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

Design and control of the energy management system of a smart vehicle

This thesis demonstrates the design of two high efficiency controllers, one non-predictive and the other predictive, that can be used in both parallel and power-split connected plug-in hybrid electric vehicles. Simulation models of three different commercially available vehicles are developed from measured data for necessary testing and comparisons of developed controllers. Results prove that developed controllers perform better than the existing controllers in terms of efficiency, fuel consumption, and emissions

Robust Transient Control of Reusable Liquid-Propellant Rocket Engines

International audienceThe current trend towards a more affordable access to space is generally materialising in reusable launchers and engines. From the control perspective, these reusable liquid-propellant rocket engines (LPRE) imply more demanding robustness requirements than expendable ones, mainly because of their multi-restart and thrust-modulation capabilities. Classically, the control system handles LPRE operation at a finite set of predefined points. That approach reduces their throttability domain to a restricted interval in which they are designed to be safe in nominal conditions. Moreover, the operation of their transient phases, which have a great impact on the duration of engine life, is not robust to the possible engine evolution. Hence, the goal of this work is to develop a control loop which is adapted to the whole set of operating phases, transient and steady-state, and which is robust to internal parametric variations. Several blocks have been assembled to constitute the control loop: engine simulation, reference generation and several controllers. First, simulators representative of the gas-generator-cycle (GG) Vulcain 1 and PROMETHEUS engines were built. The purely thermodynamic modelling of the cycle was subsequently adapted to the control framework, obtaining a nonlinear state-space model. The available actuators are continuously controllable valves, binary igniters and binary starters. These actuators are related to discrete events in transient phases. Regarding the start-up operation, the igniter, starter and valves are activated during the first seconds. Up from the end of those activations, the whole system behaves in a fully continuous way. Hence, a different control strategy is proposed for each sub-phase. For the first and discrete sub-phase, a discrete optimisation of events timing is proposed, in which the time differences between events are adapted according to operation criteria and constraints. This trajectory planning, still under implementation, is to be performed off-line. The subsequent continuous sub-phase is feedback controlled to track pre-computed reference trajectories. Apart from the start-up, throttling scenarios also present a dedicated end-state-tracking algorithm. A model-based control method, Model Predictive Control, has been applied in a linearised manner with robustness guarantees to all these scenarios, in which a set of hard state and control constraints must be respected. Tracking of pressure (thrust) and mixture-ratio operating points within the design envelope is achieved in simulation along the continuous sub-phase while respecting constraints. Robustness to variations of the parameters, which are checked to be predominant according to analyses, is also demonstrated

Investigation of Engine Coolant Loop Flow Modelling from a System Simulation Perspective

The engine cooling system in a vehicle ensures that the engine runs at its most efficient temperature under a variety of operating conditions. The system includes heat exchangers, a thermostat, pump, plumbing lines and a cooling water jacket. Each branch of the system and its different components need to receive adequate coolant flow. A system simulation (1D) model of the coolant loop is generated with components of the system individually characterized using geometry and/or performance data. Accurately modelling and capturing the flow behaviour of the coolant through the entire system, including the complex water jacket, poses a particular challenge. This thesis explores the use of experimental flow benches to support the research into converting a physical engine cooling system into a robust 1D system model. GT-SUITE software is used as the system simulation modelling platform, and its built-in application GEM3D is used to convert the 3D CAD geometry. A detailed investigation is performed by carefully splitting the plumbing and water jacket into multiple flow components. Non-dimensional pressure loss and Reynolds number are calculated based on pressure drop and flow rate data, for a wide range of temperatures including extreme cold conditions. Outcomes of this thesis include an in-depth and improved modelling process, well validated component and system level models, and an overall reduction in cost and time to achieve accurate results

Trajectory planning and tracking via MPC for transient control of liquid-propellant rocket engines

International audienceThe new scenarios associated with launchers reusability force the enhancement of control algorithms for their liquid-propellant rocket engines. The transient phases of these engines are generally executed in open loop. The goal of this paper is to improve the control performance and robustness throughout the fully continuous phase of the start-up transient of a generic gas-generator cycle. The controller has to guarantee an accurate tracking in terms of combustion-chamber pressure and chambers mixture ratios, as well as to satisfy a set of hard operational constraints. The selected strategy comprises a nonlinear preprocessor and a linearised MPC (Model-Predictive Control) controller, making use of nonlinear state-space models of the engine. The former plans the reference trajectory of states and control, which is tracked by the latter. Control goals are attained with sufficient accuracy while verifying constraints within the desired throttling range. Robustness to internal parameters variations is considered in the MPC controller by means of an epigraph formulation of the minimax robust optimisation problem, where a finite set of parameter-variation scenarios is treated

Design of a vertical test bench for hybrid sounding rocket characterization

Nell'ambito di un progetto di sviluppo di un razzo-sonda a propulsione ibrida a perossido di idrogeno/paraffina, questa tesi descrive dettagliatamente requisiti, design e dimensionamenti di due banchi da test verticali volti alla caratterizzazione di due parti cruciali del sistema propulsivo, ovvero la linea fluidica e il reattore catalitico

Design, Implementation and Validation of a Hardware-in-the-Loop Test Bench for Heating Systems in Conventional Coaches

Experimental work with heating systems installed in public transport vehicles, particularly for optimisation and control design, is a challenging task due to cost and space limitations, primarily imposed by the heating hardware and the need to have a real vehicle available. In this work, a hybrid hardware-in-the-loop (HIL) test bench for heating systems in conventional coaches is introduced. This approach consists of a hardware system made up of the main heating components, assembled as a lab experimental plant, along with a simulation component including a cabin thermal model, both exchanging real-time data using a standard communication protocol. This scheme presents great flexibility regarding data logging for further analysis and easily changing the experimental operational conditions and disturbances under different scenarios (i.e., solar irradiance, outside temperature, water temperature from the engine cooling circuit, number of passengers, etc.). Comparisons between the hybrid system’s transient and steady-state responses and those from selected experiments conducted on an actual coach allowed us to conclude that the proposed system is a suitable test bed to aid in optimisation and design tasks. In this context, several closed-loop test experiments using the test bench were additionally carried out to assess the performance of the proposed control system

14th International Conference on Turbochargers and Turbocharging

14th International Conference on Turbochargers and Turbocharging addresses current and novel turbocharging system choices and components with a renewed emphasis to address the challenges posed by emission regulations and market trends. The contributions focus on the development of air management solutions and waste heat recovery ideas to support thermal propulsion systems leading to high thermal efficiency and low exhaust emissions. These can be in the form of internal combustion engines or other propulsion technologies (eg. Fuel cell) in both direct drive and hybridised configuration. 14th International Conference on Turbochargers and Turbocharging also provides a particular focus on turbochargers, superchargers, waste heat recovery turbines and related air managements components in both electrical and mechanical forms

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller

Linear Mathematical Model for State-Space Representation of Small Scale Turbojet Engine with Variable Exhaust Nozzle

The goal of this article is to develop a linear mathematical model for a small scale turbojet engine with variable convergent nozzle, and validate it on existing laboratory hardware owned by the authors’ Departments.Control of gas turbine engines plays an essential role in the safety of aviation. Although its role is constantly expanding, ranging from pilot workload reduction to detailed diagnostics, the basic competence is to regulate the thrust output of the power plant with maximum available accuracy, rapidity, stability, and robustness. The linear quadratic control is one possible solution for the above mentioned criteria.Although civil aircraft engines include fixed exhaust nozzle geometry, in military applications the exhaust nozzle geometry is also adjustable to reach optimum efficiency due to better matching of individual engine components, etc.In the present article the authors deduce the members of state space governing equations to acquire the basis of the LQ control.The established model is based on the physical laws describing the operational behavior of the engine as well as its complexity should be reduced to an acceptable level where still enough details remain to reflect the nature of the controlled object