DSP-Enhanced Spectral-Domain Control and Transient Analysis of a Multi-Mode Dual-Cavity LLC Resonant Converter
This paper presents a multi-mode dual-cavity LLC resonant converter whose entire control and monitoring backbone is implemented in firmware on a Digital Signal Processor (DSP). A sliding discrete Fourier-transform impedance observer continuously extracts resonant-tank parameters and load condition, while a model-predictive supervisor adaptively selects among four hardware modes — full-bridge or half-bridge, single- or dual-cavity — and jointly optimizes pulse-frequency and pulse-width modulation to track the true resonant point. The same spectral-domain framework performs real-time noise characterization of switching nodes, enabling on-the-fly shaping that suppresses conducted Electromagnetic Interference (EMI). Tests on a 400[Formula: see text]W prototype demonstrate that the converter maintains over 96% efficiency across a 20–200[Formula: see text]V input range and reconfigures between modes in fewer than five switching cycles. Relative to conventional fixed-frequency or analog control, the proposed DSP architecture lifts steady-state efficiency by 2.8%, trims output-voltage overshoot by 47%, and reduces conducted EMI by up to 6[Formula: see text]dB[Formula: see text][Formula: see text]V. Embedding spectral-domain estimation and control in firmware therefore unlocks higher flexibility, power density, and electromagnetic compatibility, making the converter attractive for renewable-energy systems, electric vehicles, and distributed energy storage.
Authors
- Yong Zheng (ORCID: https://orcid.org/0000-0001-6251-3248)
- Yangmin Li (ORCID: https://orcid.org/0000-0002-4448-3310)
- Kejian Cao
- Huipin Lin (ORCID: https://orcid.org/0000-0001-9854-4142)
- Sheng Chen
- Xin Zhang (ORCID: https://orcid.org/0009-0003-3706-9044)
Institutions
- Wenzhou University (CN)
- National Computer Network Emergency Response Technical Team/Coordination Center of Chinar (CN)
- Institute of Microelectronics (CN)
- Hangzhou Dianzi University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- International Journal of Pattern Recognition and Artificial Intelligence
- Published
- 2025-08-13
- DOI
- https://doi.org/10.1142/s0218001425540151
- Primary Topic
- Advanced DC-DC Converters
- Type
- article
- Field-Weighted Citation Impact
- 0.00