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The reliability analysis of traction inverters is of great interest due to the use of new semi-conductor devices and inverter topologies in electric vehicles (EVs). Switching devices in the inverter are the most vulnerable component due to the electrical, thermal and mechanical stresses based on various driving conditions. Accurate stress analysis of power module is imperative for development of compact high-performance inverter designs with enhanced reliability. Therefore, this paper presents an inverter reliability estimation approach using an enhanced power loss model developed considering dynamic and transient influence of power semi-conductors. The temperature variation tracking has been improved by incorporating power module component parameters in an LPTN model of the inverter. A 100 kW EV grade traction inverter is used to validate the developed mathematical models towards estimating the inverter performance and subsequently, predicting the remaining useful lifetime of the inverter against two commonly used drive cycles.

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq– frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq –frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed.

Multi–phase LLC resonant converters provide high power density, increased efficiency, good thermal distribution, and enables interleaving to drastically reduce the output current ripple. In addition, phase shedding capability at reduced output current levels allows for high efficiency over a wide operating range. However, these topologies are susceptible to component tolerances that create significant current imbalances between the phases. This paper presents a thorough evaluation of the star– connected transformer with floating star point, switched controlled capacitor and common capacitor LLC converter topologies and control techniques to mitigate the load–sharing issues instigated by the component mismatches while examining their scalability, interleaving, and phase shedding capability. The phase–shedding capability of each topology is evaluated using the first harmonic approximation (FHA) model to derive the gain characteristics of the configurations at any number of active phases. In addition, the current–sharing performance of each configuration is analyzed by modeling the multi–phase LLC resonant converter topologies with their respective phase– balancing control methodologies.

Wide band-gap (WBG) devices operating at higher switching frequencies have become attractive for EV traction inverter application in the recent years as they can be used to reduce harmonic distortion and switching loss. On the other hand, 800 V EV battery drivetrain systems have been recently deployed in commercially available in premium electric vehicles in order to achieve higher power for propulsion and faster charging. Given this scenario, this paper puts an effort to investigate and compare a 2-level inverter, a 3-level hybrid inverter and a nine-switch inverter for such an application. Motivation for investigating each of the inverter types is provided in the respective sections. This paper utilizes a novel hybrid modelling approach to get deeper insights into the power loss of inverter with device non-linearity and the harmonics, torque ripple and magnetic saturation in the motor due to different inverter types and their outputs. Cosimulation has been performed on each of the inverter types driving the same 100 kW surface-mounted permanent magnet motor on a WLTC drive cycle and results are discussed.

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this paper, a baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM) designed for a direct-drive BEV utilizing the conventional machine design approach has been developed. A vehicle dynamics model for the baseline machine and its associated vehicle parameters are used against an urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, and energy. Energy Center of Gravity (ECG) and K-means clustering are two existing methods for reducing the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle are implemented, which offer high computational efficiency and low computational time cost while optimizing an electric machine. Understanding the merits and demerits of the two existing methods, a novel hybrid approach of drive-cycle data representation is proposed. The drive-cycle data elicited from all of the approaches are thereafter used towards optimization of FSCW SPMSMs. Finally, a comparative performance analysis of optimally designed SPMSMs using the two existing approaches and the proposed approach is conducted.