Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Quantum Information Processing in Optical Lattices and Magnetic Microtraps

We review our experiments on quantum information processing with neutral atoms in optical lattices and magnetic microtraps. Atoms in an optical lattice in the Mott insulator regime serve as a large qubit register. A spin-dependent lattice is used to split and delocalize the atomic wave functions in a controlled and coherent way over a defined number of lattice sites. This is used to experimentally demonstrate a massively parallel quantum gate array, which allows the creation of a highly entangled many-body cluster state through coherent collisions between atoms on neighbouring lattice sites. In magnetic microtraps on an atom chip, we demonstrate coherent manipulation of atomic qubit states and measure coherence lifetimes exceeding one second at micron-distance from the chip surface. We show that microwave near-fields on the chip can be used to create state-dependent potentials for the implementation of a quantum controlled phase gate with these robust qubit states. For single atom detection and preparation, we have developed high finesse fiber Fabry-Perot cavities and integrated them on the atom chip. We present an experiment in which we detected a very small number of cold atoms magnetically trapped in the cavity using the atom chip

Generalized harmonic modeling technique for 2D electromagnetic problems : applied to the design of a direct-drive active suspension system

The introduction of permanent magnets has significantly improved the performance and efficiency of advanced actuation systems. The demand for these systems in the industry is increasing and the specifications are becoming more challenging. Accurate and fast modeling of the electromagnetic phenomena is therefore required during the design stage to allow for multi-objective optimization of various topologies. This thesis presents a generalized technique to design and analyze 2D electromagnetic problems based on harmonic modeling. Therefore, the prior art is extended and unified to create a methodology which can be applied to almost any problem in the Cartesian, polar and axisymmetric coordinate system. This generalization allows for the automatic solving of complicated boundary value problems within a very short computation time. This method can be applied to a broad class of classical machines, however, more advanced and complex electromagnetic actuation systems can be designed or analyzed as well. The newly developed framework, based on the generalized harmonic modeling technique, is extensively demonstrated on slotted tubular permanent magnet actuators. As such, numerous tubular topologies, magnetization and winding configurations are analyzed. Additionally, force profiles, emf waveforms and synchronous inductances are accurately predicted. The results are within approximately 5 % of the non-linear finite element analysis including the slotted stator effects. A unique passive damping solution is integrated within the tubular permanent magnet actuator using eddy current damping. This is achieved by inserting conductive rings in the stator slot openings to provide a passive damping force without compromising the tubular actuator’s performance. This novel idea of integrating conductive rings is secured in a patent. A method to calculate the damping ratio due to these conductive rings is presented where the position, velocity and temperature dependencies are shown. The developed framework is applied to the design and optimization of a directdrive electromagnetic active suspension system for passenger cars. This innovative solution is an alternative for currently applied active hydraulic or pneumatic suspension systems for improvement of the comfort and handling of a vehicle. The electromagnetic system provides an improved bandwidth which is typically 20 times higher together with a power consumption which is approximately five times lower. As such, the proposed system eliminates two of the major drawbacks that prevented the widespread commercial breakthrough of active suspension systems. The direct-drive electromagnetic suspension system is composed of a coil spring in parallel with a tubular permanent magnet actuator with integrated eddy current damping. The coil spring supports the sprung mass while the tubular actuator either consumes, by applying direct-drive vertical forces, or regenerates energy. The applied tubular actuator is designed using a non-linear constrained optimization algorithm in combination with the developed analytical framework. This ensured the design with the highest force density together with low power consumption. In case of a power breakdown, the integrated eddy current damping in the slot openings of this tubular actuator, together with the passive coil spring, creates a passive suspension system to guarantee fail-safe operation. To validate the performance of the novel proof-of-concept electromagnetic suspension system, a prototype is constructed and a full-scale quarter car test setup is developed which mimics the vehicle corner of a BMW 530i. Consequently, controllers are designed for the active suspension strut for improvement of either comfort or handling. Finally, the suspension system is installed as a front suspension in a BMW 530i test vehicle. Both the extensive experimental laboratory and on-road tests prove the capability of the novel direct-drive electromagnetic active suspension system. Furthermore, it demonstrates the applicability of the developed modeling technique for design and optimization of electromagnetic actuators and devices

Demand-driven data acquisition for large scale fleets

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Experimental Characterization and Quasi-Dimensional Modeling of Cyclic Combustion Variations in Spark Ignition Engines

Variances in spark ignition (SI) engine parameter settings are still expanding, thus the need for engine calibration is further increasing and virtual engine calibration to optimize engine parameters is being more frequently considered. In particular, the focus of engine calibration is to maximize fuel efficiency and power output while also reducing exhaust emissions up to the engine smoothness limit. This limit is determined by high cycle-to-cycle variations (CCV) and can be detected from indicated mean effective pressure (IMEP) fluctuations from one engine cycle to the other. These CCV dictate the stability of the combustion process and engine vibration; thus, the aim is to limit these variations to a certain comfort level. The objective of this thesis is to set up a zero-dimensional (0D) physical cyclic combustion variations model which can predictively describe CCV. In this work, first, extensive measurement data are produced by investigating five SI engines with different underlying combustion processes. These include both conventional engines and unconventional engines with a long expansion stroke via the crank and valve trains. Engine parameters are varied, in order to experimentally characterize CCV regarding the influence from the fluid mechanics, the chemical gas composition and the thermodynamical state. The CCV model itself is set up based on recently developed 0D models for turbulence, ignition and combustion; these are initially calibrated by means of 3D CFD data and measurement data. In the model development process, first, a stochastic model is developed to offer the possibility to impose fluctuations. From research into the literature, the physical causes of CCV are extracted. In particular, the new CCV model considers results from 3D CFD Large Eddy Simulations regarding the influence of global and local in-cylinder flow fluctuations, the most significant causes of CCV. For the first time within the 0D/1D simulation environment, flow fluctuations can be taken into account thanks to their availability in the 0D turbulence model used. In the first instance, it is shown that the new CCV model is able to reproduce experimentally observed CCV qualitatively. In order to also describe cyclic combustion variations quantitatively, beside the fluctuations due to these physical causes, factors influencing CCV, i.e. several engine parameters are introduced in the new CCV model. After the development process, the new CCV model is first verified with the design engine by means of experimental data and another commercial CCV model, considered state of the art. It is shown that the new CCV model is able to reproduce not only the fluctuations in the IMEP, but also the underlying fluctuations in the combustion process. Then, with no further calibration, the newly designed CCV model is successfully validated by means of the other engines and engine parameter variations investigated. Furthermore, it is explicitly shown that the new model, in contrast to the state-of-the-art model, is able to accurately describe CCV at two engine operating points with the same engine speed and load, but different internal residual gas rates and in-cylinder turbulence levels. Summing up, the new CCV model offers more encouraging results, enabling it to be used for virtual engine calibration even for cases in which no actual test engine is available