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A model-based approach to control BMEP (Brake Mean Effective Pressure) and NOx emissions has been developed and assessed on a FPT F1C 3.0L Euro VI diesel engine for heavy-duty applications. The controller is based on a zero-dimensional real-time combustion model, which is capable of simulating the HRR (heat release rate), in-cylinder pressure, BMEP and NOx engine-out levels. The real-time combustion model has been realized by integrating and improving previously developed simulation tools. A new discretization scheme has been developed for the model equations, in order to reduce the accuracy loss when the computational step is increased. This has allowed the required computational time to be reduced to a great extent.

In the present work, different combustion control strategies have been experimentally tested in a heavy-duty 3.0 L Euro VI diesel engine. In particular, closed-loop pressure-based and open-loop model-based techniques, able to perform a real-time control of the center of combustion (MFB50), have been compared with the standard map-based engine calibration in order to highlight their potentialities. In the pressure-based technique, the instantaneous measurement of in-cylinder pressure signal is performed by a pressure transducer, from which the MFB50 can be directly calculated and the start of the injection of the main pulse (SOImain) is set in a closed-loop control to reach the MFB50 target, while the model-based approach exploits a heat release rate predictive model to estimate the MFB50 value and sets the corresponding SOImain in an open-loop control. The experimental campaign involved both steady-state and transient tests.

This paper presents the results of a long haul truck Waste Heat Recovery (WHR) system from simulation, test bench and public road testing. The WHR system uses exhaust gas recuperation only and utilizes up to 110kW of exhaust waste heat for the Organic Rankine Cycle (ORC) in a typical European driving cycle. The testing and simulation procedures are explained in detail together with the tested and simulated WHR fuel consumption benefit for different real life cycles in Europe and USA reaching fuel consumption benefits between 2.5% and 3.4%. Additionally a technology road map is shown which discusses the role of WHR in fulfilling the future CARB BSFC target value (minimum in map) of around 172 g/kWh.

This paper analyses the potential of dual clutch transmissions for MD and HD commercial vehicles in India. It compares vehicle performance, i.e. duration of a given transport cycle, and fuel consumption during this transport cycle for a 49 to. long haulage truck under typically given driving conditions in India. Additionally a performance comparison between 6 speed and 8 speed transmissions on long haul trucks in India is shown. The performance of different transmission types (MT, AMT and DCT) as well as 6 respectively 8 speed transmissions for each transmissions type are compared and discussed. The fuel efficiency as well as the cycle time of each individual transmission have been analyzed by powertrain system simulations. Furthermore the design of a modular transmission family concept for commercial vehicles (truck & bus) is presented. It combines both, a manual transmission as well as a powershift capable double clutch transmission within the same transmission family.

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional liquid fuels to CNG. To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts. In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on.

Dimethyl ether (DME) represents a promising fuel for heavy-duty engines thanks to its high cetane number, volatility, absence of aromatics, reduced tank-to-wheel CO2 emissions compared to Diesel fuel and the possibility to be produced from renewable energy sources. However, optimization of compression-ignition engines fueled with DME requires suitable computational tools to design dedicated injection and combustion systems: reduced injection pressures and increased nozzle diameters are expected compared to conventional Diesel engines, which influences both the air-fuel mixing and the combustion process. This work intends to evaluate the validity of two different combustion models for the prediction of performance and pollutant emissions in compression-ignition engines operating with DME. The first one is the Representative Interactive Flamelet while the second is the Approximated Diffusive Flamelet.

A contemporary approach for improving and developing the understanding of heavy-duty Diesel engine combustion processes is to use a concerted effort between experiments at well-characterized boundary conditions and detailed, high-fidelity models. In this paper, combustion processes of n-dodecane fuel sprays under heavy-duty Diesel engine conditions are investigated using this approach. Reacting fuel sprays are studied in a constant-volume pre-burn vessel at an ambient temperature of 900 K with three reference cases having specific combinations of injection pressure, ambient density and ambient oxygen concentration (80, 150 & 160 MPa - 22.8 & 40 kg/m3-15 & 20.5% O2). In addition to a free jet, two different walls were placed inside the combustion vessel to study flame-wall interaction.

Due to more stringent emission standards as well as customer requirements on performance improvement, model-based controls in diesel engines are becoming more and more common and necessary. In fact, as diesel engines becoming increasingly complicated with additional hardware components such as electonic throttle, EGR, VVT, VGT, as well as aftertreatment devices, the dynamics of the systems with more freedom of multiple actuators become much more sophisticated. With such complexity in the diesel engine systems, the traditional simple PI control, single-input single-output type of controls will not be good enough to address the multivariable interactions among subsystems, instead the advanced model-based, multi-input multi-output and coordinated supervisory controls almost become the only effective ways to improve system performance and achieve emission standards.

Diesel combustion is a very complex process, involving a reacting, turbulent and multi-phase flow. Furthermore, heavy duty engines operate mainly at medium and high loads, where injection durations are very long and cylinder pressure is high. Within such context, proper CFD tools are necessary to predict mixing controlled combustion, heat transfer and, eventually, flame wall interaction which might result from long injection durations and high injection pressures. In particular, detailed chemistry seems to be necessary to estimate correctly ignition under a wide range of operating conditions and formation of rich combustion products which might lead to soot formation. This work is dedicated to the identification of suitable methodologies to predict combustion in heavy-duty diesel engines using detailed chemistry.

Real Driving Emission (RDE) norms have changed the way vehicles are required to be calibrated and developed. This has moved the legislative requirements from predictable lab conditions to more realistic, real world conditions. Current Indian legislation allows certification for Heavy Duty (HD) applications on engine level and therefore decoupled from vehicle and the real world scenarios such as uncertainty and randomness in driver behavior, traffic conditions, road profiles, ambient conditions etc. which are not captured. Upcoming RDE legislation to be implemented in year 2023, has made it necessary to integrate engine with vehicle to consider the impact of various parameters on engine operating points and therefore on tail pipe emissions. This paper focusses upon the methodology developed using RDE cycle generator tool (RCG) for generating on-road parameters which influences the zone of engine operation and resulting emission levels.

The demand of game-changing technologies to improve efficiency and abate emissions of heavy-duty trucks and off-road vehicles promoted the development of novel engine concepts. The Recuperated Split-Cycle (R-SC) engine allows to recover the exhaust gases energy into the air intake by separating the compression and combustion stages into two different but connected cylinders: the compressor and expander, respectively. The result is a potential increase of the engine thermal efficiency. Accordingly, the 3D-computational fluid dynamics (CFD) modelling of the gas exchange process and the combustion evolution inside the expander becomes essential to control and optimize the R-SC engine concept. This work aims to address the most challenging numerical aspects encountered in a 3D numerical simulation of an R-SC engine.

The eHorizon unit enables the possibility to get information of the road ahead in a defined prediction horizon. This data, like road gradient, curve radius, velocity limitation and road class, can be used by the onboard transmission control unit (TCU) via Controller Area Network (CAN) bus. This obtained predictive road information combined with the actual driving conditions can be used to optimize the shifting strategy by a model predictive control (MPC) algorithm which intends to reduce the fuel consumption. In order to solve the optimum problem inside the MPC with less memory, a pre-optimization based dynamic programming (PODP) approach is proposed. In this paper, the predictive gear selection (PGS) strategy will be compared to a conventional automatic gear shifting strategy in a simulation environment and validated on road by implementing it on a heavy-duty truck with a 16-speed automated manual transmission (AMT).