The electrification of the automotive has recently shaped the current revolution in transportation. Many automotive companies are now producing their own electric vehicle (EV) models. To cope with these advances, the various subsystems need to be improved regularly, and their issues need to be solved. A way to achieve this is to make use of the extensive amount of information available today. These data can give a better picture and propose ways for enhancements and solutions to problems. The huge amounts of data are dealt with using data mining techniques. This paper surveys the clustering technique, one of the data mining techniques, used in designing and optimizing the electrical machines used for EVs. It presents the various clustering methods used to classify the different operating points of the torque speed curve and summarizes them into just a few points to be considered in the optimization process. It then illustrates a case study, developing a general design process with the aid of various clustering techniques and comparing their performance. The case study is applied to a six-phase surface-mounted permanent magnet (SPM) synchronous machine used for an electric vehicle’s propulsion and integrated onboard battery charger (IOBC) system. It suggests clustering techniques as an efficient means to achieve an optimal motor design over the entire driving cycle while considering only a few operating points to reduce time consumption and computational burdens, while getting acceptable results.

Permanent magnet (PM) machines with fractional slot concentrated windings (FSCW) constitute a notably remarkable proposition for electric vehicles. Additionally, an integrated onboard battery charger (IOBC) provides another superiority as it exploits the components of the powertrain to charge the battery without any additional components. Interior permanent magnet (IPM) motor arises as a credible choice due to its high torque density, resulting from the high saliency ratio. The optimal design of an IPM motor has been extensively presented from different perspectives, but the optimal design of a motor employed for IOBC application for both propulsion and charging modes has not been studied extensively. In this paper, the design and optimization of a 12-slot/10-pole IPM motor with IOBC are studied under both propulsion and charging modes. A finite-element-based optimization with the aid of a genetic algorithm technique is proposed to obtain the optimal machine by maximizing the average torque and minimizing the torque ripple, core losses, and magnet size.

Permanent magnet (PM) machines equipped with fractional slot concentrated windings (FSCW) offer a compelling solution for electric vehicles (EVs), boasting high torque and power density, high efficiency, a wide operational range, and several other advantageous features. However, faults can impair the magnets’ performance, leading to significant issues that negatively affect the EVs’ performance and worsen motor reliability. Although fault tolerance can be maintained through innovative control schemes to meet specific performance criteria, these solutions often introduce side issues, such as torque ripple. While extensive studies have focused on mitigating these side issues through control techniques, incorporating solutions at the design stage remains underexplored. This paper presents the design and optimization of a 12-slot / 10-pole permanent magnet synchronous motor (PMSM) aimed at achieving high-quality operation in both healthy conditions and post fault operation under an open-phase fault. A finite element-based multi-objective optimization using a genetic algorithm is proposed to optimize the machine by maximizing average torque and minimizing torque ripple during both healthy operation and in the case of an open phase fault. Embarking on an exploration of diverse rotor topologies and their implications, this study engages in comprehensive theoretical and simulation analyses. The research initiative involves the design and simulation of a 15-kW Interior Permanent Magnet (IPM) motor, crafted to emulate the characteristics of a practical light-duty Electric Vehicle (EV). To validate the conceptual framework, empirical testing is conducted using a 2-kW laboratory-scale Surface-Mounted Permanent Magnet (SPM) motor.

Wound rotor synchronous motors (WRSMs) are emerging as a compelling alternative to permanent magnet (PM) motors in the context of electric vehicles, offering magnet-less operation and cost-effectiveness. Various techniques exist to excite the field system of synchronous motors, each presenting drawbacks. This paper investigates a self-excitation technique based on a multi-phase stator design that autonomously energizes the field winding, eliminating the need for external excitation systems or rotating transformers, thus reducing motor weight and size. The multi-phase stator generates two distinct magnetomotive force (MMF) components: a dominant flux component responsible for torque production and a harmonic component crucial for field winding excitation. Independent control of both flux components is achieved by regulating the decoupled αβ and xy sequence current components, respectively. The paper presents the mathematical modeling and steady-state equivalent circuit for this topology. Extensive testing determines the motor parameters, analyzes the relationship between xy current and field current, and examines the effects of mechanical loading and speed variations on the field current. An improved Field Oriented Control (FOC) strategy is proposed to compensate for the effects of mechanical loading due to cross-coupling between the two subspaces caused by various nonidealities. An experimental setup employing a five-phase stator is developed to validate the proposed control methodology.