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A viable option to reduce global warming related to internal combustion engines is to use renewable fuels, for example methanol. However, the risk of knocking combustion limits the achievable efficiency of SI engines. Hence, most high load operation is run at sub-optimal conditions to suppress knock. Normally the fuel is a limiting factor, however when running on high octane fuels such as methanol, other factors also become important. For example, oil droplets entering the combustion chamber have the possibility to locally impact both temperature and chemical composition. This may create spots with reduced octane number, hence making the engine more prone to knock. Previous research has confirmed a connection between oil droplets in the combustion chamber and knock. Furthermore, previous research has confirmed a connection between oil droplets in the combustion chamber and exhaust particle emissions.

Modern spark-ignited (SI) engines offer excellent emission reduction when operated with a stoichiometric mixture and a three-way catalytic converter. A challenge with stoichiometric compared to diluted operation is the knock propensity due to the high reactivity of the mixture. This limits the compression ratio, thus reducing engine efficiency and increasing exhaust temperature. The current work evaluated a model of conditions at inlet valve closing (IVC) and top dead center (TDC) for steady state operation. The IVC temperature model is achieved by a cycle-to-cycle resolved residual gas fraction estimator. Due to the potential charge cooling effect from methanol, a method was proposed to determine the fraction of fuel sourced from a wall film. Determining the level of charge cooling is important as it heavily impacts the IVC and TDC temperatures.

Stoichiometric operation of a Port Fueled Injection (PFI) Spark-Ignited (SI) engine with a three-way catalytic converter offers excellent CO2 reduction when run on renewable fuel. The main drawbacks with stoichiometric operation are the increased knock propensity, high exhaust temperature and reduced efficiency. Knock is typically mitigated with a reactive knock controller, with retarded ignition timing whenever knock is detected and the timing then slowly advanced until knock is detected again. This will cause some cycles to operate with non-ideal ignition timing. The current work evaluates the possibility to predict knock using the measured and modelled temperatures at Inlet Valve Closing (IVC) and Top Dead Center (TDC). Feedback effects are studied beyond steady state operation by using induced ignition timing disturbances.

This paper investigates the FPGA resources for the implementation of in-cycle closed-loop combustion control algorithms. Closed-loop combustion control obtains feedback from fast in-cylinder pressure measurements for accurate and reliable information about the combustion progress, synchronized with the flywheel encoder. In-cycle combustion control requires accurate and fast computations for their real-time execution. A compromise between accuracy and computation complexity must be selected for an effective combustion control. The requirements on the signal processing (evaluation rate and digital resolution) are investigated. A common practice for the combustion supervision is to monitor the heat release rate. For its calculation, different methods for the computation of the cylinder volume and heat capacity ratio are compared. Combustion feedback requires of virtual sensors for the misfire detection, burnt fuel mass and pressure prediction.

The constrained indicated efficiency optimization of the set-point reference for in-cycle closed-loop combustion regulators is investigated in this article. Closed-loop combustion control is able to reduce the stochastic cyclic variations of the combustion by the adjustment of multiple-injections, a pilot and main injection in this work. The set-point is determined by the demand on engine load, burned pilot mass reference and combustion timing. Two strategies were investigated, the regulation of the start of combustion (SOC) and the center of combustion (CA50). The novel approach taken in this investigation consists of including the effect of the controlled variables on the combustion dispersion, instead of using mean-value models, and solve the stochastic optimization problem. A stochastic heat release model is developed for simulation and calibrated with extensive data from a Scania D13 six-cylinder engine. A Monte Carlo approach is taken for the simulations.

Adaptation of predictive combustion models for their use in in-cycle closed-loop combustion control of a multi-cylinder engine is studied in this article. Closed-loop combustion control can adjust the operation of the engine closer to the optimal point despite production tolerances, component variations, normal disturbances, ageing or fuel type. In the fastest loop, in-cycle closed-loop combustion control was proved to reduce normal variations around the operational point to increase the efficiency. However, these algorithms require highly accurate predictive models, whilst having low complexity for their implementation. Three models were used to exemplify the proposed adaptation methods: the pilot injection’s ignition delay, the pilot burned mass, and the main injection’s ignition delay. Different approaches for the adaptation of the models are studied to obtain the demanded accuracy under the implementation constraints.

Pilot injections are normally used for the reduction of diesel engine emissions and combustion noise. Nonetheless, with a penalty on the indicated thermal efficiency. The cost is reduced by the minimization of the pilot mass, which on its counterpart increases the risk of pilot misfire. Pilot misfire can have a higher penalty on the indicated efficiency if it is not compensated adequately. This paper investigates how in-cycle closed-loop combustion control techniques can reduce the effects of pilot misfire events. By closed-loop combustion control, pilot misfire can be detected and counteracted in-cycle. Two injection strategies are investigated. The first is the control of the main injection, the second includes an additional second pilot injection. Based on the in-cycle misfire diagnose, two architectures are investigated. The first uses a cycle-to-cycle controller to set the main injection under each scenario.

For the reduction of emissions and combustion noise in an internal combustion diesel engine, multiple injections are normally used. A pilot injection reduces the ignition delay of the main injection and hence the combustion noise. However, normal variations of the operating conditions, component tolerances, and aging may result in the lack of combustion i.e. pilot misfire. The result is a lower indicated thermal efficiency, higher emissions, and louder combustion noise. Closed-loop combustion control techniques aim to monitor in real-time these variations and act accordingly to counteract their effect. To ensure the in-cycle controllability of the main injection, the misfire diagnosis must be performed before the start of the main injection. This paper focuses on the development and evaluation of in-cycle algorithms for the pilot misfire detection. Based on in-cylinder pressure measurements, different approaches to the design of the detectors are compared.

Increasing demands for more efficient engines and stricter legislations on exhaust emissions require more accurate control of the engine operating parameters. Engine control is based on sensors monitoring the condition of the engine. Numerous sensors, in a complex control context, increase the complexity, the fragility and the cost of the system. An alternative to physical sensors are virtual sensors, observers used to monitor parameters of the engine thus reducing both the fragility and the production cost but with a slight increase of the complexity. In the current paper a virtual intake manifold cylinder port pressure sensor is presented. The virtual sensor is based on a compressible flow model and on the pressure signal of the intake manifold pressure sensor. It uses the linearized pressure coefficient approach to keep vital performance behaviors while still conserving calibration effort and embedded system memory.

To conduct system level studies on internal combustion engines reduced order models are required in order to keep the computational load below reasonable limits. By its nature a reduced order model is a simplification of reality and may introduce modeling errors. However what is of interest is the size of the error and if it is possible to reduce the error by some method. A popular system level study is gas exchange and in this paper the focus is on the exhaust valve. Generally the valve is modeled as an ideal nozzle where the flow losses are captured by reducing the flow area. As the valve moves slowly compared to the flow the process is assumed to be quasi-steady, i.e. interpolation between steady-flow measurements can be used to describe the dynamic process during valve opening. These measurements are generally done at low pressure drops, as the influence of pressure ratio is assumed to be negligible.

The possibility of non-volatile particle agglomeration in engine exhaust was experimentally examined in a Euro VI heavy duty engine using a variable cross section agglomeration pipe, insulated and double walled for minimal thermophoresis. The agglomeration pipe was located between the turbocharger and the exhaust treatment devices. Sampling was made across the pipe and along the centre-line of the agglomeration pipe. The performance of the agglomeration pipe was compared with an equivalent insulated straight pipe. The non-volatile total particle number and size distribution were investigated. Particle number measurements were conducted according to the guidelines from the Particle Measurement Programme. The Engine was fuelled with commercially available low sulphur S10 diesel.

Global warming and climate change have led to a greater interest in the implementation of biofuels in internal combustion engines. In spark ignited engines, biofuels have been shown to improve efficiency and knock resistance while decreasing emissions of unburned hydrocarbons, carbon monoxide and particles. This study investigates the effect of biofuels on SI engine combustion through a graphical compilation of previously reported results. Experimental data from 88 articles were used to evaluate the trends of the addition of different biofuels in gasoline. Graphs illustrating engine performance, combustion phasing and emissions are presented in conjunction with data on the physiochemical properties of each biofuel component to understand the observed trends. Internal combustion engines have the ability to handle a wide variety of fuels resulting in a broad range of biofuel candidates.

Gaseous methane fuel for internal combustion engines have proved to be a competitive source of propulsion energy for heavy duty truck engines. Using biogas can even reduce the carbon footprint of the truck to near-zero levels, creating fully environmentally friendly transport. Gas engines have already been on the market and proved to be a popular alternative for buses and waste transport. However, for long haulage these gas engines have not been on par with the equivalent diesel engines. To improve the power and efficiency of EURO VI gas engines running stoichiometrically, a direct way forward is adding more boost pressure and spark advance in combination with more EGR to mitigate knock. Using in-cylinder turbulence to achieve higher mixing rate, the fuel can still be combusted efficiently despite the increased fraction of inert gases.

Typically the combustion in a direct injected compression ignited internal combustion engine is open-loop controlled. The introduction of a cylinder pressure sensor opens up the possibility of a virtual combustion sensor which could enable closed-loop combustion control and thus the potential to counteract effects such as engine part to part variation, component ageing and fuel quality diversity. Closed-loop combustion control requires precise, robust and preferably cheap sensors. This paper presents a virtual cylinder pressure sensor based on the signal from the inexpensive but well proven knock sensor. The method used to convert the knock sensor signal into a pressure estimate included the stages: Phase correcting the raw signal, Filtering the raw signal, Scaling the signal to known thermodynamic laws and provided engine sensors signals and Reconstructing parts of the signal with other known models and assumptions.

Clean combustion is one of the inherent benefits of using a high methane content fuel, natural gas or biogas. A single carbon atom in the fuel molecule results, to a large extent, in particle-free combustion. This is due to the high energy required for binding multiple carbon atoms together during the combustion process, required to form soot particles. When scaling up this process and applying it in the internal combustion engine, the resulting emissions from the engine have not been observed to be as particle free as the theory on methane combustion indicates. These particles stem from the combustion of engine oil and its ash content. One common practice has been to lower the ash content to regulate the particulate emissions, as was done for diesel engines. For a gas engine, this approach has been difficult to apply, as the piston and valvetrain lubrication becomes insufficient.

In this article, a virtual sensor for the estimation of the injected pilot mass in-cycle is proposed. The method provides an early estimation of the pilot mass before its combustion is finished. Furthermore, the virtual sensor can also estimate pilot masses when its combustion is incomplete. The pilot mass estimation is conducted by comparing the calculated heat release from in-cylinder pressure measurements to a model of the vaporization delay, ignition delay, and the combustion dynamics. A new statistical approach is proposed for the detection of the start of vaporization and the start of combustion. The discrete estimations, obtained at the start of vaporization and the start of combustion, are optimally combined and integrated in a Kalman Filter that estimates the pilot mass during the vaporization and combustion. The virtual sensor was programmed in a field programmable gate array (FPGA), and its performance tested in a Scania D13 Diesel engine.

A turbocharged Diesel engine for heavy-duty on-road vehicle applications employs a compact exhaust manifold to satisfy transient torque and packaging requirements. The small exhaust manifold volume increases the unsteadiness of the flow to the turbine. The turbine therefore operates over a wider flow range, which is not optimal as radial turbines have narrow peak efficiency zone. This lower efficiency is compensated to some extent by the higher energy content of the unsteady exhaust flow compared to steady flow conditions. This paper experimentally investigates the relationship between exhaust energy utilization and available energy at the turbine inlet at different degrees of unsteady flow. A special exhaust manifold has been constructed which enables the internal volume of the manifold to be increased. The larger volume reduces the exhaust pulse amplitude and brings the operating condition for the turbine closer to steady-flow.

The combustion in a direct injected internal combustion engine is normally open-loop controlled. The introduction of cylinder pressure sensors enables a virtual combustion sensor which in turn enables closed-loop combustion control, and the possibility to counteract effects such as engine part-to-part variation, component ageing and fuel quality diversity. Closed-loop combustion control requires precise, robust and preferably cheap sensors. This paper presents an investigation of the robustness and the limitation of a knock sensor based virtual combustion sensor. This virtual combustion sensor utilize the common heat release analysis using a knock sensor based virtual cylinder pressure signal. Major virtual sensor error sources in a heavy-duty engine were identified as: the specific heat ratio model, the boost pressure and the crank angle phasing. The virtual sensor errors were quantified in relation to both the measured cylinder pressure and the total virtual sensor error.

On-engine surge detection could help in reducing the safety margin towards surge, thus allowing higher boost pressures and ultimately low-end torque. In this paper, experimental data from a truck turbocharger compressor mounted on the engine is investigated. A short period of compressor surge is provoked through a sudden, large drop in engine load. The compressor housing is equipped with knock accelerometers. Different signal treatments are evaluated for their suitability with respect to on-engine surge detection: the signal root mean square, the power spectral density in the surge frequency band, the recently proposed Hurst exponent, and a closely related concept optimized to detect changes in the underlying scaling behavior of the signal. For validation purposes, a judgement by the test cell operator by visual observation of the air filter vibrations and audible noises, as well as inlet temperature increase, are also used to diagnose surge.

Improving turbocharger performance to increase engine efficiency has the potential to help meet current and upcoming exhaust legislation. One limiting factor is compressor surge, an air flow instability phenomenon capable of causing severe vibration and noise. To avoid surge, the turbocharger is operated with a safety margin (surge margin) which, as well as avoiding surge in steady state operation, unfortunately also lowers engine performance. This paper investigates the possibility of detecting compressor surge with a conventional engine knock sensor. It further recommends a surge detection algorithm based on their signals during transient engine operation. Three knock sensors were mounted on the turbocharger and placed along the axes of three dimensions of movement. The engine was operated in load steps starting from steady state. The steady state points of operation covered the vital parts of the engine speed and load range.