Coupling excavator hydraulic system and internal combustion engine models for the real-time simulation

Rising energy costs and emissions restrictions force manufacturers to exploit new techniques to reduce fuel consumption and pollutant production. Many solutions have been proposed for off-road vehicles, mainly based on reduction of hydraulic losses, better control strategies and introduction of hybrid architectures. In these applications the optimization of the matching between hydraulic system and thermal engine is a major concern to improve system overall efficiency. The work presented in the paper is focused on the development of a method for the simulation of typical mobile machinery where hydraulic systems are powered by internal combustion engines; the proposed co-simulation approach can be useful in the development cycle of this machinery

Development and Application of Co-simulation and "Control- oriented" Modeling in the Improvement of Performance and Energy Saving of Mobile Machinery☆

Abstract Due to rising energy costs and tighter emissions restrictions from law regulations, mobile machinery and off-road vehicles manufacturers are forced to develop and exploit new techniques for the reduction of fuel consumption and pollutant emission. The main focus in this direction is the optimization of the matching between the fluid power circuit and the thermal engine to improve the efficiency of the hydraulic system and reducing the fuel consumption. A specific research activity has been started in this field by the authors to define methods and techniques for the mathematical simulation of off-road vehicles, where usually hydraulic systems are powered by internal combustion engines. The models proposed in the paper and the related results clearly show how these simulation tools can be used to improve the energy efficiency of the overall system, leading to an interesting reduction in fuel consumption by merely changing the engine rotational speed instead of adopting a constant-speed strategy

Optimization of Load Allocation Strategy of a Multi-source Energy System by Means of Dynamic Programming

AbstractMulti-source systems for the fulfillment of electric, thermal and cooling demand of a building can be based on different technologies (e.g. solar photovoltaic, solar heating, cogeneration, heat pump, absorption chiller) which use renewable, partially renewable and fossil energy sources. The main issues of these kinds of multi-source systems are (i) the allocation strategy which allows the division of the energy demands among the various technologies and (ii) the proper sizing of each technology.Furthermore, these two issues proves to be deeply interrelated because, while a wiser energy demand allocation strategy can lead to significant reductions in primary energy consumption, the definition itself of an optimal allocation strategy strongly depends on the actual sizing of the employed technologies. Thus the problem of optimizing the sizing of each technology cannot be separated from the definition of an optimal control strategy. For this purpose a model of a multi-source energy system, previously developed and implemented in the Matlab® environment, has been considered. The model takes account of the load profiles for electricity, heating and cooling for a whole year and the performance of the energy systems are modelled through a systemic approach. A dynamic programming algorithm is therefore employed in order to obtain an optimal control strategy for the energy demand allocation during the winter period. While the resulting control strategy is non-causal and therefore not suitable for the implementation on a real-time application, it allows the definition of a benchmark on the maximum primary energy savings achievable with a specific sizing solution. This result is therefore very helpful both in comparing different solutions and in subsequently define a proper causal control strategy. Finally, the model is applied to the case of a thirteen-floors tower composed of a two-floor shopping mall at ground level and eleven floors used as offices

Development and validation of a "crank-angle" model of an automotive turbocharged Engine for HiL Applications

Abstract Management and diagnostic functions are playing a key role in the improvement of engines performance and in the reduction of fuel consumption and pollutant emissions especially in automotive applications. As widely documented in the open literature, design, validation, and testing of control systems take actually advantage of theoretical models to a great extent, due to their capabilities to reduce development time and costs. However, the increasing complexity of present engines and related management systems give rise to challenging issues in the development and applications of mathematical models. The paper describes the improvements introduced in the original Library set up by the authors in Simulink® for "control-oriented" simulation of Internal Combustion Engines (ICE) and powertrains. The tool has been initially developed to build up Mean Value Models (MVMs) of automotive engines for "real-time" simulations, and in that version has been used in several HiL applications. Due to the enhancing requirements in engine control functions, the Library has been recently improved to allow for "crank-angle" simulation of the engine. To this extent models of intake and exhaust valves and of in-cylinder processes have been built up (where combustion process is described following a classic single-zone approach based on a proper Heat Release Rate, HRR). An original algorithm has been developed to run the model at a computational speed comparable with real time even with a resolution of 1 degree CA for in-cylinder calculation. Modeling tools have been applied to the simulation of a four-cylinder turbocharged Diesel engine with Exhaust Gas Recirculation. Through a specific calibration procedure, the model was fitted on a typical layout of an automotive Diesel engine and then validated comparing simulation results with experimental data measured by the OEM on a test bench. With a very low computational time, the model showed interesting capabilities in the simulation of the behavior of automotive engines with "crank-angle" resolution and therefore has been used in an original HiL application developed by the authors

Simulation of an excavator hydraulic system using nonlinear mathematical models

This paper describes a mathematical modelling methodology developed for the rapid simulation of an excavator hydraulic system. The modelling approach presented enables a reduction of the control system development time for a complete excavator, while providing accurate system dynamics. A tool for defining an appropriate control strategy is a key point for satisfying the need for systems with better energyefficiency. Moreover, the model will be a significant support in investigating energy recovery solutions and evaluating the suitability of hybrid solutions (mechanical/hydraulic/electric). The hydraulic model, composed of the pump’s grey box model and the valve block white box model, has been validated on the basis of a set of experimental data collected on a test bench. The results of this study are presented in this paper

Co-simulation and “control-oriented” modelling in the development of a hydraulic hybrid system.

Due to rising energy costs and emissions restrictions, vehicles manufacturers are con- tinuously forced to exploit new techniques to reduce fuel consumption and pollutant production through energy-saving solutions. In this direction hybrid architectures based on hydraulic systems seems to be promising for energy recovery strategies, but their real effectiveness is tightly related to the level of integration between sub- systems. In this work methods for mathematical simulation of hydraulic hybrid system pow- ered by internal combustion engines (ICEs) are proposed: co-simulation techniques have been applied coupling models developed both in AMESim® for the hydraulic system and in Simulink® for the engine to build up a first application on a basic hy- draulic-hybrid architecture. Taking properly care to the definition of components boundary and causality, and choosing consistently the integration time step, physical- based models can be developed to simulate the steady-state and transient behaviour of complex systems with limited computation burden (i.e., faster than real-time). A model of a basic hydraulic-hybrid system powered by an ICE is presented in the paper and its performance with reference to a given working cycle was estimated. Re- sults reported in the paper show clearly how the proposed approach can be useful to improve energy savings through the optimisation of system layout and related man- agement strategies using existing models of main components

A switching Moving Boundary Model for the simulation of ORC plants in automotive applications

Waste heat recovery is a promising approach to improve fuel economy and emissions of thermal engines for stationary and mobile applications. Among recent solutions, Organic Rankine Cycles (ORC) seem to join effectiveness and technological readiness for the application to Internal Combustion Engines (ICE), both Spark Ignition (SI) and Diesel. Significant reductions in fuel consumption have been reported, but – especially in automotive applications – further improvements in the ORC plant matching and performance in transient operations are required. This paper presents a lumped-parameter model of an ORC system for exhaust waste heat recovery in automotive engines. The heat exchangers dynamics is accounted for by modeling the behavior of the working fluid through the Moving Boundary Method (MBM), which is based on a lumped-parameter representation of the conservation laws for single and two-phase fluid flows. An original switching technique has been implemented to account for variations in the fluid properties and heat transfer during transient operations of the ORC plant. Grey-box models for the pump and expander have been developed starting from steady-state characteristic maps. The behavior of the comprehensive model in transient operating conditions has been significantly improved also during start-up process, usually a threatening situation for mathematical models

Nonlinear Model Predictive Control of an Organic Rankine Cycle for Exhaust Waste Heat Recovery in Automotive Engines

Energy recovery from exhaust gas waste heat can be regarded as an effective way to improve the energy efficiency of automotive powertrains, thus reducing CO2 emissions. The application of Organic Rankine Cycles (ORCs) to waste heat recovery is a solution that couples effectiveness and reasonably low technological risks. On the other hand, ORC plants are rather complex to design, integrate and control, due to the presence of heat exchangers operating with phase changing fluid, and several control devices to regulate the thermodynamic states of the systems. Furthermore, the power output and efficiency of ORC systems are extremely sensitive to the operating conditions, requiring precise control of the evaporator pressure and superheat temperature. This paper presents an optimization and control design study for an Organic Rankine Cycle plant for automotive engine waste heat recovery. The analysis has been developed using a detailed Moving Boundary Model that predicts mass and energy flows through the heat exchangers, valves, pumps and expander, as well as the system performance. Starting from the model results, a nonlinear model predictive controller is designed to optimize the transient response of the ORC system. Simulation results for an acceleration-deceleration test illustrate the benefits of the proposed control strategy

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

Background: Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. Methods: Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. Results: Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61-0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. Conclusions: Although clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome