Using machine learning to identify primary features in choosing electric vehicles based on income levels

An electric vehicle is becoming one of the popular choices when choosing a vehicle.People are generally impressed with electric vehicles’zero-emission and smooth drives,while unstable battery duration keeps people away.This study tries to identify the primary factors that affect the likelihood of owning an electric vehicle based on different income levels.We divide the dataset into three subgroups by household income from$50,000 to$150,000 or low-medium income level,$150,000 to$250,000 or medium-high income level,and$250,000 or above,the high-income level.We considered several machine learning classifiers,and naive Bayes gave us a relatively higher accuracy than other algorithms in terms of overall accuracy and F1 scores.Based on the probability analysis,we found that for each of these groups,one-way commuting distance is the most important for all three income levels.

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional ELMs.To demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

Feasibility of a New Ironless-stator Axial Flux Permanent Magnet Machine for Aircraft Electric Propulsion Application

With the development of aviation electrification,higher demands for electrical machines are put forward in aircraft electric propulsion systems.The aircraft electric propulsion requirements and propulsion motor features are analyzed in this paper.Comparing with conventional PM machines,ironless stator axial flux permanent magnet(AFPM)machine topologies with Litz wire windings allow designs with higher compactness,lightness and efficiency,which are suitable for high-frequency and high-power density applications.Based on the motor requirements and constraints of aircraft electric propulsion systems,this paper investigates a high-power 1 MW multi-stack ironless stator AFPM machine,which is composed of four 250kW modular motors by stacking in axial.The design guidelines and special attentions are presented,in term of electromagnetic,thermal,and mechanical performance for the high-frequency coils and Halbach-array PM rotor.Finally,an ironless stator AFPM motor is manufactured,tested and evaluated with the consideration of cost and processing cycle.The results show that the output power is up to 53.8kW with 95%efficiency at 9000r/min at this stage.The proposed ironless stator AFPM machine with oil immersed forced cooling proves to be a favorable candidate for application in electric aircraft as propulsion motors.

High Specific Power Electrical Machines: A System Perspective

There has been a growing need for high specific power electrical machines for a wide range of applications.These include hybrid/electric traction applications,and aerospace applications.A lot of work has been done to accomplish significantly higher specific power electrical machines especially for aerospace applications.Several machine topologies as well as thermal management schemes have been proposed.Even though there has been a few publications that provided an overview of high-speed and high specific power electrical machines[1-3],the goal of this paper is to provide a more comprehensive review of high specific power electrical machines with special focus on machines that have been built and tested and are considered the leading candidates defining the state-of-the art.Another key objective of this paper is to highlight the key“system-level”tradeoffs involved in pushing electrical machines to higher specific power.Focusing solely on the machine specific power can lead to a sub-optimal solution at the system-level.

CUTTING REGULARITY AND DISCHARGE CHARACTERISTICS BY USING COMPOSITE COOLING LIQUID IN WIRE CUT ELECTRICAL DISCHARGE MACHINE WITH HIGH WIRE TRAVELING SPEED

The analysis of cutting regularity is provided through using and comparing two typical cooling liquids. It is proved that cutting regularity is greatly affected by cooling liquid＇s washing ability. Discharge characteristics and theoretic analysis between two electrodes are also discussed based on discharge waveform. By using composite cooling liquid which has strong washing ability, the efficiency in the first stable cutting phase has reached more than 200 mm^2/min, and the roughness of the surface has reached Ra〈0.8 μm after the fourth cutting with more than 50 mm^2/min average cutting efficiency. It is pointed out that cutting situation of the wire cut electrical discharge machine with high wire traveling speed （HSWEDM） is better than the wire cut electrical discharge machine with low wire traveling speed （LSWEDM） in the condition of improving the cooling liquid washing ability. The machining indices of HSWEDM will be increased remarkably by using the composite cooling liquid.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

Multiobjective Electric Machine Optimization for Highest Reliability Demands

This article is about illustrating a workflow for incorporating reliability measures to typical electric machine design optimization scenarios.Such measures facilitate comparing designs not only for rated conditions,but also allow to analyze their performance in the presence of unevitable tolerances.Consequently,by additionally considering reliability or robustness as objectives compared to conventional optimization scenarios,designs featuring low parameter sensitiveness can be obtained.The analysis of the design’s reliability as part of solving optimization problems involves a significant increase in required numerical evaluations.To minimize the associated prolongation of the runtime,an approach featuring a design of experiments based reduction of required computations and a consequent surrogate modeling technique is presented here.After successful training,the metamodel can be applied for fast evaluating lots of different parameter combinations.A test problem is defined and analyzed.Based on the observed findings,the necessity of incorporating robustness evaluations to machine design optimization becomes evident.In addition,the derived models allow for studying the impact of any tolerance-affected parameter on the machine performance in detail.This facilitates further beneficial studies,as for instance the analysis of selected changes of tolerance levels rather than a general minimization of the respective ranges which usually is associated with high production cost.

Role of Advanced Materials in Electrical Machines

There has been a revived and growing role for electrical machines and drives across a wide range of applications.Such applications include,hybrid/electrical traction applications,aerospace applications,and renewable energy.All these applications present different set of requirements and challenges.The common trend is that there is a need for higher-performance electrical machines in terms of higher power/torque density,and higher efficiency while keeping cost under control.There has been a lot of work done around coming up with novel machine topologies,optimizing more conventional topologies as well as improved thermal management schemes.Like many other areas of engineering/research,advanced materials can play a key role in opening up the design space for electrical machines leading to a step improvement in their performance.This paper will present an overview of some of the key advanced materials that are either recently developed or are currently under development and their potential impact on electrical machines.

Hybrid Excited Permanent Magnet Machines for Electric and Hybrid Electric Vehicles

This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.

Implementation of Machine-to-Machine Solutions to Improve Quality of Service for Electrical Energy Distribution Networks in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of a GENEKO modem that exploits its digital inputs together with a full coverage of certain required auxiliary services so as to generate proper detection signals whenever failure currents occur;which allows incorporating the latest failure detection technology into the system.

Overview of the Rectangular Wire Windings AC Electrical Machine

The rectangular wire winding AC electrical machine has drawn extensive attention due to their high slot fill factor,good heat dissipation,strong rigidity and short end-windings,which can be potential candidates for some traction application so as to enhance torque density,improve efficiency,decrease vibration and weaken noise,etc.In this paper,based on the complex process craft and the electromagnetic performance,a comprehensive and systematical overview on the rectangular wire windings AC electrical machine is introduced.According to the process craft,the different type of the rectangular wire windings,the different inserting direction of the rectangular wire windings and the insulation structure have been compared and analyzed.Furthermore,the detailed rectangular wire windings connection is researched and the general design guideline has been concluded.Especially,the performance of rectangular wire windings AC machine has been presented,with emphasis on the measure of improving the bigger AC copper losses at the high speed condition due to the distinguished proximity and skin effects.Finally,the future trend of the rectangular wire windings AC electrical machine is prospected.

Quantitative Comparison of Electromagnetic Performance of Electrical Machines for HEVs/EVs

In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.

Recent Developments of Modulation and Control for High-Power Current-Source-Converters Fed Electric Machine Systems

The pulse-width-modulated(PWM)current-source converters(CSCs)fed electric machine systems can be considered as a type of high reliability energy conversion systems,since they work with the long-life DC-link inductor and offer high fault-tolerant capability for short-circuit faults.Besides,they provide motor friendly waveforms and four-quadrant operation ability.Therefore,they are suitable for high-power applications of fans,pumps,compressors and wind power generation.The purpose of this paper is to comprehensively review recent developments of key technologies on modulation and control of high-power(HP)PWM-CSC fed electric machines systems,including reduction of low-order current harmonics,suppression of inductor–capacitor(LC)resonance,mitigation of common-mode voltage(CMV)and control of modular PWM-CSC fed systems.In particular,recent work on the overlapping effects during commutation,LC resonance suppression under fault-tolerant operation and collaboration of modular PMW-CSCs are described.Both theoretical analysis and some results in simulations and experiments are presented.Finally,a brief discussion regarding the future trend of the HP CSC fed electric machines systems is presented.

Numerical-Analytical Method for Magnetic Field Computation in Rotational Electric Machines

A numerical-analytical method is applied for the two-dimensional magnetic field computation in rotational electric machines in this paper. The analytical expressions for air gap magnetic field are derived. The pole pairs in the expressions are taken into account so that the solution region can be reduced within one periodic range. The numerical and analytical magnetic field equations are linked with equal vector magnetic potential boundary conditions. The magnetic field of a brushless permanent magnet machine is computed by the proposed method. The result is compared to that obtained by finite element method so as to validate the correction of the method.

Global Control Simulation of Electric Vehicle Based on Finite State Machine Theory

Finite state machine theory (FSM) is introduced and applied to global control of electric vehicle. Theoretical adaptation for application of FSM in control of electric vehicle is analyzed. Global control logic for parts of electric vehicle is analyzed and built based on FSM. Using Matlab/Simulink, BJD6100-HEV global control algorithm is modeled and prove validity by simulation.

Machine Learning Empowered Electricity Consumption Prediction

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy usage.Due to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment waste.Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and consumed.This paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi Arabia.Hence,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia.The dataset used in this research is gathered exclusively from the Saudi Electric Company.The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets.The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning.Finally,the standalone model was tested against the bagging ensemble using the optimized ANN.The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model.The results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Effective Electrical Submersible Pump Management Using Machine Learning

Artificial lift plays an important role in petroleum industry to sustain production flowrate and to extend the lifespan of oil wells. One of the most popular artificial lift methods is Electric Submersible Pumps (ESP) because it can produce high flowrate even for wells with great depth. Although ESPs are designed to work under extreme conditions such as corrosion, high temperatures and high pressure, their lifespan is much shorter than expected. ESP failures lead to production loss and increase the cost of replacement, because the cost of intervention work for ESP is much higher than for other artificial lift methods, especially for offshore wells. Therefore, the prediction of ESP failures is highly valuable in oil production and contributes a lot to the design, construction and operation of oil wells. The contribution of this study is to use 3 machine learning algorithms, which are Decision Tree, Random Forest and Gradient Boosting Machine, to build predictive models for ESP lifespan while using both dynamic and static ESP parameters. The results of these models were compared to find out the most suitable model for the prediction of ESP life cycle. In addition, this study also evaluated the influence factor of various operating parameters to forecast the most impact parameters on the duration of ESP. The results of this study can provide a better understanding of ESP behavior so that early actions can be realized to prevent potential ESP failures.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Robustness Criteria for Concurrent Evaluation of the Impact of Tolerances in Multiobjective Electric Machine Design Optimization

This article is about a comparison of different measures for determining the robustness or reliability of electric machine designs in the presence of inevitable tolerances.The selected criteria shall be suitable for concurrent evaluation in the course of solving state-of-the-art large scale multi-objective opti-mization problems.In the past,besides particularly customized criteria,mainly gradient based measures,worst case information,or standard deviation based quantities were considered.In this work,the quantile measure is introduced for electric machine design optimization and compared with the existing solutions.The evaluation of a design’s robustness is typically examined based on finite element simulations.As for most measures a signif-icant number of parameter combinations and thus computations are required,a surrogate model assisted approach is presented to minimize computational effort and runtime.A test problem is defined and analyzed to illustrate the differences of selected robustness measures.Results reveal the importance of considering robustness in the optimization process.Moreover,a careful choice of appropriate measures has to be taken.Selected designs are compared and conclusions and an outlook on future activities are presented.