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Interest in the use of kerosene fuel in diesel engines has garnered researchers’ attention in the past few years due to its improve premixed combustion and its ability to decrease soot emission. The potential of using kerosene in the design stage of a diesel engine is thus a great motivator to study fuel spray development and to evaluate known fuel spray tip correlations and models with respect to their predictive capability with such a fuel. Therefore, the present paper proposes to investigate the spray development of a multi-hole solenoid injector fueled with kerosene under non-evaporative conditions. Moreover, the experimental results are used to evaluate how different phenomenological models proposed in the literature for diesel fuel are able to predict kerosene spray tip penetration. The experimental test rig is composed of a constant-volume pressurized vessel and a camera allowing to visualize the liquid phase using a backlight illumination technique. The influence of the injection pressure is studied at 400, 800 and 1600 bar, while three different injection durations (0.5, 1, and 2 ms) and five ambient pressures (2.5, 5, 10, 15 and 20 bar) are investigated. The experimental results are presented using a nondimensional time and fuel spray tip penetration to facilitate the analysis. The results show, as expected, that increasing the injection pressure or decreasing the ambient pressure results in a faster fuel spray tip penetration. The models that are evaluated include a constant ambient density hypothesis formulation, a variable ambient density model and three empirical correlations. A comparison between the models and experimental results shows that low injection pressure and short injection duration are two conditions in which the models have difficulty to predict the fuel spray tip penetration. Overall, the best performance was offered by the variable density model, which predicted the experimental data well.

Turbocharger testing is a time consuming process, and as rapid-prototyping technology advances, so must other areas in the development chain. As an example, in one study a compressor map took over 34 hours to measure. In this paper, an effort to combat the main bottleneck of turbocharger testing, namely the thermal inertia, is made. When changing operating point during the measurement process, several minutes can be required before the turbocharger components reach temperature steady state. In an earlier paper, a method based on non-linear trajectory optimization was developed that significantly reduced the testing time required to produce compressor performance maps. The time was reduced by a factor of over 60, compared to waiting for the system to reach steady state with constant inputs. However, the method required a model of the turbocharger. This paper extends the method with system identification and model predictive control (MPC). This is an important step in order to use the optimal control method when only geometric information of the turbocharger is known, such as new prototypes. To demonstrate the effectiveness of the combination of system identification, non-linear trajectory optimization and MPC, the control strategy is applied to a virtual gas-stand implemented as a Simulink model, based on data from a Mitsubishi TD04 turbocharger. The data that was used to create the model was originally collected at Saab Automobile in Trollhättan, 2011. The results show that system identification captures the turbocharger behavior. Trajectory optimization finds a set of time optimal input trajectories. MPC successfully tracks the generated references. Real time implementation of the Matlab/Simulink based algorithm is planned for experimental testing.

E-motors are characterized by their high-frequency content. This brings a new challenge for a technology such as Component-based Transfer Path Analysis. This paper will intend to show how this modular methodology has been applied for a wiper e-motor. The source is characterized in-situ by a set of blocked forces and alternatively with pseudo forces. Afterward, the independent loads are converted to contact forces using a Frequency Based Substructuring technique, allowing assembly definition and target prediction on a different receiver. Finally, the predicted vibrations on the target assembly are compared to the measured one for validation purposes. A dedicated high-frequency shaker was developed to meet the required data quality. In this way the substructuring technique was applicable in a wide frequency range up to 5 kHz. © 2021 SAE International. All Rights Reserved., QC 20220525

Bus drivers are a group at risk of often suffering from musculoskeletal problems, such as low-back pain, while bus passengers on the last-row seats experience accelerations of high values. In this paper, the contribution of K-seat in decreasing the above concern is investigated with a detailed simulation study. The K-seat model, a seat with a suspension that functions according to the KDamper concept, which combines a negative stiffness element with a passive one, is benchmarked against the conventional passive seat (PS) in terms of comfort when applied to different bus users' seats. More specifically, it is tested in the driver's and two different passengers' seats, one from therear overhang and one from the middle part. For the benchmark shake, both are optimized by applyingexcitations that correspond to real intercity bus floor responses when it drives over a real road profile. Then a human model is placed on the seats in order to compare their optimum solutions in terms of the user's whole-body vibrations (WBVs), using objective comfort metrics. Based on the results, the K-seat improves significantly the comfort of the users (~92%) compared to the PS, while it achieves a similar decrease in the maximum values of the user's back accelerations (~97%)., QC 20211216QC 20220315

Thermoacoustic engine has been proven to be a promising technology for automotive exhaust waste heat recovery to save fossil fuel and reduce emission thanks to its ability to convert heat into acoustic energy which, hence, can be harvested in useful electrical energy. In this paper, based on the practical thermodynamic parameters of the automotive exhaust gas, including mass flow rate and temperature, two traveling-wave thermoacoustic engines are designed and optimized for the typical heavy-duty and light-duty vehicles, respectively, to extract and reutilize their exhaust waste heat. Firstly, nonlinear thermoacoustic models for each component of a thermoacoustic engine are established in the frequency domain, by which any potential steady operating point of the engine is available. Then, a matching between the required heat for the engine and the available heat from the exhaust gas is performed to determine the practical operating point and the performance of the thermoacoustic engine driven by this exhaust gas with a specified thermodynamic state. Furthermore, an outline-size optimization of the thermoacoustic engines is carried out for the heavy-duty and light-duty applications, respectively, to improve the waste heat utilization efficiency and maximize the acoustic power. To consider the impact of the system weight on vehicles, a power-to-weight ratio is adopted in this work as the optimization objective. It's shown that the optimum system outside diameters for the heavy- and light-duty applications are different due to the different thermodynamic characteristics of the exhaust gas. The available acoustic powers with the maximum power-to-weight ratio for heavy- and light-duty applications are approximately 515 Watts and 935 Watts with 8.2% and 18% of thermal efficiency, respectively, corresponding to 29.3% and 42.9% of their respective Carnot efficiencies. The resulted CO2 emission reductions are approximately 0.37 kg/h and 0.77 kg/h, respectively., QC 20220309

In this paper, a High-Temperature Electrical Low-Pressure Impactor (HT-ELPI+) was used to measure particles from a light-duty direct injected spark ignited (DISI) engine fueled with gasoline and ethanol. The HT-ELPI+ measured volatile and non-volatile particle emissions down to 6 nm without the need for dilution. Particle emissions were measured at four operating points while sweeping the end of injection, and at idle operation. The total particle number (PN) and particle size distribution (number and mass) for both non-volatile and volatile emissions were measured with the HT-ELPI+ and compared to the measured PN using two 71.4 times diluted Condensation Particle Counters (CPCs) with two different cut-off sizes, with 23 nm and 7 nm cut-off, respectively. The results show an increase in particle emissions in terms of particle mass and total particle number for ethanol compared to gasoline. The difference in soot mass emissions is small between the fuels. However, PN shows a significant increase for ethanol, especially at low engine speed due to a deterioration in the evaporation, mixture formation and air entrainment of ethanol. A majority of the emitted particles exhibit particle sizes below 23 nm, wherein the highest numbers occurred below 10 nm. At steady-state operation, no clear difference was observed between the diluted and undiluted measurements. On the contrary, a significant difference is detected between the undiluted and diluted measurements of ethanol at idle. These observations could indicate a greater significance of dilution at lower exhaust gas temperatures and mass flow rates or when a large number of nucleation mode particles are emitted from the engine., QC 20210518, Framtida Alternativa Transportbränslen

The assembly of threaded fasteners may seem straightforward. However, there are many factors to consider to achieve quality tightened joints, including the joint material, threaded fastener, and coatings. Additionally, there are many assembly tool types and torque application strategies to choose from. This investigation studies the tightening speed dynamics when using torque as a control method. The clamp force obtained in the joint changes when tightening at high speed or when the speed varies greatly during tightening. This type of tightening is called highly dynamic. Highly dynamic torque control tightening strategies are studied, such as impact, pulse, and inertia-controlled methods, and compared with the continuous drive strategy, which is a standard dynamic torque tightening method. The clamp force and its scatter caused by the torque accuracy in the assembly tool type are investigated for the abovementioned torque application strategies. The study also focuses on the different results obtained from the International Organization for Standardization’s (ISO) 16047:2005 (Fasteners-Torque/clamp force testing) standard compared to a production-like setup., SAE Technical Paper 2021-01-5073

Renewable fuels from different feedstocks can enable sustainable transport solutions with significant reduction in greenhouse gas emissions compared to conventional petroleum-derived fuels. Nevertheless, the use of biofuels in diesel engines will still require similar exhaust gas cleaning systems as for conventional diesel. Hence, the use of diesel particulate filters (DPF) will persist as a much needed part of the vehicle's aftertreatment system. Combustion of renewable fuels can potentially yield soot and ash with different properties as well as larger amounts of ash compared to conventional fossil fuels. The faster ash build-up and altered ash deposition pattern lead to an increase in pressure drop over the DPF, increase the fuel consumption and call for premature DPF maintenance or replacement. Prolonging the maintenance interval of the DPF for heavy-duty trucks, having a demand for high up-Time, is highly desirable. Understanding the mechanisms of ash formation and build-up in the DPF, especially for renewable fuels, is therefore of uttermost importance. Techniques using accelerated ash accumulation methods offer a way to cut down on the costly and time consuming field tests, as well as offering a robust method to perform tests under conditions relevant for real-driving. This paper presents a literature review regarding accelerated ash accumulation methods by summarizing these methods and giving an overview of the various rigs found in literature. Moreover, the paper outlines the strengths and weaknesses of the different methods. Furthermore, it offers a simplistic route to choose between the different techniques based on the particular research question to be addressed., QC 20210324

This paper presents experimental results concerning the distribution of the refrigerant R134a in the inlet header of plate evaporators in automotive air-conditioning systems, as a function of operating conditions. Typical conditions in such air-conditioning systems cover the: mass flow rates of 20-60 g/s, and inlet vapor quality at the inlet header of up to 30%. The distribution of the refrigerant is expressed as the fraction of the liquid available at the inlet. The distribution results are given with flow images (shown in the paper) followed by analysis and discussions of the results.

Modelbased systems engineering is becoming an important tool when meeting the challenges of developing the complex future vehicles that fulfill the customers and legislators ever increasing demands for reduced pollutants and fuel consumption. To be able to work systematically and efficiently it is desirable to have a library of components that can be adjusted and adapted to each new situation. Turbocharged engines are complex and the compressor model serves as an in-depth example of how a library can be designed, incorporating the basic physics and allowing fine tuning as more information becomes available. A major part of the paper is the summary and compilation of a set of rules of thumb for compressor map extrapolation. The considerations discussed are extrapolation to surge, extrapolation to restriction region, and extrapolation out to choking. Furthermore the compressor diameter is coupled to the maximum performance of the compressor such as maximum speed, mass flow, and pressure ratio. All this is a result of an analysis of a database of more than 300 compressors. The paper uses the compressor modeling to discuss how wishes for extendability and reuse of component performance influences the library design. A Modelica library named Vehicle Propulsion Library VehProLib has been developed to meet these goals by including basic components that give a starting point for modeling and at the same time allows reuse and extendablility.

Vehicle dynamics development relies on subjective assessments (SA), which is a resource-intensive procedure requiring both expert drivers and vehicles. Furthermore, development projects becoming shorter and more complex, and increasing demands on quality require higher efficiency. Most research in this area has focused on moving from physical to virtual testing. However, SA remains the central method. Less attention has been given to provide better tools for the SA process itself. One promising approach is to introduce computer-tablets to aid data collection, which has proven to be useful in medical studies. Simple software solutions can eliminate the need to transcribe data and generate more flexible and better maintainable questionnaires. Tablets’ technical features envision promising enhancements of SA, which also enable better correlations to objective metrics, a requirement to improve CAE evaluations. However, it cannot be assumed that a tablet-based solution is feasible in vehicle dynamics SA context. Any distraction might result in low SA quality and safety issues when test-drivers are subjected to high mental workload pushing the vehicles to their performance-limits. In this study, a SA tablet-software for steering feel, handling, and ride was developed and systematically evaluated versus the traditional pen-and-paper method. The results indicate that the new approach is technically feasible in this context, meets more use-cases, and the drivers’ attitude towards it is positive. It increased questionnaire completion and rating resolution while reducing the error rate and transcription time. Although attendees reported that the paper-based approach has advantages from a usability point of view, the benefits of the tablet-based approach enable further process-related advantages., QC 20170208, iCOMSA

© 2019 SAE International. All Rights Reserved. Electric vehicles are considered as one of the most popular way to decrease the consumption of petroleum resources and reduce environmental pollutions. Motor control system is one of the most important part of electric vehicles. It includes power supply module, IGBT driver, digital signal processing (DSP) controller, protection adjustment module, and resolver to digital convertor. To implement the control strategies on motor control system, a lot of practical aspects need to be taken into accounts. It includes setup of the initial excitation current, consistency of current between motor and program code, over-modulation, field weakening control, current protection, and so on. In this paper, an induction motor control system for electric vehicles is developed based on DSP. The control strategy is based on the field-oriented control (FOC) and space vector pulse width modulation (SVPWM). Speed calculation, over-modulation, field weakening control, PI controller, and fault diagnosis are also applied in this DSP algorithm. As an industry product running on a real electric bus with a 100kW induction motor, communication with vehicle control unit (VCU) by CAN bus, control system safety and PC software designed for lab experiments are also discussed. This paper focused on how to develop the advanced motor control system for electric vehicles for industrial application. The steady-state and transient performances of this motor control system are analyzed by both test-bench experiments and road experiments. Its performance is satisfactory when applied to the real electric vehicle.

With increasing demands to reduce fuel consumption and CO2 emissions, it is necessary to recover waste heat from modern Heavy Duty (HD) truck engines. Organic Rankine Cycle (ORC) has been acknowledged as one of the most effective systems for Waste Heat Recovery (WHR) due to its simplicity, reliability and improved overall efficiency. The expander and working fluid used in ORC WHR greatly impact the overall performance of an integrated engine and WHR system. This paper presents the effects of volumetric expanders on the ORC WHR system of a long haulage HD truck engine at a steady-state engine operating point chosen from a real-time road data. Performance of a long haulage HD truck engine is analyzed, based on the choice of three volumetric expanders for its WHR system, using their actual performance values. The expanders are: An oil-free open-drive scroll, a hermetic scroll and an axial piston expander with working fluids R123, R245fa and ethanol, respectively. Performance of the engine that accommodates the WHR system, with each expander and working fluid combination, is assessed based on the overall system efficiency that can be achieved through heat recovery from the engine exhaust. This simulation study is carried out using validated 0D models of the scroll expanders and performance data of the piston expander, adopted from literature, and a 1D system model of a long haulage HD truck engine encompassing a WHR system. Under the given conditions, the open-drive scroll expander (R123) leads to a higher system efficiency of 6.3% at an optimum expander speed of 3400 rpm with an estimated fuel saving of 3.6% in the vehicle under study. The hermetic scroll expander (R245fa) exhibits potential performance at higher rotational speeds; it leads to 5.4% system efficiency at 5000 rpm and 3% fuel-saving. The axial piston expander (ethanol) results in a consistent performance over a wide range of expander speeds. The effect of sizing a volumetric expander in improving the over, QC 20200702

Monitoring, modeling, prediction, and control of the braking process is a difficult task due to a complex interaction between the brake contact surfaces (disc pads and brake disc). It is caused by different influences of braking regimes and brake operation conditions on its performance. Faster and better control of the braking process is extremely important in order to provide harmonization of the generated braking torque with the tire-road adhesion conditions. It has significant influence on the stopping distance. The control of the braking process should be based on monitoring of the previous and current values of parameters that have influence on the brake performance. Primarily, it is related to the disc brake actuation pressure, the vehicle speed, and the brake interface temperature. The functional relationship between braking regimes and braking torque has to be established and continuously adapted according to the change of mentioned influencing factors. In this paper dynamic neural networks have been used for the purpose of modeling and control of the disc brake actuation pressure. Parameters of the developed dynamic neural model were used to build a program for implementation in a microcontroller. Recurrent neural networks have been implemented in 8-bit CMOS microcontroller for control of the disc brake actuation pressure. Two different models have been developed and integrated into the microcontroller. The first model was used for modeling and prediction of the braking torque. Based on that, the second inverse neural model, has been developed able to predict the brake actuation pressure needed for achieving previously selected (desired) braking torque value.

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. The tolerance analysis of the virtual sensor showed the signals of the crank angles 10% and 50% heat release as robust with low sensitivity to errors. An additional dependency found for the crank angle of 10% heat release was the compression ratio error. The study concluded that it is possible to estimate the mass fraction of 10% and 50% heat release accurately enough to make the virtual sensor an option for closed loop combustion control. The study was not able to prove the virtual sensor cylinder pressure signal, crank angle of burned fractions signals or total heat release signal capable of confident determination of the amount of biodiesel in fossil diesel., Conference code: 134884; Export Date: 9 May 2018; Conference Paper. QC 20180516

When electrifying the powertrain, there arises an opportunity to revise the traditional turbocharging trade-off between fuel-economy and transient performance. With the help of electrification, it might be possible to make the trade-off in favor of fuel economy, since transient response can be improved by the electric machine. The paper investigates this trade-off by looking at three turbocharger selections. A conventionally dimensioned turbocharger, an efficiency optimized turbocharger with maintained flow capacity, and an efficiency optimized turbocharger with increased flow capacity. The concepts are evaluated on the following cases: stationary operation, engine tip-in performance, vehicle acceleration performance, and on road fuel economy performance. The investigation is based on a validated mean value engine model of a six cylinder inline CI engine, and on a validated driveline and vehicle model of a heavy-duty truck. The evaluations are made with the help of simulations and numerical optimal control. The results show that there is a fuel saving potential, and that the transient response is improved by the electric machine.

Batteries are useful in Fuel Cell Hybrid Electric Vehicles (FCHEV) to fulfill transient demands and for regenerative braking. Efficient energy management strategies paired with optimal powertrain design further improves the efficiency. In this paper, a new methodology to simultaneously size the propulsive elements and optimize the power-split strategy of a Range Extended Battery Electric Vehicle (REBEV), using a Polymer Electron Membrane Fuel Cell (PEMFC), is proposed and preliminary studies on the effects of the driving mission profile and the auxiliary power loads on the sizing and optimal performance of the powertrain design are carried out. Dynamic Programming is used to compute the optimal energy management strategy for a given driving mission profile, providing a global optimal solution. The component sizing problem is performed using a machine learning based, guided design space exploration to find the set of Pareto-optimal solutions that give the best trade-offs between the different objectives. The powertrain model includes the dynamic behavior of the fuel cell system compressor and a battery lumped parameter thermal model along with the quasi-static semi-empirical model of the fuel cell and a zero-order battery model. Initial results indicate an increase in the Pareto-optimal sizes with the inclusion of thermal management.

Wear of brake friction materials were found to be a complex combination of abrasion, adhesion, fatigue, delamination, and thermal decomposition. Stochastic nature of wear of brake friction materials is result of these wear mechanisms and their transition from one combination to another. The dominant wear mechanism of brake friction materials is influenced by braking regimes and friction material characteristics. Regarding friction material characteristics, the most important influences are related to its formulation and manufacturing conditions. Prediction of friction materials wear versus their manufacturing conditions can be considered as an important issue for further friction materials development. In this paper, the artificial neural network abilities have been used for predicting wear of the friction materials versus synergistic influence of: (i) mass percentages of phenolic resin, fibers, abrasives, and lubricants in formulation of the friction materials, (ii) work done by brake application, (iii) brake interface temperature, and (iv) friction materials manufacturing conditions (moulding pressure, moulding time, moulding temperatures, heat treatment time, and heat treatment temperatures). The model of the friction materials wear has been developed able to predict how manufacturing conditions of the friction material influence its wear in synergy with different mass percentages of key ingredients in the friction material formulations at different brake interface temperatures.

Prediction of the brake friction material performance versus changes of its composition, manufacturing, and operation conditions is considered as an important step in the friction materials development. Due to complex synergy effects of these influencing factors on the friction coefficient stability, an analytical model of the brake friction materials performance is difficult to obtain. That is why in this paper artificial neural networks have been used for modelling and predicting the effects of these influencing factors on the brake friction materials speed sensitivity. A two hidden-layer neural network model, trained by the Bayesian Regulation algorithm, has been developed with inherent abilities to generalize the complex influences on the speed sensitivity performance of the brake friction materials.

The demands imposed on a braking system of passenger cars, under wide range of operating conditions, are high and manifold. Improvement of automotive braking systems' performance, under different operating conditions, is complicated by the fact that braking process has stochastic nature. The automotive brake's performance primarily affected the braking system's performance because their performance results from the complex interrelated phenomena occurring in the contact of the friction pair. These complex braking phenomena are mostly affected by the physicochemical properties of friction materials ingredients, its manufacturing conditions, and brake's operation regimes. Analytical models of brakes performance are difficult, even impossible to obtain due to complex and highly nonlinear phenomena involved during braking. That is why, in this paper all relevant influences on the disc brake operation of a passenger car have been integrated by means of artificial neural networks. The influences of the friction material's composition (18 ingredients), its manufacturing conditions (5 parameters), and brake's operation regimes (application pressure, initial speed, and temperature in the contact of friction pair) have been modelled versus changes of the brake factor C i.e. coefficient of the friction. The artificial neural network's model of the disc brake operation has been used for their intelligent controlling. Based on an optimal neural model of the disc brake operation, the neural network's controller of disc brake operation can be developed. The intelligent control of the brake's operation has been related to adjusting of application pressure of the disc brakes. Accordingly, the brake's performance would be adjusted on the level that corresponds to driver's demanded deceleration taking into consideration the history of brake's performance represented by the neural model.