arXiv subjects

Frank Willems

Publications and source records attributed to Frank Willems.

5 recordsLinked to original sources

arXiv2026

An Optimal Power Management Policy for Hydrogen-based Hybrid Aero Engines

This paper presents a power management policy for a hydrogen-based hybrid aero engine combining a gas turbine and a solid oxide fuel cell (SOFC). Specifically, we first identify a quadratic quasi-steady-state model of the propulsion system and formulate the minimum-fuel optimal control problem as a function of the power split between gas turbine and SOFC that captures the interconnections between the components and accounts for their operational limits. Second, leveraging the Karush-Kuhn-Tucker optimality conditions and partial convexity and monotonicity model properties, we compute the globally optimal steady-state power split for the different phases of the flight in closed form. Finally, we verify this power management policy with a high-fidelity integrated static model %simulator across different flight phases, revealing in less than 1.5 % normalized root mean square error in power allocation and less than 0.7 % in predicted fuel consumption. Our results show that the optimal power management policy can be translated into a heuristic control law requesting the highest SOFC power that does not exceed its maximum operating temperature, ultimately paving the way for minimal-effort on-board implementations.

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arXiv2026

Optimization Models and Steady-State Minimum-Fuel Operating Strategies for Hydrogen-based Hybrid Electric Aerospace Propulsion Systems

This paper presents an optimization framework for the operation of hydrogen-based hybrid electric aerospace propulsion systems consisting of a hydrogen gas turbine and an electric motor powered by a solid oxide fuel cell, connected to the gas turbine via multiple gas channels and heat exchangers. Our framework computes the minimum-fuel optimal operating strategies over a flight mission accounting for the complex propulsion system with strong thermodynamic and mechanical coupling between components. First, we identify surrogate optimization models of the components employing high-fidelity model simulations. Second, we frame the minimum-fuel optimal control problem over a given flight mission and parse it into a static nonlinear optimization problem that can be efficiently solved with off-the-shelf nonlinear programming algorithms. Finally, we apply our optimization framework to a typical flight mission of an advanced, commuter aircraft (Beechcraft 1900D market segment), considering a parallel propulsion system architecture with four different configurations that share a common baseline but differ in the inclusion of an additional battery and by-pass valves around the two heat exchangers. The resulting optimal trajectories are validated against high-fidelity simulation results, demonstrating the accuracy of our framework. Results show that adding by-pass valves around the air and hydrogen heat exchangers can reduce fuel consumption by 19.11 % without the battery, and by 19.56% with the battery. We show that adding a battery yields a slight increase in fuel consumption (below 1%) for future projected energy densities under steady-state conditions. Conversely, when considering state-of-the art energy densities, the additional battery weight outweighs the benefits, limiting its potential applicability to only assisting transients, which are not considered in the present work.

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arXiv2025

Automated and Risk-Aware Engine Control Calibration Using Constrained Bayesian Optimization

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.

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arXiv2024

HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort

Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.

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arXiv2024

Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: A RCCI Engine Application

Cylinder pressure-based control is a key enabler for advanced pre-mixed combustion concepts. Besides guaranteeing robust and safe operation, it allows for cylinder pressure and heat release shaping. This requires fast control-oriented combustion models. Over the years, mean-value models have been proposed that can predict combustion measures (e.g., Gross Indicated Mean Effective Pressure, or the crank angle where 50% of the total heat is released) or models that predict the full in-cylinder pressure. However, these models are not able to capture cyclic variations. This is important in the control design for combustion concepts, like Reactivity Controlled Compression Ignition, that can suffer from large cyclic variations. In this study, the in-cylinder pressure and cyclic variation are modelled using a data-based approach. The model combines Principle Component Decomposition and Gaussian Process Regression. A detailed study is performed on the effects of the different hyperparameters and kernel choices. The approach is applicable to any combustion concept, but most valuable for advance combustion concepts with large cyclic variation. The potential of the proposed approach is demonstrated for an Reactivity Controlled Compression Ignition engine running on Diesel and E85. The prediction quality of the evaluated combustion measures has an overall accuracy of 13.5% and 65.5% in mean behaviour and standard deviation, respectively. The peak-pressure rise-rate is traditionally hard to predict, in the proposed model it has an accuracy of 22.7% and 96.4% in mean behaviour and standard deviation, respectively. This Principle Component Decomposition-based approach is an important step towards in-cylinder pressure shaping. The use of Gaussian Process Regression provides important information on cyclic variation and provides next-cycle controls information on safety and performance criteria.

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