Online Dead-Time Control for Half Bridges Without Preliminary Training Based on Switching Transient Steepness

Half bridges operating at high switching frequencies allow power electronics to achieve higher energy densities. However, they also suffer from proportionally high switching losses. Soft-switching techniques can mitigate these losses, but achieving optimal switching dead times is crucial, as shorter or longer dead times lead to increased switching losses. This study proposes an online dead-time controller that utilizes in-situ measurements. To achieve this, the controller evaluates the steepness of the switch node voltage, leveraging the fact that the maximum steepness increases during hard switching. The proposed algorithm utilizes this dependency independently of system characteristics, thereby eliminating the need for preliminary training.

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Publication Details

Published
2025-03-16
DOI
https://doi.org/10.1109/apec48143.2025.10977545
Primary Topic
Hydraulic and Pneumatic Systems
Type
article
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article

Online Dead-Time Control for Half Bridges Without Preliminary Training Based on Switching Transient Steepness

Martin Pfost, Lukas Knappstein, Niklas Falkenberg
Hydraulic and Pneumatic Systems
article

Online Dead-Time Control for Half Bridges Without Preliminary Training Based on Switching Transient Steepness

Martin Pfost, Lukas Knappstein, Niklas Falkenberg
article en

Abstract

Half bridges operating at high switching frequencies allow power electronics to achieve higher energy densities. However, they also suffer from proportionally high switching losses. Soft-switching techniques can mitigate these losses, but achieving optimal switching dead times is crucial, as shorter or longer dead times lead to increased switching losses. This study proposes an online dead-time controller that utilizes in-situ measurements. To achieve this, the controller evaluates the steepness of the switch node voltage, leveraging the fact that the maximum steepness increases during hard switching. The proposed algorithm utilizes this dependency independently of system characteristics, thereby eliminating the need for preliminary training.

TU Dortmund University (DE)
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Hydraulic and Pneumatic Systems
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