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

Predictive Control of HCCI Engines Using Physical Models

Homogeneous Charge Compression Ignition (HCCI) is a promising internal combustion engine concept. It holds promise of combining low emission levels with high efficiency. However, as ignition timing in HCCI operation lacks direct actuation and is highly sensitive to operating conditions and disturbances, robust closed-loop control is necessary. To facilitate control design and allow for porting of both models and the resulting controllers between different engines, physics-based mathematical models of HCCI are of interest. This thesis presents work on a physical model of HCCI including cylinder wall temperature and evaluates predictive controllers based on linearizations of the model. The model was derived using first principles modeling and is given on a cycle-to-cycle basis. Measurement data including cylinder wall temperature measurements was used for calibration and validation of the model. A predictive controller for combined control of work output and combustion phasing was designed and evaluated in simulation. The resulting controller was validated on a real engine. The last part of the work was an experimental evaluation of predictive combustion phasing control. The control performance was evaluated in terms of response time and steady-state output variance

Adaptive Observer for Nonlinearly Parameterised Hammerstein System with Sensor Delay – Applied to Ship Emissions Reduction

Taking offspring in a problem of ship emission reduction by exhaust gas recirculation control for large diesel engines, an underlying generic estimation challenge is formulated as a problem of joint state and parameter estimation for a class of multiple-input single-output Hammerstein systems with first order dynamics, sensor delay and a bounded time-varying parameter in the nonlinear part. The paper suggests a novel scheme for this estimation problem that guarantees exponential convergence to an interval that depends on the sensitivity of the system. The system is allowed to be nonlinear parameterized and time dependent, which are characteristics of the industrial problem we study. The approach requires the input nonlinearity to be a sector nonlinearity in the time-varying parameter. Salient features of the approach include simplicity of design and implementation. The efficacy of the adaptive observer is shown on simulated cases, on tests with a large diesel engine on test bed and on tests with a container vessel

Development of a virtual methodology based on physical and data-driven models to optimize engine calibration

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved

Control Structures for Low-Emission Combustion in Multi-Cylinder Engines

Traditionally, heavy-duty diesel engines have high efficiencies but also high emissions of NOx and soot particles. New engine concepts show the potential to retain diesel-like efficiencies while reducing emissions by forming a completely or partially homogeneous mixture of fuel and air prior to ignition through compression. The long ignition delay required to form this homogeneous mixture makes the combustion process less predictable and inherently more difficult to control. This thesis summarizes work on control structures for three different setups of such low-emissions combustion engines. In a port-fuel injection engine, it was shown that combining two control variables in a mid-ranging control structure can address the problem of actuator saturation. In a fumigation engine, control was proven to be a powerful tool for automatic calibration in a laboratory setting. In a direct injection engine, LQG controllers were designed to optimize an emissions trade-off cost function during transients. Experiments were performed on a six-cylinder heavy-duty engine, and multi-cylinder effects and complications were explicitly considered in the work