, decrease power consumption by about 10%, and enhance the system’s SCOP by approximately 19.3%.

Research on Energy-Saving Control Strategy of Loader Based on Intelligent Identification of Working Stages

Research on Energy-Saving Control Strategy of Loader Based on Intelligent Identification of Working Stages

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Research on Efficiency of Permanent-Magnet Synchronous Motor Based on Adaptive Algorithm of Fuzzy Control

In today’s world, energy is undoubtedly one of the most significant problems. As the global electricity consumption continues to increase, electric motors, which are widely used as power devices, account for an increasingly prominent proportion of the energy consumed. Motors now consume about 45% of the total electricity in the world (60% in China); therefore, improving motor efficiency has become an important way to achieve carbon emission reduction and sustainable development. The aim of this research was to devise a new strategy to reduce CO2 emissions other than by building green power factories, because even the building of green power factories produces a great deal of CO2 emissions, and improving motor efficiency to reduce CO2 emissions could contribute to sustainable development worldwide. However, the improvement of motor efficiency encounters challenges, such as nonlinearity and disturbances, which affect the motor performance and energy efficiency. To address this issue, this paper proposes a control algorithm for permanent-magnet synchronous motors (PMSMs) that is highly efficient and would be most widely used based on a fuzzy control adaptive forgetting factor. It aims to enhance the efficiency and accuracy of the online parameter estimation for the PMSM flux linkage, thereby achieving more precise and energy-efficient motor control. Firstly, the recursive least-squares parameter estimation algorithm is used to identify the parameters of the PMSM. This ensures that the parameter estimation values can be dynamically updated with data changes, adapting to the time-varying parameters. Secondly, the Padé approximation method is adopted, which is a method that does not depend on the motor hardware, to improve the accuracy of the linearized model of the motor. Finally, a control algorithm based on the fuzzy control adaptive forgetting factor algorithm is constructed on a physical experimental platform. A comparison of these results proves that the control technology under this algorithm provides a new energy-saving control strategy that can estimate the motor flux linkage parameters more accurately, help to reduce energy consumption, promote the use of clean energy, and achieve sustainable performance optimization.

Energy-Saving Control Strategy for Multisleep States of CNC Machine Tool Considering Components Priority

As the representative energy-consuming equipment in the manufacturing industry, machine tools consume lots of energy during the non-machining period, which leads to low energy efficiency. The existing strategies do not consider the startup sequence among components, which causes the control parameters to fail to run during the practical startup process, and this paper conducts energy-saving research to address the issues. The relationship of priority among components was used as the constraint to obtain an energy-saving control strategy for multi-sleep states of machine tools considering components priority. Furthermore, the improved whale algorithm was applied to solve the energy-saving control strategy of the machine tool, and the search and convergence ability of the algorithm was improved by introducing the chaotic convergence factor and adaptive weight coefficient. Finally, the energy-saving strategy was utilized with a vertical CNC machine tool as an experimental example to mill the end face of the randomly arriving workpieces. The results show that this changed algorithm has a better solution speed and effect than the ordinary whale algorithm and some other improved whale algorithms. Compared with the practical control process and other control strategies, the control strategy considering components priority can obtain more stable productivity and better energy-saving effect.

Energy-efficient load domain based dual-motor planetary differential gear train for mechatronic equipment under inconsistent working conditions: Modeling, design, and optimization

The conventional servo powertrain exhibits low energy efficiency under inconsistent working conditions, such as low speed with high torque or high speed with low torque. To handle this problem, this paper presents an in-depth study of a dual-motor planetary differential gear train with brakes (DMPTB) that can allocate the working requirements to two motors to meet higher energy efficiency. A comprehensive energy model of the DMPTB is developed, and the effect of losses on the output torque is considered to give more explicit torque-speed characteristics. To ensure both motors work in high energy efficiency domains, evaluation functions of the energy-saving control strategy and parameter optimization are proposed. The results of a stamping process demonstrate that the energy consumption and installed power of the DMPTB are reduced by about 8% and 27% compared to a dual-motor torque coupling gear train, respectively. Furthermore, by comparing the energy efficiency maps, the energy-saving principle of the DMPTB is revealed. It can be seen that the application of brakes is crucial for the energy-saving effect of the DMPTB.

A model predictive control approach for energy saving optimization of an electronic assembly line

The energy-saving potential of original equipment manufacturers (OEMs) for electronic products is substantial owing to the huge market size of assembling industry in China. A semi-automatic electronic assembly line (SAEAL) for smart-phones is introduced, and its digital twin (DT) enabled energy-saving platform is developed as the hardware foundation. We propose a stochastic dynamics model via max-plus algebra to characterize the spatio-temporal nature of state transition, which provides dynamical opportunity windows for further energy-saving control strategy. Accordingly, model linearization is conducted to obtain linear state and output equations. Then, a model predictive control (MPC) approach for energy saving optimization is provided to determine the optimal start-stop control signal. The dynamics modeling and MPC approach have been applied and verified in the case of assembly line. The results show that the optimization approach could reach a proportion of 49% for the equipment sleep time of total running time and save a considerable amount of energy for normal production of OEMs.

Research on hybrid power load matching control method of new energy pumping machine

In order to improve the energy saving effect of new energy pumping machine, the power matching energy saving control of wind energy, electric energy and pumping machine load should be carried out when the actual wind speed is lower than the rated wind speed of the system. In this paper, according to the wind power hybrid transmission system, we study the load matching energy saving control strategy and method that takes into account the simultaneous change of wind speed and load, and propose the hybrid power load matching adaptive energy saving control strategy based on the cascade controller. In the strategy, the motor torque control method is used to eliminate the pressure deviation and realize the system pressure tracking load matching adaptive control, while keeping the motor output torque stable.

Experimental study on precise control air quantity on demand by the cooperation of multiple main fans in mine

Due to the global energy crisis worsening, numerous businesses have prioritized energy conservation and consumption reduction. However, in mines with multiple main fans, the main fans' operation efficiency is low to meet the ventilation on-demand, and the ventilation system's energy consumption is very high. This paper proposes an energy-saving control strategy for ventilation on demand with multiple fans network, which can save much energy in mines. We use the circuit method to calculate the operating condition parameters required for the joint control of multiple main fans by analyzing the influence features of multiple fans' mutual disturbance. We study the advantages and shortcomings of the traditional fan control method and create control strategy expressions for multiple fans. The two-step fan control strategy uses the adjusting wheel blades' angle method to determine the high-efficiency operating angle and the modulating fan speed method for accurate air quantity control. Furthermore, the effectiveness of the control strategy in ventilation on demand and fan energy-saving are verified on the three-dimensional complex ventilation pipe network experimental platform. The research results show that, compared to the two typical fan control strategies, our control strategy can save energy by 27.74% and 26.92%, respectively. If this strategy is applied to a mine with two main fans rated power of 500 kW, it can save energy 2,429,806 kW per year, saving USD 442.69 per day or USD 161,582.1 per year. The payback period for this strategy is approximately 14.1 months. Therefore, this research can provide a promising step towards intelligent control of air quantity on-demand while indicating enormous potential for energy savings in mine ventilation systems.

An Energy Saving Control Strategy Based on Multi-Agent Q-Learning Algorithm for Data Center

In recent years, the application of green renewable energy to data centers has become an important trend. Traditional solutions lack the consideration of matching tasks to renewable energy supplies. Therefore, in the face of diverse real-time computing tasks, how to reduce the total energy cost while ensuring the quality of service is an important challenge for the data center in the future. In this paper, our focus is on using the information on renewable energy supply and task characteristics as input states to assign tasks that maximize user satisfaction while meeting the minimum total cost of energy consumption. We consider the diversity of real-time tasks and design three different task types: the most crucial task, the crucial task and the non-crucial task. According to the different characteristics of these tasks, we propose a scheduling algorithm based on multi-agent, which uses multiple sets of agents with different initial positions to parallel search in different dimensions of the parameter space to find the optimal solution. To further optimize the algorithm, we eliminate the centralized noise solution based on the Pareto sorting method and sort the multiple optimal solutions to highlight the most suitable solution. The experimental results show that the proposed algorithm compared with other algorithms can reduce the total energy consumption by 11% and increase the customer satisfaction by 13% on average, and has better performance and applicability.

Medium and Low-Level Energy Saving Control Strategies for Electric-Powered UAVs

Medium and Low-Level Energy Saving Control Strategies for Electric-Powered UAVs

Energy saving control and operation strategy analysis of thermal coupling system of fuel cell and metal hydride tank

The thermal coupling system (TCS), where the heat generated by proton exchange membrane fuel cell (PEMFC) is transferred to the metal hydride (MH) tank, has been investigated extensively and proved beneficial to system compactness as well as capital cost. However, there is a lack of attention to the regulation of hydrogen desorption to increase the efficiency of hydrogen-to-power/heat (ηh−p), especially when the TCS is used in a distributed energy system (DES) combining power and heat. In this study, an energy saving control strategy for a TCS applied in DES is proposed to increase the ηh−p on the premise of the satisfaction of cosine-form hydrogen demand. The threshold temperature is introduced to characterize the maximum of ηh−p, and a prediction framework of threshold temperature for arbitrary signal is proposed based on its functional relationship with five operation parameters and Fourier decomposition method (FDM). The operation strategy of two-tank TCS is also discussed. The results indicate that up to 7.26 × 103 kJ more heat could be utilized by energy saving control strategy compared with the reference case. The relative error of predicted threshold temperature within 0.14% is obtained for a square wave and aperiodic signal, verifying the generalization capability of the proposed framework. Besides, the operation strategy that the rated hydrogen demand is supplied by both MH tanks, is suggested in a two-tank TCS to increase the hydrogen utilization and extend the supply duration.

Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of “Low carbon”, a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers’ average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains.

An Energy-Saving Regulation Framework of Central Air Conditioning Based on Cloud–Edge–Device Architecture

As energy plays a fundamental role in our modern life and most of a building’s energy is used for air conditioning, understanding the sustainable regulation theory of central air conditioning remains a significant scientific issue. In view of three shortcomings of existing energy-saving regulation methods of central air conditioning: (1) few studies on low-latency, high-reliability, and safer energy-saving control operation modes, (2) lack of consideration for human comfort, and (3) insufficient analysis of the comprehensive impact of the human–machine–environment, this paper proposes an energy-saving control framework of central air conditioning based on cloud–edge–device architecture. The framework establishes a prediction model of human comfort based on recurrent neural network. An intelligent energy-saving control strategy is proposed to ensure indoor personnel’s thermal comfort, considering the human–machine–environment factors. This study provides a basis for better understanding the sustainable control theory of building central air conditioning. Finally, the experiment proves that the proposed method can effectively reduce the energy consumption of central air conditioning. Compared with traditional regulation approaches, the proposed real-time control strategy can save up to 91% of energy consumption, depending on the environment, and advance control strategies can save an average of 4%.

Energy-Saving Control of Hybrid Tractors Based on Instantaneous Optimization

In this study, an energy-saving control strategy based on instantaneous optimization is proposed to improve the energy efficiency of hybrid tractors. Using a parallel diesel–electric hybrid tractor as the research object, the topological and working characteristics were analyzed, and a coupled dynamic model of rotary tillage and tractor plow was constructed. Aiming to minimize the equivalent fuel consumption of the entire machine, the motor and diesel engine torques were taken as the control variables, and the state of charge of the power battery was taken as the state variable. Subsequently, an energy-saving control strategy based on instantaneous optimization is proposed. Finally, a simulation experiment was carried out using MATLAB to verify the effectiveness of the energy-saving control strategy based on instantaneous optimization. Compared with the energy-saving control strategy based on power-following, the results show that energy-saving control strategy based on instantaneous optimization can reasonably control the operating state of the diesel engine and motor. Therefore, the diesel engine and motor work in the high-efficiency area, and effectively reduce the equivalent fuel consumption of the tractor during field operation. Under rotary tillage and plowing conditions, equivalent fuel consumption is reduced by 4.70% and 6.31%, respectively.

Coordinated control of heterogeneous vehicle platoon stability and energy-saving control strategies

This work presents an overall control strategy for heterogeneous vehicle platoons to minimize energy consumption and improve transportation efficiency by sustaining the string stability of the platoon on signalized arterials under external perturbations. First, the stability criterion is derived by analyzing the string stability of multi-time-lag heterogeneous platoon based on the intelligent driver model (IDM). The optimal model of vehicle platoon consumption is reconstructed to reduce the optimization variables to simplify the solution process. Then, by combining the reconstructed optimal control model with stability, a stable eco-driving model is established for a heterogeneous vehicle platoon consisting of multiple purely electric intelligent connected vehicles (ICVs) and human-driven vehicles (HVs). The overall speed control strategy is proposed by combining the vehicle platoon stability and energy consumption for nonlinear optimization analysis. The platoon is made to pass through the signal intersection with the highest efficiency and the lowest energy consumption to achieve a balance of stability and energy saving. Third, the overall model is optimally solved by a global optimization algorithm, and the numerical simulation has clearly validated the performance of the proposed driving control strategy.

Research on Energy Management Strategy of Pure Electric Vacuum Vehicle Based on Fuzzy Control

The current pure electric vacuum vehicle is equipped with main and auxiliary motors, and the two motors work independently without affecting each other. The traditional auxiliary motor usually operates with constant power while the main motor is only responsible for the vehicle driving. The lack of cooperation between the two motors results in high energy consumption. Therefore, formulating a reasonable strategy for the two motors has a significant effect on the performance of the vacuum vehicle. This paper takes a pure electric vacuum vehicle as an example to propose an energy management strategy based on fuzzy control. First, for the working motor, a fuzzy controller is designed by taking the vehicle speed and acceleration as input and motor speed and torque as output. Therefore, the vacuum vehicle can automatically adjust the operating power of the cleaning system according to the real-time road conditions; the driving motor control strategy adopts a closed-loop control strategy that combines driver input and vehicle state parameter feedback based on considering the operating motor. Finally, the effectiveness of the strategy is verified by simulation. The results show that the energy-saving control strategy effectively reduces the power consumption per 100 km and increases the driving range, which is of great significance to the development and design of the vacuum vehicle.

Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

The unmanned ground vehicle (UGV) is developing towards miniaturization, lightweight and intelligence. Path tracking and energy-saving control strategies are of great significance. In this study, a hierarchical control strategy is proposed to realize the path tracking control of six-wheel independent drive UGV (6WID UGV) based on differential steering, where actuator fault and energy-saving are considered. Firstly, a model free adaptive sliding mode control (MFASMC) method is utilized in the upper controller to estimate the required yaw moment. Then, in the lower controller, the seeker optimization algorithm (SOA) is utilized to achieve the torque distribution, realize the required yaw moment and longitudinal force, and reduce the energy consumption in the process of path tracking. The fault-tolerant control is considered in the torque distribution strategy to ensure the stability of 6WID UGV in case of motor execution fault. Finally, the effectiveness of the proposed scheme is verified by simulation under various operating conditions. The results show that the proposed method has better control performance, and the characteristics of energy-saving are optimized while ensuring the vehicle stability.

Coordinated Control of Heterogeneous Vehicle Platoon Stability and Energy-Saving Control Strategies

Coordinated Control of Heterogeneous Vehicle Platoon Stability and Energy-Saving Control Strategies

Improved energy saving control of IPMSM based on the weighted average current method

Aiming at the low efficiency of vector control system caused by unreasonable current allocation, an improved energy saving control strategy based on weighted average current method is proposed in this paper, and the research is carried out on the background of interior permanent magnet synchronous motor vector control system. This method does not add new parameters, but achieves the goal of reducing motor loss and improving current utilization rate. Firstly, according to the mathematical model of permanent magnet synchronous motor considering iron loss resistance, the relationship between motor torque and d-q axis current is deduced. Then, by introducing the weighted average current factor, the minimum copper loss current and the minimum iron loss current are calculated analytically. Finally, the simulation analysis shows that the energy saving strategy proposed in this paper can further reduce the electrical loss of permanent magnet motor compared with the traditional energy saving control method, which proves the feasibility of this method in improving the energy efficiency of the vector control system of permanent magnet motor.