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Cited by 208

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Bi-directional DC to DC converters for fuel cell systems

Wang, K., Lin, C.Y., Zhu, L., Qu, D., Lee, F.C., Lai, J.S.·2002·Power Electronics in Transportation (Cat. No.98TH8349)

Abstract

This paper first describes the need of a bi-directional DC to DC converter for a fuel cell system. Various combinations of current-fed and voltage-fed converters are explored for the application of different voltage levels. With a preliminary study, putting current-fed on low-voltage side and voltage fed on high voltage side indicated higher efficiency than the other way around. Two low-side circuit topologies were then selected for hardware implementation. One is the L-type half-bridge current-fed converter, and the other is full-bridge current-fed converter. The high-side circuit topology is fixed with a full-bridge voltage-fed converter. Two systems were built and tested to full power. The results indicate that the combination with the full-bridge converter on the low-voltage side is more efficient than the combination with the L-type half-bridge converter on the low-voltage side for both charging and discharging modes.

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Crossref · 10.1109/pet.1998.731056

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Metadata

Authors
Wang, K., Lin, C. Y., Zhu, L., Qu, D., Lee, F. C., & Lai, J. S.
Venue
Power Electronics in Transportation (Cat. No.98TH8349)
Publisher
IEEE
Published
2002-11-27
Pages
47-51
OpenAlex
W2098556032
Cited by
208
References
0
FWCI
11.04
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bi-directional DC to DC converters for fuel cell systems. https://app.cochat.ai/papers/10.1109/pet.1998.731056
The paper
Wang, K., Lin, C. Y., Zhu, L., Qu, D., Lee, F. C., & Lai, J. S. (2002). Bi-directional DC to DC converters for fuel cell systems. Power Electronics in Transportation (Cat. No.98TH8349). https://doi.org/10.1109/pet.1998.731056

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.