Oliver Wallscheid

Acting professor

University of Paderborn

Germany

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Oliver Wallscheid is an Acting Professor at the University of Paderborn, Germany. His research focuses on electric drives, model predictive control, and reinforcement learning applied to power electronics and electric energy systems. He has contributed to several innovative projects, including techniques for improving efficiency and control in electric machines through advanced simulations and machine learning approaches.

Articles (15)

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2025

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2023

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2023

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2023

Collaborators (3)

Joachim Böcker

University of Paderborn

GERMANY

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Mario Porrmann

GERMANY

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Sebastian Peitz

Assistant Professor (Jun.-Prof.)

University of Paderborn

GERMANY

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Oliver Wallscheid

Research Interests

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Articles (15)

HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores

Evaluation of the Efficiency Measurement Uncertainty of Electric Drive Test Benches for Direct Data-Driven Control Optimization

Insights and Challenges of Co-Simulation-Based Optimal Pulse Pattern Evaluation for Electric Drives

Nonlinear Efficiency-Optimal Model Predictive Torque Control of Induction Machines

Reducing Contact Bouncing of a Relay by Optimizing the Switch Signal During Run-Time

Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent-Magnet Synchronous Motors

Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines

ElectricGrid.jl - A Julia-based modeling and simulation tool for power electronics-driven electric energy grids

Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes

Model Predictive Torque Control for Permanent- Magnet Synchronous Motors Using a Stator- Fixed Harmonic Flux Reference Generator in the Entire Modulation Range

Long-Term Memory Recursive Least Squares Online Identification of Highly Utilized Permanent Magnet Synchronous Motors for Finite-Control-Set Model Predictive Control

Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning

Finite-Set Direct Torque Control via Edge Computing-Assisted Safe Reinforcement Learning for a Permanent Magnet Synchronous Motor

Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends

Steady-State Error Compensation for Reinforcement Learning-Based Control of Power Electronic Systems

Collaborators (3)

Joachim Böcker

Mario Porrmann

Sebastian Peitz

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