Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively

Driver evaluation in heavy duty vehicles based on acceleration and braking behaviors

In this paper, we present a real-time driver evalua-tion system for heavy-duty vehicles by focusing on the classifica-tion of risky acceleration and braking behaviors. We utilize animproved version of our previous Long Short Memory (LSTM)based acceleration behavior model [10] to evaluate varyingacceleration behaviors of a truck driver in small time periods.This model continuously classifies a driver as one of six driverclasses with specified longitudinal-lateral aggression levels, usingdriving signals as time-series inputs. The driver gets accelerationscore updates based on assigned classes and the geometry ofdriven road sections. To evaluate the braking behaviors of atruck driver, we propose a braking behavior model, which usesa novel approach to analyze deceleration patterns formed duringbrake operations. The braking score of a driver is updated foreach brake event based on the pattern, magnitude, and frequencyevaluations. The proposed driver evaluation system has achievedsignificant results in both the classification and evaluation ofacceleration and braking behaviors

Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors

n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness

Estimating soot emission in diesel engines using gated recurrent unit networks

In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles

Araç Takip Sistemi Verilerini Kullanarak Elektrikli ve Hibrit Taşıtlar için Enerji Yönetim Sistemi Algoritmalarının Optimizasyonu ve Ticari/Kamusal Kullanıma Yönelik Motorlu Taşıtlar için Optimal Hibritleştirme Alternatiflerinin Değerlendirilmesi

TÜBİTAK MAG Proje01.03.2019Bir yandan toplu tasımada kullanılan sehir içi otobüsler ve servisler, öte yandan her gün yüzkilometrenin üzerinde yol kateden çöp kamyonları ve kurye araçları çok miktarda yakıttüketmektedir. Bu proje kapsamında, Ankara içinde yolcu tasımacılıgında görev yapanbelediye otobüsleri üzerinden yogun veri toplama ve simülasyon faaliyetlerini içerenanalizlerin sonuçlarına dayanarak, belediyelerin ve Ulastırma Bakanlıgı?nın gelecekte içtenyanmalı motora sahip tasıtlar yerine elektrikli ve/veya hibrit tasıtların kullanması halinde,saglanabilecek yakıt tasarrufunun analizine yönelik bir arastırma yapılmıstır. Baska birdeyisle, mevcut tasıtların ya elektrikli araca ya hibrit araca dönüsümü yapılarak ve bunlarınenerji yönetim algoritmalarını (proje esnasında elde edilme yöntemi çözümlenen sürüsçevrimlerine göre) optimize etmek suretiyle, teorik olarak, ne kadar yakıt tasarrufuyapılabilecegi hesaplanmıstır.Proje esnasında sürüs çevrimleri bir araç takip sistemi üreticisi ile ortak çalısma yürütülerekelde edilmistir. Ayrıca bir otobüs üreticisiyle de proje ekibi veri toplayarak araç takip üreticisitarafından saglanan verilerin validasyonu yapmıstır. Proje esnasında, özgün bir yöntemle,araç takip sistemi verileri kullanılarak ülkemizin farklı sehirleri için geçerli sürüs çevrimlerinin(driving cycle) belirlenmesinde kullanılabilecek yöntemler gelistirilmis ve sonrasında buyöntemler hibritlestirme analizinde kullanılmıstır.Üstteki amaçlar dogrultusunda, bahsi geçen tasıtların güç dizini ve tasıt dinamigi modellerisanal ortamda kurulmus. Elektrik-hibrit tasıtların enerji yönetim sistemlerinin algoritmalarıliteratürden arastırılmıs ve özellikle Esdeger Enerji Minimizasyon Yöntemi (EEMY) veDinamik Programı tabanlı yöntemler gibi gelismis yöntemlerin, daha basit olan kural tabanlıyöntemlere göre ne miktarda fayda saglayacagı konusunda analizler yürütülmüstür. ÖzellikleEEMY nin gerçek zamanlı sürüs çevrimine göre güncellenmesi tabanlı özgün bir yöntemgelistirilmistir. Bu yöntem kullanılarak % 50 ye varan yakıt tüketimi tasaruffu yapmanınmümkün oldugu tespit edilmistir. Yöntem kapsamında, araç takip sistemlerinin kullanımı ileenerji yönetim sistemi parametrelerinin trafik yogunluk bilgisine göre uyarlanmasısaglanmıstır. Baska bir deyisle, sanal ortamda, trafige yeni katılan bir aracın teorik olarakbulundugu yol segmenti için hız zaman grafiginin ne sekilde olacagı yakın geçmiste bu yolsegmentinde seyahat etmis araçların araç takip sistemi verileri kullanılarak öngörülerek enerjisarfiyatı en aza indirilmistir. Dolayısıyla, sürüs çevrimleri kullanılarak elde edilmisalgoritmaların kalibrasyonunu bu yol segmenti için yapılmıstır.Projenin son asamasında üç tekerlekli, ön tekerlekleri elektrikli jant motorlu, arka tekerlegiiçten yanmalı motor tahrikli paralel hibrit mimariye sahip bir tasıt üretilmistir. Bu tasıtın tasıtkontrol bilgisayarına projenin teorik asamaları esnasında tasarlanan hibrit enerji yönetimalgoritmaları kodlanmıstır. Hacettepe kampüsünde hız-zaman verileri toplanmıstır. Elde edilenverilerden basitlestirilmis bir sürüs çevrimi türetilmistir. Kontrollü deneylerin yapılabilmesiadına Hacettepe Ü. Otomotiv Laboratuvarında bulunan dinamometre deney düzenegiüzerinde özgün bir test prosedürü gelistirilmistir. Test esnasında içten yanmalı motorun veelektrikli jant motorlarının (sökülüp paralel bir araca monte edilerek) dinamometretamburlarının es zamanlı olarak tahrik etmesi mümkün kılınmıstır. Deneysel sonuçlar,projenin teorik asamalarında da gösterildigi gibi, hibrit tasıtların kullanılması halinde, EEMYnin, diger yöntemlere göre, çok daha fazla yakıt tasarrufu saglayabildigini göstermektedir.Hem teorik hem de pratik sonuçlar sürüs çevrimi hakkında bilgi sahibi olundugunda (ki busehir için yolcu tasımacılıgında kullanılan tasıtlar için son derece geçerlidir), üstte bahsi geçenhibrit enerji yönetim algoritmasının yakıt tüketimini azaltmada büyük potansiyeli oldugunukanıtlamaktadır.Both city buses and personnel services that are used in public transportation on the one hand andgarbage trucks and courier vehicles on the other travel more than a hundred kilometers per dayand consume substantial amounts of fuel. Within the scope of this project, based on results basedon intensive data collection and simulation activities related to municipal buses serving the city ofAnkara, a research study about the analysis of potential fuel savings, that could be provided ifmunicipalities and the Ministry of Transport used electric and/or hybrid vehicles instead of vehicleswith internal combustion engines, has been carried out. In other words, the amount of fuel savingshas theoretically been calculated by transforming the existing vehicles to either electric or hybridvehicles and by optimizing their energy management algorithms (according to the driving cyclesobtained during the project).During the project, driving cycles were obtained by collaborating with a vehicle tracking systemmanufacturer. In addition, together with a bus manufacturer, the project team collectedexperimental data and validated the data provided by the vehicle tracking system manufacturer.Original methods were developed to determine driving cycles for the different cities of our countryand were used in the subsequent hybridization analyses.For the above mentioned purposes, powertrain and vehicle dynamics models of aforementionedvehicles were established in the virtual environment. The algorithms of the energy managementsystems of the electric/hybrid vehicles have been investigated from the literature and analyseshave been carried out to determine the benefits of advanced methods such as the EquivalentConsumption Minimization Strategy (ECMS) and Dynamic Programming based methodscompared to simpler rule based methods. In particular, an original method based on adaptive-ECMS, which consists in scheduling control parameters according to the real–time driving cyclehas been developed. By using this method, it was found that fuel consumption savings up to 50%fuel consumption were possible. Within the scope of the method, the parameters of the energymanagement system were adapted to the traffic density information provided by the vehicletracking system. In other words, in the virtual environment, the speed time graph for the roadsegment where the ego vehicle is about to travel is predicted theoretically, using the vehicletracking system data of vehicles that travelled on the same road segment in the recent past.Thereby, the calibration of the hybrid energy system algorithms is made possible by using drivingcycles calculated for the road segment under interest.In the final stage of the project, a three–wheeled parallel hybrid vehicle with electric hub motorsat the front wheels and an internal combustion engine driven rear wheel has been constructed.The hybrid energy management system algorithms designed during the theoretical stages of theproject, have been coded to the vehicle control computer of this vehicle. Speed – time data havebeen collected in the Hacettepe University campus and a simplified driving cycle has beenobtained according to the data. In order to carry out controlled experiments, a unique testprocedure has been developed on the dynamometer test setup in the Hacettepe UniversityAutomotive Laboratory. During the tests, internal combustion engine and electric hub motors(which were disassembled and mounted to a separate vehicle chassis) were made to drivedynamometer drums simultaneously. Experimental results show that ECMS can provide muchmore fuel savings than other methods as also shown in the theoretical stages of the project. Boththe theoretical and practical results prove that the hybrid energy management algorithmmentioned above has a great potential in reducing fuel consumption, when the driving cycle ismore or less known, which is quite valid for vehicles used in urban passanger transportation thatgenerally have fixed routes.Keywords: Electric Vehicle, Hybrid Vehicle, Energy Management System, Equivalent EnergyMinimization Strateg

Diesel engine NOx emission modeling with airpath input channels

Stringent international regulations in terms of emissions necessitate more efficient transient calibration procedures for diesel engines which in turn implies utilization of dynamic models of the combustion process. In this paper, a novel input design framework in terms of multi-sweep chirp signals is developed and airpath input channels are excited by designed chirp signals. Linear and nonlinear system identification methods are utilized to model NOx emissions with airpath input channels. Experimental results show that while linear identification techniques provide poor performance in terms of training and validation fits, nonlinear models achieve remarkable performance in training and validation fits

Towards driving-independent prediction of fuel consumption in heavy-duty trucks

Heavy-duty vehicles are among the major contributors to greenhouse gas emissions in addition to their high energy consumption. Thus, modeling their fuel consumption (FC) is of prime importance to the limitation of these environment-harmful emissions and energy saving. In this paper, we propose a data-driven model based on artificial neural networks (ANN) to predict the average FC in heavy-duty cloud-connected Ford trucks. In particular, we propose a driving-independent model based only on the vehicle weight and road grade. Owing to idling situations, the average FC includes some outliers; we propose to remove these outliers based on the weight-normalized average FC to take the changing vehicle weights into consideration. Initially, the model uses the percent torque, vehicle speed, vehicle weight, and road slope as predictors. In that case, our proposed model achieved an R2 of 0.96 outperforming the results in the literature by a significant margin. Next, we investigate the cases of excluding the torque and vehicle speed in order to assess the model's effectiveness when using only those predictors which are independent of the vehicle dynamics. In these challenging cases, our proposed model still maintains an R2 above 0.8