Incorporating a Deep Neural Network into Moving Horizon Estimation for Embedded Thermal Torque Derating of an Electric Machine

Alexander Winkler, Pranav Shah, Katrin Baumgärtner, Vasu Sharma, David Gordon and Jakob Andert ()
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Alexander Winkler: Teaching and Research Area Mechatronics in Mobile Propulsion, RWTH Aachen University, Forckenbeckstr. 4, 52074 Aachen, Germany
Pranav Shah: Teaching and Research Area Mechatronics in Mobile Propulsion, RWTH Aachen University, Forckenbeckstr. 4, 52074 Aachen, Germany
Katrin Baumgärtner: IMTEK—Department of Microsystems, University of Freiburg, Georges-Köhler-Allee 103, 79108 Freiburg im Breisgau, Germany
Vasu Sharma: Teaching and Research Area Mechatronics in Mobile Propulsion, RWTH Aachen University, Forckenbeckstr. 4, 52074 Aachen, Germany
David Gordon: Donadeo Innovation Centre for Engineering, Department of Mechanical Engineering, University of Alberta, 10th Floor, Edmonton, AB T6G 1H9, Canada
Jakob Andert: Teaching and Research Area Mechatronics in Mobile Propulsion, RWTH Aachen University, Forckenbeckstr. 4, 52074 Aachen, Germany

Energies, 2025, vol. 18, issue 14, 1-20

Abstract: This study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-based models within MHE towards data-driven techniques. Specifically, a Long Short-Term Memory (LSTM)-based DNN is trained using synthetic data derived from a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), applied within a thermal derating torque control strategy for battery electric vehicles. The trained DNN is directly embedded within an MHE formulation, forming a discrete-time nonlinear optimal control problem (OCP) solved via the acados optimization framework. Model-in-the-Loop simulations demonstrate accurate temperature estimation even under noisy sensor conditions and simulated sensor failures. Real-time implementation on embedded hardware confirms practical feasibility, achieving computational performance exceeding real-time requirements threefold. By integrating the learned LSTM-based dynamics directly into MHE, this work achieves state estimation accuracy, robustness, and adaptability while reducing modeling efforts and complexity. Overall, the results highlight the effectiveness of combining model-based and data-driven methods in safety-critical automotive control systems.

Keywords: state estimation; deep learning; moving horizon estimation; nonlinear and optimal automotive control; neural networks; temperature control; electric machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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