Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple - Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.publishedVersio

NOVEL METHODS FOR PERMANENT MAGNET DEMAGNETIZATION DETECTION IN PERMANENT MAGNET SYNCHRONOUS MACHINES

Monitoring and detecting PM flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. The key problems that need to be solved at this stage are to: 1) establish a demagnetization magnetic flux model that takes into account the influence of various nonlinear and complex factors to reveal the demagnetization mechanism; 2) explore the relationship between different factors and demagnetizing magnetic field, to detect the demagnetization in the early stage; and 3) propose post-demagnetization measures. This thesis investigates permanent magnet (PM) demagnetization detection for PMSM machines to achieve high-performance and reliable machine drive for practical industrial and consumer applications. In this thesis, theoretical analysis, numerical calculation as well as experimental investigations are carried out to systematically study the demagnetization detection mechanism and post-demagnetization measures for permanent magnet synchronous motors. At first a flux based acoustic noise model is proposed to analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. Then torque ripple is chosen for online PM demagnetization diagnosis by using continuous wavelet transforms (CWT) and Grey System Theory (GST). This model is able to reveal the relationship between torque variation and PM electromagnetic interferences. After demagnetization being detected, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization. Next, in order to compare the demagnetization detection accuracy, different data mining techniques, Vold-Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) based detection approach is applied to real-time PM flux monitoring through torque ripple again. VKF-OT is introduced to track the order of torque ripple of PMSM running in transient state. Lastly, the combination of acoustic noise and torque is investigated for demagnetization detection by using multi-sensor information fusion to improve the system redundancy and accuracy. Bayesian network based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. During the analysis of demagnetization detection methods, the proposed PM detection approaches both form torque ripple and acoustic noise are extensively evaluated on a laboratory PM machine drive system under different speeds, load conditions, and temperatures

Analysis and Design Optimization of a Permanent Magnet Synchronous Motor for a Campus Patrol Electric Vehicle

© 1967-2012 IEEE. This work presents the analysis, design and optimization of a permanent magnet synchronous motor (PMSM) for an electric vehicle (EV) used for campus patrol with a specific drive cycle. Firstly, based on the collected data like the parameters and speed from a test EV on the campus road, the dynamic calculation of the EV is conducted to decide the rated power and speed range of the drive PMSM. Secondly, according to these requirements, an initial design and some basic design parameters are obtained. Thirdly, optimization process is implemented to improve the performance of the designed PMSM. The permanent magnet (PM) structure, airgap length and stator core geometry are optimized respectively in this step. Different optimization processes are proposed to meet multiple optimization objectives simultaneously. Based on the finite element analysis (FEA) method, it is found that the harmonic of the optimized PMSM is lower than that of the initial design, and the torque ripple is reduced by 24%. The effectiveness of optimization on the core loss and PM eddy loss is validated and the temperature rise is suppressed effectively. Finally, a prototype is fabricated for the optimized PMSM and an experimental platform is developed. The test results verify that the optimized PMSM meets the requirements of the specific campus patrol EV well

Prediction and Diagnosis for Unsteady Electromagnetic Vibroacoustic of IPMSMs for Electric Vehicles Considering Rotor Step Skewing and Current Harmonics

Purpose: This study provides a detailed investigation on the prediction and diagnosis of unsteady electromagnetic vibroacoustic performance of IPMSMs for electric vehicles under typical unsteady operating conditions with consideration of rotor step skewing and current harmonics. Methods: Firstly, the control model considering the influence of PWM carrier modulation and rotor step skewing is established. Based on this, the currents of the IPMSM under unsteady operating conditions (driving condition and feedback braking condition) are obtained. Accordingly, the currents calculated through the control model are used as the excitation source of electromagnetic finite element. Then, the electromagnetic vibroacoustic performance under unsteady operating conditions is calculated through electromagnetic force subsection mapping and acoustic transfer vector (ATV) method. Moreover, the conditions where resonance vibroacoustic occurs are diagnosed. Finally, the results of prediction and diagnosis are fully verified by experiments of multiple physical fields. Results and Conclusions: The amplitude errors between prediction results and test results are less than 3.2%. The influence of current harmonics on electromagnetic vibroacoustic can be predicted. The frequency range and speed range of predicted peak vibroacoustic are consistent with the experimental results. The rotor step skewing can be used to weaken the vibroacoustic amplitude of IPMSMs under typical unsteady conditions in the full speed range. This study provides guidance for prediction and diagnosis for electromagnetic vibroacoustic performance of IPMSMs under typical unsteady operating conditions.</p

In-wheel Motors: Express Comparative Method for PMBL Motors

One of the challenges facing the electric vehicle industry today is the selection and design of a suitable in-wheel motor. Permanent Magnet Brushless (PMBL) motor is a good choice for the in-wheel motor because of its lossless excitation, improved efficiency, reduced weight and low maintenance. The PMBL motors can be further classified as Axial-Flux Twin-Rotor (AFTR) and Radial-Flux Twin-Rotor (RFTR) machines. The objective of this dissertation is to develop a fast method for the selection of appropriate in-wheel motor depending on wheel size. To achieve this, torque equations are developed for a conventional single-rotor cylindrical, twin-rotor axial-flux and twin-rotor radial-flux PMBL motors with slot-less stators based on magnetic circuit theory and the torque ratio for any two motors is expressed as a function of motor diameter and axial length. The theoretical results are verified, on the basis of magnetic field theory, by building the 3-dimensional Finite Element Method (FEM) models of the three types of motors and analyzing them in magnetostatic solver to obtain the average torque of each motor. Later, validation of software is carried out by a prototype single-rotor cylindrical slotted motor which was built for direct driven electric wheelchair application. Further, the block diagram of this in-wheel motor including the supply circuit is built in Simulink to observe the motor dynamics in practical scenario. The results from finite element analysis obtained for all the three PMBL motors indicate a good agreement with the analytical approach. For twin-rotor PMBL motors of diameter 334mm, length 82.5mm with a magnetic loading of 0.7T and current loading of 41.5A-turns/mm, the error between the express comparison method and simulation results, in computation of torque ratio, is about 1.5%. With respect to the single-rotor cylindrical motor with slotless stator, the express method for AFTR PMBL motor yielded an error of 4.9% and that of an RFTR PMBL motor resulted in an error of -7.6%. Moreover, experimental validation of the wheelchair motor gave almost the same torque and similar dynamic performance as the FEM and Simulink models respectively

Cal Poly Supermileage Electric Vehicle Drivetrain and Motor Control Design

The Cal Poly Supermileage Vehicle team is a multidisciplinary club that designs and builds high efficiency vehicles to compete internationally at Shell Eco-Marathon (SEM). Cal Poly Supermileage Club has been competing in the internal combustion engine (ICE) category of the competition since 2007. The club has decided it is time to expand their competition goals and enter their first battery electric prototype vehicle. To this end, a yearlong senior design project was presented to this team of engineers giving us the opportunity to design an electric powertrain with a custom motor controller. This system has been integrated into Ventus, the 2017 Supermileage competition car, bringing it back to life as E-Ventus for future competitions. The scope of this project includes sizing a motor, designing the drivetrain, programing the motor driver, building a custom motor controller, and finally mounting all these components into the chassis. The main considerations in this design are the energy efficiency measured in distance per power used (mi/kWh) and the whole system reliability. Driven train system reliability has been defined as the car starts the first time every time and can complete two competition runs of 6.3 miles each without mechanical or electrical failure. Drivetrain weight target was less than 25 pounds, and the finished system came in at 20 lbs 4 oz. Due to the design difficulties of the custom controller, three iterations were able to be produced by the end of this project, but there will need to be further iterations to complete the controller. Because of these difficulties our sponsor, Will Sirski, and club advisor, Dr. Mello, have agreed that providing the club with a working mechanical powertrain, powertrain data from the club chassis dynamometer using the programmed TI evaluation motor controller board, and providing board layout for the third iteration design for the custom controller satisfy their requirements for this project

Active suspension control of electric vehicle with in-wheel motors

In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices

Design and Application of Electrical Machines

Electrical machines are one of the most important components of the industrial world. They are at the heart of the new industrial revolution, brought forth by the development of electromobility and renewable energy systems. Electric motors must meet the most stringent requirements of reliability, availability, and high efficiency in order, among other things, to match the useful lifetime of power electronics in complex system applications and compete in the market under ever-increasing pressure to deliver the highest performance criteria. Today, thanks to the application of highly efficient numerical algorithms running on high-performance computers, it is possible to design electric machines and very complex drive systems faster and at a lower cost. At the same time, progress in the field of material science and technology enables the development of increasingly complex motor designs and topologies. The purpose of this Special Issue is to contribute to this development of electric machines. The publication of this collection of scientific articles, dedicated to the topic of electric machine design and application, contributes to the dissemination of the above information among professionals dealing with electrical machines

State feedback control for a PM hub motor based on gray Wolf optimization algorithm

© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response