An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space.

Zweitveröffentlichung, ursprünglich veröffentlicht: Jonas Holley: Stress-Constrained Topology Optimization with Application to the Design of Electrical Machines. München: Verlag Dr. Hut, 2023, 199 Seiten, Dissertation Humboldt-Universität Berlin (2023). ISBN 978-3-8439-5378-8 / Während des Designprozesses physischer Gegenstände stellt die mechanische Stabilität in nahezu jedem Anwendungsbereich eine essentielle Anforderung dar. Stabilität kann mittels geeigneter Kriterien, die auf dem mechanischen Spannungstensor basieren, mathematisch quantifiziert werden. Dies dient dem Ziel der Vermeidung von Schädigung in jedem Punkt innerhalb des Gegenstands. Die vorliegende Arbeit behandelt die Entwicklung einer Methode zur Lösung von Designoptimierungsproblemen mit punktweisen Spannungsrestriktionen. Zunächst wird eine Regularisierung des Optimierungsproblems eingeführt, die einen zentralen Baustein für den Erfolg einer Lösungsmethode darstellt. Nach der Analyse des Problems hinsichtlich der Existenz von Lösungen wird ein Gradientenabstiegsverfahren basierend auf einer impliziten Designdarstellung und dem Konzept des topologischen Gradienten entwickelt. Da der entwickelte Ansatz eine Methode im Funktionenraum darstellt, ist die numerische Realisierung ein entscheidender Schritt in Richtung der praktischen Anwendung. Die Diskretisierung der Zustandsgleichung und der adjungierten Gleichung bildet die Basis für eine endlich-dimensionale Version des Optimierungsverfahrens. Im letzten Teil der Arbeit werden numerische Experimente durchgeführt, um die Leistungsfähigkeit des entwickelten Algorithmus zu bewerten. Zunächst wird das Problem des minimalen Volumens unter punktweisen Spannungsrestriktionen anhand der L-Balken Geometrie untersucht. Ein Schwerpunkt wird hierbei auf die Untersuchung der Regularisierung gelegt. Danach wird das multiphysikalische Design einer elektrischen Maschine adressiert. Zusätzlich zu den punktweisen Restriktionen an die mechanischen Spannungen wird die Maximierung des mittleren Drehmoments berücksichtigt, um das elektromagnetische Verhalten der Maschine zu optimieren. Der Erfolg der numerischen Tests demonstriert das Potential der entwickelten Methode in der Behandlung realistischer industrieller Problemstellungen. / In the process of designing a physical object, the mechanical stability is an essential requirement in nearly every area of application. Stability can be quantified mathematically by suitable criteria based on the stress tensor, aiming at the prevention of damage in each point within the physical object. This thesis deals with the development of a framework for the solution of optimal design problems with pointwise stress constraints. First, a regularization of the optimal design problem is introduced. This perturbation of the original problem represents a central element for the success of a solution method. After analyzing the perturbed problem with respect to the existence of solutions, a line search type gradient descent scheme is developed based on an implicit design representation via a level set function. The core of the optimization method is provided by the topological gradient, which quantifies the effect of an infinitesimal small topological perturbation of a given design on an objective functional. Since the developed approach is a method in function space, the numerical realization is a crucial step towards its practical application. The discretization of the state and adjoint equation provide the basis for developing a finite-dimensional version of the optimization scheme. In the last part of the thesis, numerical experiments are conducted in order to assess the performance of the developed algorithm. First, the stress-constrained minimum volume problem for the L-Beam geometry is addressed. An emphasis is put on examining the effect of the proposed regularization. Afterwards, the multiphysical design of an electrical machine is addressed. In addition to the pointwise constraints on the mechanical stress, the maximization of the mean torque is considered in order to improve the electromagnetic performance of the machine. The success of the numerical tests demonstrate the potential of the developed design method in dealing with real industrial problems.

Optimal control problems for finite-strain elasticity are considered. An inner pressure or an inner fiber tension is acting as a driving force. Such internal forces are typical, for instance, for the motion of heliotropic plants, and for muscle tissue. Non-standard objective functions relevant for elasticity problems are introduced. Optimality conditions are derived on a formal basis, and a limited-memory quasi-Newton algorithm for their solution is formulated in function space. Numerical experiments confirm the expected mesh-independent performance.

Optimal control problems for finite-strain elasticity are considered. An inner pressure or an inner fiber tension is acting as a driving force. Such internal forces are typical, for instance, for the motion of heliotropic plants, and for muscle tissue. Non-standard objective functions relevant for elasticity problems are introduced. Optimality conditions are derived on a formal basis, and a limited-memory quasi-Newton algorithm for their solution is formulated in function space. Numerical experiments confirm the expected mesh-independent performance.

This thesis addresses optimal control problems with elasticity for large deformations. A hyperelastic model with a polyconvex energy density is employed to describe the elastic behavior of a body. The two approaches to derive the nonlinear partial differential equation, a balance of forces and an energy minimization, are compared. Besides the conventional volume and boundary loads, two novel internal loads are presented. Furthermore, curvilinear coordinates and a hierarchical plate model can be incorporated into the formulation of the elastic forward problem. The forward problem can be solved with Newton\\\'s method, though a globalization technique should be used to avoid divergence of Newton\\\'s method. The repeated solution of the Newton system is done by a CG or MinRes method with a multigrid V-cycle as a preconditioner. The optimal control problem consists of the displacement (as the state) and a load (as the control). Besides the standard tracking-type objective, alternative objective functionals are presented for problems where a reasonable desired state cannot be provided. Two methods are proposed to solve the optimal control problem: an all-at-once approach by a Lagrange-Newton method and a reduced formulation by a quasi-Newton method with an inverse limited-memory BFGS update. The algorithms for the solution of the forward problem and the optimal control problem are implemented in the finite-element software FEniCS, with the geometrical multigrid extension FMG. Numerical experiments are performed to demonstrate the mesh independence of the algorithms and both optimization methods.