Using portable emissions measurement systems (PEMS) to derive more accurate estimates of fuel use and nitrogen oxides emissions from modern Euro 6 passenger cars under real-world driving conditions

Data from portable emissions measurement systems (PEMS) and other sources have allowed the discrepancy between type approval and real-world fuel economy and nitrogen oxides (NOx) emissions to be both identified and quantified. However, a gap in the knowledge persists because identifying this discrepancy does not allow us to predict real-world fuel economy and emissions accurately. We address this gap in the knowledge using a bottom-up approach: a PEMS is used across a range of Euro 6 petrol and diesel vehicles, from which internally-consistent powertrain models are derived. These training vehicles are simulated over 20 real-world and regulated driving cycles. 26 metrics representing driving, vehicle and ambient characteristics are used to develop quantile regression (QR) models for three vehicle groups: direct-injection petrol vehicles with three way catalysts; diesel vehicles with selective catalytic reduction; and diesel vehicles with lean NOx traps. 95% prediction intervals are used to assess the predictive accuracy of the QR models from a set of validation vehicles. Across the vehicle groups, QR models for both fuel economy and NOx emissions depended on the dynamics of the driving cycles more than the engine characteristics or ambient conditions. The 95% prediction interval for fuel economy enclosed most of the observed values from the PEMS test, with similar prediction error to COPERT in most cases. The bene ts of the QR approach were more pronounced for NOx emissions, where the majority of PEMS observed data was enclosed in the 95% PI and median prediction error was up to two times lower than COPERT.EPSR

New Trends on the Combustion Processes in Spark Ignition Engines

This Special Issue on "New Trends on the Combustion Processes in Spark Ignition Engines" contains nine papers on new developments on Internal Combustion (IC) engines aiming to enhance their efficiency, leading to the reduction of fossil CO2 and other gaseous pollutants. It is divided into two parts. In the initial part, the focus in on fuels, with four papers discussing the use of biofuels and other alternative fuels that can be used in different types of IC Engines. Additionally, conventional fuels are tested in order to evaluate their optimal use in new downsizing high-boost engines. A revision paper on alternative fuels is also included. The second part involves the study and improvement of engine combustion diagnostics as well as the presentation of an alternative type of propulsion system

ANN Technique to Predict Performances of Diesel Engine Runs by Butanol-Diesel Blends

تعتبر دراسه الاداء لمحركات الديزل التي تعمل بخليط من البيوتانول– ديزل واحدة من حالات البحث المهمة. و قد بذلت جهود كبيره في ابحاث و دراسات عمليه و نظريه في هذا المجال. ان تقنيه استخدام الشبكات العصبيه الصناعيه واحدة من الطرق المستخدمه لدراسه وتخمين اداء محركات الديزل التي تعمل بخلائط البيتانول – الديزل. ان الشبكه العصبيه الصناعيه تستخدم مايسمى بخوارزميه التغذيه الاماميه و التقدم الرجعيه. تمثلت خواص الاداء ب الكفاءه المكبحيه و كميه صرف الوقود و درجه حرارة غازات العادم. تم دراسه اكثر من اربعين تشكيل للموديل موضع البحث. ولكل تشكيل تم حساب معدل الخطا و اعلى خطا و اقل خطا و قد استخدمت الانحرافات المعياريه من الاحصاء لدراسه افضل تشكيل و الذي يعطي اقل خطا و اسرع وصول الى الحل. تم ضبط الخطا في برنامج الموديل ليكون فقط 1% و نسبه الخطا في التعديل و التصويب و3%. لقد اكدت النتائج المستقاه من هذا الموديل كفاءه الشبكات العصبيه الصناعيه في تخميين سلوك الاداء لمحرك يعمل بوقود الديزل و استخدم خليط البيوتانول كوقود نظيف له .Performance of a diesel engine running under butanol-diesel blends one of important cases to evaluate the variance in the engine performance due to the fuel type change. Many efforts exerted in this field. Artificial neural network (ANN) model one of modern technique is used to predict the engine performance. ANN using a multi layer feed forward back propagation learning algorithm is developed to evaluate diesel engine performance. The brake efficiency, fuel consumption and exhaust temperature are predicted. The data required for training of ANN model are collected from experimental tests carried out on multi cylinder diesel engine. More than forty different architectures are tested for obtaining best fitting model. Maximum, minimum as well as average percentage errors are calculated for each architecture and R & s test is carried out to decide upon the best architecture for this model. The training process is set to stop when all errors are below 0.01 for training and below 3% for the validation. The results obtained from trained model are compared with experimental data of engine performance. The numerical investigation demonstrated that the ANN model is the best approach and assessment program for diesel engine performance with only 0.7% absolute average errors. The precise results of the model indicated an excellent and prompting training of ANN model. &nbsp

Artificial neural network based modelling of internal combustion engine performance

The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create the model are load, engine speed (RPM), fuel flow rate (FFR) & air flow rate (AFR); contrariwise the output parameters that have been used are brake power (BP), brake thermal efficiency (BTE), volumetric efficiency (VE), brake mean effective pressure (BMEP) and brake specific fuel consumption (BSFC). To begin with, databank has been alienated into training sets and testing sets. At that juncture, an ANN based model has been developed using training dataset which is based on standard back-propagation algorithm. Subsequently, performance and validation of the ANN based models have been measured by relating the predictions with the experimental results. Correspondingly, four different statistical functions have been used to examine the performance and reliability of the ANN based models. Moreover, Garson equation has been used to estimate the relative importance of the four different input variables towards their specific output. The results of the model suggests that, ANN based model is impressively successful to forecast the performance parameters of diesel engines for different input variables with a greater degree of accurateness and to evaluate relative impact of input variables

Engine maps of fuel use and emissions from transient driving cycles

Air pollution problems persist in many cities throughout the world, despite drastic reductions in regulated emissions of criteria pollutants from vehicles when tested on standardised driving cycles. New vehicle emissions regulations in the European Union and United States require the use of OBD and portable emissions measurement systems (PEMS) to confirm vehicles meet specified limits during on-road operation. The resultant in-use testing will yield a large amount of OBD and PEMS data across a range of vehicles. If used properly, the availability of OBD and PEMS data could enable greater insight into the nature of real-world emissions and allow detailed modelling of vehicle energy use and emissions. This paper presents a methodology to use this data to create engine maps of fuel use and emissions of nitrous oxides (NO$_x$), carbon dioxide (CO$_2$) and carbon monoxide (CO). Effective gear ratios, gearbox shift envelopes, candidate engine maps and a set of vehicle configurations are simulated over driving cycles using the ADVISOR powertrain simulation tool. This method is demonstrated on three vehicles – one truck and two passenger cars – tested on a vehicle dynamometer and one driven with a PEMS. The optimum vehicle configuration and associated maps were able to reproduce the shape and magnitude of observed fuel use and emissions on a per second basis. In general, total simulated fuel use and emissions were within 5% of observed values across the three test cases. The fitness of this method for other purposes was demonstrated by creating cold start maps and isolating the performance of tailpipe emissions reduction technologies. The potential of this work extends beyond the creation of vehicle engine maps to allow investigations into: emissions hot spots; real-world emissions factors; and accurate air quality modelling using simulated per second emissions from vehicles operating in over any driving cycle.Engineering and Physical Sciences Research Council (Centre for Sustainable Road Freight Transport (EP/K00915X/1), Energy Efficient Cities Initiative (EP/ F034350/1)

Estimating on-road vehicle fuel economy in Africa : A case study based on an urban transport survey in Nairobi, Kenya

In African cities like Nairobi, policies to improve vehicle fuel economy help to reduce greenhouse gas emissions and improve air quality, but lack of data is amajor challenge. We present a methodology for estimating fuel economy in such cities. Vehicle characteristics and activity data, for both the formal fleet (private cars, motorcycles, light and heavy trucks) and informal fleet-minibuses (matatus), three-wheelers (tuktuks), goods vehicles (AskforTransport) and two-wheelers (bodabodas)-were collected and used to estimate fuel economy. Using two empirical models, general linear modelling (GLM) and artificial neural network (ANN), the relationships between vehicle characteristics for this fleet and fuel economy were analyzed for the first time. Fuel economy for bodabodas (4.6 ± 0.4 L/100 km), tuktuks (8.7 ± 4.6 L/100 km), passenger cars (22.8 ± 3.0 L/100 km), and matatus (33.1 ± 2.5 L/100 km) was found to be 2-3 times worse than in the countries these vehicles are imported from. The GLM provided the better estimate of predicted fuel economy based on vehicle characteristics. The analysis of survey data covering a large informal urban fleet helps meet the challenge of a lack of availability of vehicle data for emissions inventories. This may be useful to policy makers as emissions inventories underpin policy development to reduce emissions

A new approach for static NOx measurement in PTI

NOx emissions in vehicles are currently only controlled through the homologation process. There is a lack of knowledge to assess and control real NOx emissions of vehicles reliably. Even if vehicles in EU-27 are subject to Periodical Technical Inspection (PTI), NOx are not among the pollutants currently being controlled. For PTIs, tests need to be simple, quick, inexpensive, representative, and accurate. Ideally, tests need to be carried out under static conditions, without the need for a power bench or complex equipment. In this paper, a new approach for measuring NOx in PTI is proposed. The method has been developed and validated at a PTI Spanish station to ensure feasibility and repeatability. This method is based on the relationship between the “% engine load” value and exhaust NOx concentration at idle engine speed. Starting from the state of minimum possible power demand in a vehicle (idling and without any consumption), a load state with an average 98% increase in engine power demand is generated by connecting elements of the vehicle’s equipment. The relationship between power demand (through the “% engine load” value) and NOx concentration is then analyzed. The quality and representativity of this relationship have been checked with a p-value lower than 0.01. The method has been compared with a different NOx measurement technique, based on the simulation on a test bench and the ASM 2050 cycle, showing better performance in terms of repeatability and representativeness. The “% engine load” dispersion with the new approach is 7%, which ensures the reliability and repeatability of the method. The results show that the proposed method could be a valuable tool in PTI to detect high NOx emitting vehicles and to obtain information from the diesel vehicles fleet. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

Theoretical and experimental investigation of a CDI injection system operating on neat rapeseed oil - feasibility and operational studies

This thesis presents the work done within the PhD research project focusing on the utilisation of plant oils in Common Rail (CR) diesel engines. The work scope included fundamental experimental studies of rapeseed oil (RSO) in comparison to diesel fuel, the feasibility analysis of diesel substitution with various plant oils, the definition and implementation of modifications of a common rail injection system and future work recommendations of possible changes to the injection system. It was recognised that neat plant oils can be considered as an alternative substitute for diesel fuel offering a natural way to balance the CO2 emissions. However, due to the differences between diesel and plant oils, such as density, viscosity and surface tension, the direct application of plant oils in common rail diesel engines could cause degradation of the injection process and in turn adversely affect the diesel engine’s performance. RSO was chosen to perform the spray characterisation studies at various injection pressures and oil temperatures under conditions similar to the operation of the common rail engine. High speed camera, Phase Doppler Anemometry and Malvern laser techniques were used to study spray penetration length and cone angle of RSO in comparison to diesel. To study the internal flow inside the CR injector the acoustic emission technique was applied. It was found that for oil temperatures below 40°C the RSO viscosity, density and surface tension are higher in comparison to diesel, therefore at injection pressures around 37.50 MPa the RSO spray is not fully developed. The spray penetration and cone angle at these spray conditions exhibit significant spray deterioration. In addition to the lab experiments, KIVA code simulated RSO sprays under CR conditions. The KH-RT and RD breakup models were successfully applied to simulate the non-evaporating sprays corresponding to the experimental spray tests and finally to predict i real in-cylinder injection conditions. Numerical results showed acceptable agreement with the experimental data of RSO penetration. Based on experimental and numerical results it was concluded that elevated temperature and injection pressure could be the efficient measures to overcome operational obstacles when using RSO in the CR diesel engine. A series of modifications of low- and highpressure loops was performed and experimentally assessed throughout the engine tests. The results revealed that the modifications allowed to run the engine at the power and emission outputs very close to diesel operation. However, more fundamental changes were suggested as future work to ensure efficient and trouble-free long-term operation. It is believed that these changed should be applied to meet Euro IV and V requirements

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand