Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Smart materials and vehicle efficiency. Design and experimentation of new devices.

In this dissertation the activities carried out during the PhD are comprehensively described. The research mainly focused on the development of novel smart devices aimed at disengaging auxiliaries in internal combustion engine vehicles. In particular, the activities dealt with modeling, design, manufacturing and testing different fail-safe magnetorheological clutch prototypes, in the framework of a project funded by Regione Toscana, which involved two departments of the University of Pisa and Pierburg Pump Technology - Stabilimento di Livorno. After an extended literature review, several concepts of the clutch were proposed, which led to the design of the first magnetorheological prototype. An intensive experimental campaign was conducted, which involved several prototypes. A particular attention was focused on the measurement and analysis of the torque transmitted by the clutch in different operating conditions and new indices were proposed to objectively analyze the performances of magnetorheological clutches in general. On the basis of the results of the first experimental phase, the limits of the first design were analyzed and a novel prototype was developed, which succeeded in fulfilling all the design specifications. Further analyses were carried out in order to develop a clutch model starting from the experimental results. The effect of clutch heating was considered and a complete model of the clutch based on neural networks was proposed. The model was capable of taking into account the effect of the main parameters influencing the torque characteristic and may be used in a vehicle simulator or in a hardware-in-the-loop bench. Finally, an additional component to be connected to the clutch, which made use of shape memory alloys, was developed and tested during the visiting period at the University of Toledo (OH), USA

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

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

Passenger car active braking system: Pressure control design and experimental results (part II)

This paper deals with the design of a brake caliper pressure controller for a conventional anti-lock braking system/electronic stability control system and the experimental validation of its tracking performances. The analysis of the hydraulic plant, carried out in part I of this two-part study, is here utilized to develop the control algorithm. The control strategy is based on a feed-forward and a proportional integral controller through pulse width modulation with a constant frequency and variable duty cycle. The feed-forward contribution requires modeling of the nonlinear openloop system behavior which has been experimentally identified and described through two-dimensional maps: the inputs are the duty cycle applied to the electrovalves and the pressure drop across their orifice, while the output is the pressure gradient in the brake caliper. These maps, obtained for inlet and outlet valves, are used to set the feed-forward term. Finally a proportional integral controller is designed to reject external disturbances and compensate for model uncertainties. A brake system test rig, described in part I, is used for building inverse maps and validating the proposed control logic. Different reference pressure profiles are used to experimentally verify the control tracking performances

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

Smart materials and vehicle efficiency. Design and experimentation of new devices.

In this dissertation the activities carried out during the PhD are comprehensively described. The research mainly focused on the development of novel smart devices aimed at disengaging auxiliaries in internal combustion engine vehicles. In particular, the activities dealt with modeling, design, manufacturing and testing different fail-safe magnetorheological clutch prototypes, in the framework of a project funded by Regione Toscana, which involved two departments of the University of Pisa and Pierburg Pump Technology - Stabilimento di Livorno. After an extended literature review, several concepts of the clutch were proposed, which led to the design of the first magnetorheological prototype. An intensive experimental campaign was conducted, which involved several prototypes. A particular attention was focused on the measurement and analysis of the torque transmitted by the clutch in different operating conditions and new indices were proposed to objectively analyze the performances of magnetorheological clutches in general. On the basis of the results of the first experimental phase, the limits of the first design were analyzed and a novel prototype was developed, which succeeded in fulfilling all the design specifications. Further analyses were carried out in order to develop a clutch model starting from the experimental results. The effect of clutch heating was considered and a complete model of the clutch based on neural networks was proposed. The model was capable of taking into account the effect of the main parameters influencing the torque characteristic and may be used in a vehicle simulator or in a hardware-in-the-loop bench. Finally, an additional component to be connected to the clutch, which made use of shape memory alloys, was developed and tested during the visiting period at the University of Toledo (OH), USA

Neutral network-PID control algorithm for semi-active suspensions with magneto-rheological damper

In this paper, a semi-active suspension control system based on Magneto-Rheological (MR) damper is designed for a commercial vehicle to improve the ride comfort and driving stability. A mathematical model of MR damper based on the Bouc-Wen hysteresis model is built. The mathematical model could precisely describe the characteristics of MR damper compared with the bench test results. The neural network-PID controller is designed for the semi-active suspension systems. According to the numerical results, the proposed controller can constrain vehicle vibrations and roll angle significantly. A detailed multi-body dynamic model of the light vehicle with four semi-active suspensions are established, and an actual vehicle handling and stability tests are carried out to verify the control performances of the proposed controller. It can be concluded that MR semi-active suspension systems can play a key role in coordination between the ride comfort and handling stability for the commercial vehicle