TY - GEN
T1 - Semiconductor Power Module Current Balancing Using Reinforcement Machine Learning
AU - Westmoreland, B.
AU - Bilbao, A. V.
AU - Bayne, S. B.
N1 - Funding Information:
The authors of this work would like to thank our sponsor. This work was supported by the U.S. Army Research Laboratory Power Integration and Architecture (PIA) branch. Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NFͲ20Ͳ2Ͳ0192. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In high power applications, semiconductor power modules containing paralleled MOSFETs are often used to achieve high output currents. The current distribution between devices within a module is influenced by several factors such as component layout, minor defects due to manufacturing tolerances, and general devices degradation that occurs over time. This paper describes a method of balancing the current between paralleled MOSFETs by independently modulating each device's gate-to-source voltage and measuring the corresponding drain-to-source currents. To achieve this, a detailed simulation is created using MATLAB and Simulink. A reinforcement learning agent is implemented with the goal of adaptively balancing power module current as the components inside degrade over time.
AB - In high power applications, semiconductor power modules containing paralleled MOSFETs are often used to achieve high output currents. The current distribution between devices within a module is influenced by several factors such as component layout, minor defects due to manufacturing tolerances, and general devices degradation that occurs over time. This paper describes a method of balancing the current between paralleled MOSFETs by independently modulating each device's gate-to-source voltage and measuring the corresponding drain-to-source currents. To achieve this, a detailed simulation is created using MATLAB and Simulink. A reinforcement learning agent is implemented with the goal of adaptively balancing power module current as the components inside degrade over time.
UR - http://www.scopus.com/inward/record.url?scp=85127262934&partnerID=8YFLogxK
U2 - 10.1109/PPC40517.2021.9733124
DO - 10.1109/PPC40517.2021.9733124
M3 - Conference contribution
AN - SCOPUS:85127262934
T3 - IEEE International Pulsed Power Conference
BT - 2021 IEEE Pulsed Power Conference, PPC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 December 2021 through 16 December 2021
ER -