Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.

Control Strategy Optimization of Voltage Source Converter Connected to Various Types of AC Systems

Connecting the voltage source converters(VSCs) to various types of AC systems results in different operation characteristics and core problems associated with traditional control strategies. Therefore, it is necessary to optimize the control strategies of the VSCs according to the types of AC systems.For the VSCs connected to islanded renewable power plants, a voltage/frequency(V/f) droop control strategy is proposed to damp fluctuations of AC voltage and frequency in the island,which is vital for bipolar VSC control. In addition, a multibranch impedance equivalent method for renewable power plants is proposed, with which large-scale renewable power plants can be modeled accurately in the frequency domain to prevent wide-band oscillation. For the VSCs connected to strong AC systems, smart AC voltage and coordinated frequency transient control strategies are proposed, which can improve AC system transient stability. For the VSCs connected to weak AC systems, the relationship between the system stability and strength is analyzed, and then the control strategy of inner-loop control parameter optimization and outer-loop power limiting(if necessary) is proposed to improve the stability of the allied system. The proposed strategies are verified by both software simulation and field commissioning.

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential field method,the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets.Secondly,according to the finite-time convergence high-order differentiator,a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory.Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking,high precision,strong stability and avoiding chattering.Finally,a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy.Theoretical analysis and numerical simulation results show that the model has the fast convergence,high control accuracy,strong stability and good robustness.

Optimal control strategy for energy saving in trains under the four-aspect fixed autoblock system

This paper deals with both the leading train and the following train in a train tracking under a four-aspect fixed autoblock system in order to study the optimum operating strategy for energy saving. After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model is created based on the dynamics equation of the Wains. In addition to safety, energy consumption and time error are the main concerns of the model. Based on this model, dynamic speed constraints of the following train are proposed, defined by the leading gain dynamically. At the same time, the static speed constraints defined by the line conditions are also taken into account. The parallel genetic algorithm is used to search the optimum operating strategy. In order to simplify the solving process, the external punishment function is adopted to transform this problem with constraints to the one without constraints. By using the real number coding and the strategy of dividing ramps into three parts, the convergence of GA is accelerated and the length of chromosomes is shortened. The simulation result from a four-aspect fixed autoblock system simulation platform shows that the method can reduce the energy consumption effectively in the premise of ensuring safety and punctuality.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

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 con-straints 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 re-frigerator,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.

Optimal control analysis of Ebola disease with control strategies of quarantine and vaccination

Background:The 2014 Ebola epidemic is the largest in history,affecting multiple countries in West Africa.Some isolated cases were also observed in other regions of the world.Method:In this paper,we introduce a deterministic SEIR type model with additional hospitalization,quarantine and vaccination components in order to understand the disease dynamics.Optimal control strategies,both in the case of hospitalization(with and without quarantine)and vaccination are used to predict the possible future outcome in terms of resource utilization for disease control and the effectiveness of vaccination on sick populations.Further,with the help of uncertainty and sensitivity analysis we also have identified the most sensitive parameters which effectively contribute to change the disease dynamics.We have performed mathematical analysis with numerical simulations and optimal control strategies on Ebola virus models.Results:We used dynamical system tools with numerical simulations and optimal control strategies on our Ebola virus models.The original model,which allowed transmission of Ebola virus via human contact,was extended to include imperfect vaccination and quarantine.After the qualitative analysis of all three forms of Ebola model,numerical techniques,using MATLAB as a platform,were formulated and analyzed in detail.Our simulation results support the claims made in the qualitative section.Conclusion:Our model incorporates an important component of individuals with high risk level with exposure to disease,such as front line health care workers,family members of EVD patients and Individuals involved in burial of deceased EVD patients,rather than the general population in the affected areas.Our analysis suggests that in order for R0(i.e.,the basic reproduction number)to be less than one,which is the basic requirement for the disease elimination,the transmission rate of isolated individuals should be less than one-fourth of that for non-isolated ones.Our analysis also predicts,we need high levels of medication and hospitalization at the beginning of an epidemic.Further,optimal control analysis of the model suggests the control strategies that may be adopted by public health authorities in order to reduce the impact of epidemics like Ebola.

Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction

This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm.Three parallel soft actor-critic(SAC)networks are trained for high speed,medium speed,and low-speed conditions respectively;the reward function is designed as minimizing the cost of energy cost and battery aging.During operation,the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization(LVQ)neural network.On top of that,a driving cycle reconstruction algorithm is proposed.The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed,medium speed,and low speed,based on which the algorithms are online updated.The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data.The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38%after the online update for uncertain driving conditions.