High power density is one of the requirements for traction drive inverters for meeting increasing demand for higher power and performance electrical vehicles (EV). This paper presents design and preliminary experimental results for a 100 kW high-power density inverter for EV traction drive applications. The inverter design was based on the segmented inverter topology that can significantly reduce the inverter DC filter capacitor and employs low-profile planar double-side-cooled SiC MOSFET-based power modules, compact mini-channel heat sinks with fin-profile optimized using genetic-algorithms, and high-ripple current capacitors. The design produced a compact inverter package with a total volume less than 1 litter, exceeding the power density goal of 100 kW/L. Preliminary experimental results are included to demonstrate the cooling and electrical performance.

Power semiconductor die layout in substrates used in power modules is generally optimized for minimum electrical parasitics (e.g., stray inductance) by considering the minimum spacing between dies for thermal decoupling. The layout assumes sufficient heat spreading and transfer from dies to the cooling structure. For module designs using a direct substrate cooling method, the base plate is removed, leading to a steady-state thermal asymmetry in the power module due to insufficient heat spreading/transfer. This causes significant temperature differences among the devices. Such unintentional thermal asymmetries can lead to undesirable asymmetries in power conversion among semiconductor devices, which impact reliability. This article proposes a thermal imbalance mitigation method that uses evolutionary optimized liquid-cooled heat sinks to improve the thermal loading among devices.

Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This study proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis–based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry was compared with state-of-the-art solutions in the literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform conventional heat sink geometries. Optimized heat sink design from the proposed method was fabricated and tested in an experimental setup under representative operating conditions. The experimental setup was also modeled in the finite element model that was used for the proposed heat sink optimization method. The experimental results show that developed finite element models can predict the thermal and flow performance of the complex design with high fidelity, and the results validate the proposed design approach.

The transition to electric road transport technologies requires electric traction drive systems to offer improved performances and capabilities, such as fuel efficiency (in terms of MPGe, i.e., miles per gallon of gasoline-equivalent), extended range, and fast-charging options. The enhanced electrification and transformed mobility are translating to a demand for higher power and more efficient electric traction drive systems that lead to better fuel economy for a given battery charge. To accelerate the mass-market adoption of electrified transportation, the U.S. Department of Energy (DOE), in collaboration with the automotive industry, has announced the technical targets for light-duty electric vehicles (EVs) for 2025. This article discusses the electric drive technology trends for passenger electric and hybrid EVs with commercially available solutions in terms of materials, electric machine and inverter designs, maximum speed, component cooling, power density, and performance. The emerging materials and technologies for power electronics and electric motors are presented, identifying the challenges and opportunities for even more aggressive designs to meet the need for next-generation EVs. Some innovative drive and motor designs with the potential to meet the DOE 2025 targets are also discussed.

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

In this article, a new analytical method is proposed to estimate eddy currents inside linear conductive materials. The novel closed-form formulation takes into account the effects of both conductor reaction field that dominates at high frequencies as well as the spatially non-homogeneous nature of the magnetic field penetrating the material. Although the model is suitable for most kinds of permanent magnet machines, it is particularly useful for Surface-mounted Permanent Magnet (SPM) machines where both of these phenomena are prominent. The numerical implementation of the proposed model, which consists of a combination of the method of images and 4-D Fourier transform, is presented. The model is then validated against 2-D and 3-D finite element models (FEMs) for a simple magnetic circuit, showing good agreement. Finally, the eddy current magnet loss in an SPM synchronous machine is evaluated and results are discussed.

In this paper, analysis and optimization of a multi-layer organic substrate for high current GaN HEMT based power module are discussed. The organic multi-layer substrates can provide high electrical performance in terms of low parasitic inductance in the power loop by providing vertical layout, and shielding for reduction of common-mode noise, a common problem in fast switching power converters. Furthermore, high performance cooling solutions, such as micro-channel heat sinks, can be directly bonded to the substrate for optimum thermal management. The structure of the proposed architecture, thermal analysis and optimization of layer thickness, thermo-mechanical stress analysis of the GaN HEMT and development of a high-performance heat sink are discussed.

Optimal heat dissipation in power modules can significantly increase their power density. Removing the generated heat is critical for capturing the benefits of advanced semiconductor materials and improving the reliability of the device operation. This article proposes a design optimization method for liquid-cooled heat sinks that use a Fourier analysis-based tool and an evolutionary optimization algorithm to optimize the heat sink geometry for specified objectives. The optimized heat sink geometry is then compared with state-of-the-art solutions in literature based on finite element analysis of different designs. The proposed methodology can develop complex geometries that outperform the conventional heat sink geometries.

This work sets forth an approach whereby a system designer can rapidly estimate the size versus loss characteristics of a class of electric machinery, and the impact of this tradeoff at the system level. In essence, the methodology provides the first physics-based multiobjective scaling law for a Pareto-optimal front capturing the tradeoff between size and loss as a function of speed and requirements (output power or torque). It is the first time such a representation has been achieved. This is accomplished through the metamodeling of a normalized optimization based machine design framework. The method is applied to a permanent magnet ac machine.

Modular Multilevel Converters (MMC) are being widely considered for shipboard applications. Designing such converters through multi-objective optimization is of interest, because such an approach allows the trade-off between competing objectives (for example, mass and loss) to be explicitly and quantitatively identified. However, doing so can require 10 4 -10 6 time domain simulations in order to rigorously explore the design space. In this work, a computationally efficient simulation is developed to estimate the MMC waveforms. While the existing literature sets forth average-value models for transient studies of MMCs, fast waveform-level models are still needed. This work presents a suitable approach which is validated against a detailed simulation model.