Graduation Announcement

Last Monday, on the 29th of July 2019, Matthieu Amyotte successfully graduated from his MASc program in our lab. Matt’s great achievements include but are not limited to the following:

3 IEEE International Conference papers (one as first author)
1 IEEE Journal publication (submitted)
Major contributions to our UBC Sustaingineering Team
Key involvement in the research collaboration projects with our industry partner, Alpha Technologies Ltd., on WBG switch losses
A number of great contributions to our lab’s operations and logistics

Right after graduation, Matt successfully transitioned to the industry by taking a position at Corvus Energy, Richmond, BC.

We congratulate Matt for achieving this significant milestone in his career and wish him success in all future endeavours!

Abstract for Matt’s MASc Thesis

Title: “Improved Power Loss Estimation for Device-to-System-Level Analysis”

Abstract: Power converters are found nearly everywhere electric power is used and are ubiquitous in renewable energy generation and electric vehicles. Power converters transform electricity between alternating current (AC) and direct current (DC) electricity and change the voltage level (AC or DC). Modern power converters have very high efficiency, often reaching peak efficiency > 95%. However, the losses in these systems are still significant and must be considered for thermal and financial purposes. For example, a 1% efficiency improvement from 98 to 99% corresponds to a 50% reduction in losses. This would allow for a significant reduction, if not the complete elimination, of the thermal management system. To enable maximum loss reduction, a thorough understanding of the losses in power converters is necessary. In particular, accurate prediction of the losses at the design stage allows designers to create better power converters and energy systems with lower losses. Gallium Nitride (GaN) power switches are an emerging technology due to their high efficiency operation and smaller size compared to traditional Silicon (Si) devices.

To date, traditional topologies, such as boost and resonant converters, have been implemented with Gallium Nitride (GaN) devices, and simplistic power loss models have been employed for loss prediction and thermal management design. However, these simplistic models do not provide accurate loss prediction, resulting in over-design of the thermal management systems. Meanwhile, high accuracy power loss analysis tools for GaN devices are missing in the literature. With very small footprints and thermal capacity, accurate power loss prediction for GaN is mandatory.

This work proposes a comprehensive method to predict conduction and switching losses in GaN devices. Through the use of thermal measurement, the inaccuracy of traditional electrical measurements for power losses is eliminated and a higher accuracy model is achieved. The proposed model is verified experimentally against common traditional approaches. The proposed model provides a nearly four-fold reduction in loss prediction error across a variety of operating conditions. Ultimately, the model provides confidence in loss prediction, allowing power converter designers to effectively design thermal management systems for maximum power density and efficiency.

Having established accurate converter-level loss prediction, a higher level of abstraction is then considered. The rapid expansion of distributed energy resources has led to increasingly complex systems with numerous power converters. Given the pervasiveness of power converters in both large grids and microgrids, accurate converter loss prediction is essential for system-level financial and reliability evaluation. Existing system-level analysis focuses on distribution losses and oversimplifies converter losses by assuming fixed efficiency. In reality, converter losses are highly variable under different operating conditions. However, the multi-domain simulation tools employed for GaN loss prediction at the converter level are too slow to be applied to system-level analysis.

In this work, the Rapid Loss Estimation equation (RLEE) is proposed to provide computationally simple loss prediction under all operating conditions. First, the real operating conditions are determined for the intended application. Then, accurate loss information is extracted from detailed converter behavior in multi-domain simulations at select operating conditions. Finally, the RLEE is obtained: a parametric equation which is fast enough for system-level simulation while capturing the converter’s complexity at different operating conditions. Three different converters are considered: one for solar generation, one for electric vehicle charging stations, and one for battery storage. These converters are simulated in a DC microgrid to highlight the benefits of the proposed loss estimation tool.

Ultimately, the tools developed in this work provide improved loss estimation in power converters from the component level through to the system level. The proposed techniques, while explained through specific examples, are widely applicable and can be readily implemented to other devices, topologies, and systems. Improved loss estimation is valuable at all levels of abstraction, from designing thermal management systems for individual devices in a converter to optimizing the financial outcomes of a complex grid with multiple power converters.

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