Date of Award
Doctor of Philosophy (PhD)
Electrical, Computer & Energy Engineering
Robert W. Erickson
In an electric vehicle powertrain, a boost dc-dc converter enables size reduction of the electric machine and optimization of the battery system. Design of the powertrain boost converter is challenging because the converter must be rated at high peak power, while efficiency at medium to light load is critical for the vehicle system performance. The previously proposed efficiency improvement approaches only offer limited improvements in size, cost and efficiency trade-offs.
In this work, the concept of composite converter architectures is proposed. By emphasizing the direct / indirect power path explicitly, this approach addresses all dominant loss mechanisms, resulting in fundamental efficiency improvements over wide ranges of operating conditions. The key component of composite converter approach, the DC Transformer (DCX) converter, is extensively discussed in this work, and important improvements are proposed. It enhances the DCX efficiency over full power range, and more than ten times loss reduction is achieved at the no load condition.
Several composite converter prototypes are presented, ranging from 10 kW to 60 kW rated power. They validate the concept of composite converter, as well as demonstrate the scalability of this approach. With peak efficiency of 98.5% to 98.7% recorded, the prototypes show superior efficiency over a wide operation range. Comparing with the conventional approach, it is found that the composite converter results in a decrease in the total loss by a factor of two to four for typical drive cycles. Furthermore, the total system capacitor power rating and energy rating are substantially reduced, which implies potentials for significant reductions in system size and cost.
A novel control algorithm is proposed in this work as well, which proves the controllability of the composite converter approach.
Chen, Hua, "Advanced Electrified Automotive Powertrain with Composite DC-DC Converter" (2016). Electrical, Computer & Energy Engineering Graduate Theses & Dissertations. 127.