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.

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.

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.

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.

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.

Control Strategies for PM Synchronous Machine Controlled Rectifier Intended for Heavy-Duty Vehicle

In order to charge batteries and supply all the electrical devices like wheel-motors used in a heavy-duty hybrid electric vehicle, a solution consists in using an assembly permanent magnet generator driven by a diesel engine and a three-phase insulated gate bipolar transistor/diodes bridge controlled rectifier connected to the battery. In this work, hysteresis current control strategies combined with a judicious current sensing mode for the assembly permanent magnet synchronous machine-controlled rectifier are investigated. Main issues first concern the different kinds of transistors switching modes allowed by the proposed current sensing mode when the machine operates either as a generator or as a motor. Second, the modulated hysteresis method is presented, which merges the performances of robustness and dynamic of the classical hysteresis method and imposes the switching frequency alike pulsewidth modulation techniques. A test bench at reduced power permits to test the switching modes as well as classical and modulated hysteresis methods for both motor and generator operating modes and to validate the simulation predictions. The digital signal processor algorithm elaborated for the control strategy is flexible and adaptable to all kinds of transistor switchings and machine operating modes.

Development of High Torque and High Power Density Hybrid Excitation Flux Switching Motor for Traction Drive in Hybrid Electric Vehicles

This paper presents design feasibility study and development of a new hybrid excitation flux switching motor (HEFSM) as a contender for traction drives in hybrid electric vehicles (HEVs). Initially, the motor general construction, the basic working principle and the design concept of the proposed HEFSM are outlined. Then, the initial drive performances of the proposed HEFSM are evaluated based on 2D-FEA, in which the design restrictions, specifications and target performances are similar with conventional interior permanent magnet synchronous motor (IPMSM) used in HEV. Since the initial results fail to achieve the target performances, deterministic design optimization approach is used to treat several design parameters. After several cycles of optimization, the proposed motor makes it possible to obtain the target torque and power of 333 Nm and 123 kW, respectively. In addition, due to definite advantage of robust rotor structure of HEFSM, rotor mechanical stress prediction at maximum speed of 12,400 r/min is much lower than the mechanical stress in conventional IPMSM. Finally, the maximum torque and power density of the final design HEFSM are approximately 11.41 Nm/kg and 5.55 kW/kg, respectively, which is 19.98% and 58.12% more than the torque and power density in existing IPMSM for Lexus RX400h.

Recent Advances in Variable Flux Memory Machines for Traction Applications: A Review

This paper overviews the recent advances in variable flux memory machines(VFMMs)for traction applications with particular reference to newly emerged machine topologies and related control strategies.Due to the use of flux memorable low coercive force(LCF)magnets,the air-gap flux of VFMM can be flexibly varied via a magnetizing current pulse.Thus,the copper loss associated with the flux weakening current and high-speed iron loss can be significantly reduced,and hence high efficiency can be achieved over a wide speed and torque/power operation.These merits make VFMM potentially attractive for electric vehicle(EV)applications.Various novel VFMMs are reviewed with particular reference to their topologies,working principle,characteristics and related control techniques.In order to tackle the drawbacks in the existing VFMMs,some new designs are introduced for performance improvement.Then,the electromagnetic characteristics of an exemplified EV-scaled switched flux memory machine and various benchmark traction machine choices,such as induction machine,synchronous reluctance machines,as well as commercially available Prius 2010 interior permanent magnet(IPM)machine are compared.Finally,the key challenges and development trends of VFMM are highlighted,respectively.

Modularity Techniques in High Performance Permanent Magnet Machines and Applications

This paper reviews the modularity techniques in the stator manufacture of permanent magnet machines for different applications.Some basic concepts of modular machines are firstly introduced.Modular machines for several typical applications are then described in details,including domestic appliances,automobiles and electric vehicles,more electric aircrafts and civic applications,wind power generators,etc.Besides,the influence of manufacture tolerance gaps and flux barriers on the electromagnetic performance is discussed.

State of charge estimation for electric vehicles using random forest

This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating conditions.The electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery management.This study applies a Random Forest(RF)machine learning algorithm to improve SOC estimation.Traditionally,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions.Previous methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV applications.In contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world scenarios.By leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC values.This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions.Comprehensive comparative analyses showcase the superiority of the RF over ELM.The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV industry.The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3.This integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV technology.Importantly,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a lower Mean Absolute Error(MAE)of 4.432,1%versus 5.111,2%for ELM across rigorous k-fold cross-validation testing,reaffirming its superiority in quantitative SOC estimation.

Schedulable capacity forecasting for electric vehicles based on big data analysis

Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

Reliability of electric vehicle charging infrastructure:A cross-lingual deep learning approach

Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation sector.Deployment of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across regions.In this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including Chinese.We investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously available.We find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of interest.This evidence contrasts with predictions in the U.S.and European markets,where the performance is closer to parity.We also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.

Data-driven smart charging for heterogeneous electric vehicle fleets

The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.

Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and remaining-useful-lifetime.Using several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

电动汽车用多相电驱重构型车载充电系统关键技术综述

电驱重构型车载充电系统为电动汽车电气部分提供了一种全新轻量化设计思路,旨在利用电驱单元分时执行电驱与充电2种变流功能。相较于三相电驱系统,多相电驱系统具有输出转矩平滑、容错性能强等诸多优势特性。鉴于此,该文首先归纳现有并网式多相电驱重构型车载充电系统的拓扑结构与控制策略,并介绍其容错运行方案;然后,着重分析太阳能电动汽车用多能量端口电驱重构型车载充电系统的工作原理与控制策略;最后,总结全文并对多相电驱重构型车载充电系统的发展方向进行展望。

实现纹波电流还原的PMSM端口模拟算法

电机模拟器作为一种模拟电机端口特性的三相电力电子装置,为电驱动系统的测试提供了高效的测试手段。电机模拟器还原目标电机端口特性的标志是精确还原目标电机的工作电流,现有研究实现了基波电流及较低阶次谐波电流的还原,但针对电机在电机控制器驱动下的高频纹波电流的还原仍严重依赖滤波电感与目标电机电感的匹配,降低了电机模拟器的通用性。为此,本文基于电路等效虚拟法将永磁同步电机等效电路拆分为两部分,一部分由电机模拟器实际电路代替,另一部分由控制算法模拟,并结合无差拍电流预测控制对控制差拍进行了补偿,提出了前馈解耦无差拍电流跟随策略。实验结果表明,基于新提出的永磁同步电机端口模拟算法,电机模拟器可以在不更换电感的情况下模拟不同参数的永磁同步电机,高频纹波电流的频域跟踪误差从传统策略的160%降低至20%,显著提高了电机模拟器对永磁同步电机端口特性的模拟精度。

分布式驱动系统用轮毂电机及其技术综述

应用轮毂电机的分布式驱动型电动汽车在系统效率、车身控制、平台开发等方面具有突出优势,是新一代电动汽车领域的研究热点和重要发展方向。该文对比分析电动汽车集中式驱动与分布式驱动的技术优缺点,指出分布式驱动系统用轮毂电机具有巨大的发展潜力;介绍国内外学者在轮毂电机拓扑创新方面做的工作,总结该领域的研究特点以及面向高转矩密度的技术发展方向;阐述振动分析与抑制、温升计算与冷却、多目标优化与算法加速这三大轮毂电机优化设计研究领域的技术进展,提出全局性多物理场分析优化理论的技术空白;阐明驱动控制领域的研究主要围绕单电机层的高性能控制与容错控制、整车层的多电机协同控制展开;最后,该文总结分布式驱动轮毂电机的研究进展,展望该领域的技术研究方向。

计及退磁分析的电驱重构型车载充电系统

电驱重构型车载充电(EDROC)系统通过将驱动系统组件复用为充电系统,分时执行电驱和充电功能,能够有效降低成本和充电设备的体积占用。然而,在高温工况下EDROC系统中的永磁同步电机(PMSM)存在不可逆退磁的风险。鉴于此,本文通过分析电机在不同条件下退磁程度,进而提出电机在充电工况下安全运行边界。首先,分析EDROC系统在直流充电和边跑边充模式下的绕组电流;其次,建立电机不可逆退磁模型;然后,针对不同充电模式,详细分析不同温度和充电电流下电机退磁程度,并以此建立电机在充电工况下的安全运行边界;最后,基于一台2 kW实验样机开展实验,验证本文不可逆退磁模型的准确性以及安全运行边界对EDROC系统安全运行的必要性。