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

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

A Big Data Application for Low Emission Heavy Duty Vehicles

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO2 emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%

Optimization-oriented high fidelity NFIR models for estimating indicated torque in diesel engines

In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively

Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis

This study develops a novel semi-supervised approach for detecting Air Pressure System (APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning (ML) models: Long Short-Term Memory Autoencoder (LSTM-AE) and Transformer for Anomaly Detection (TranAD), and enhancing their performance with human expertise. To tackle the failure detection problem, a dataset comprising 30 days of operational time-series data from 110 healthy vehicles with no recorded APS issues and 30 vehicles that experienced APS failures requiring road assistance was acquired. Several preprocessing steps are proposed and three key features are extracted as APS health indicators. These features are then utilized both in human expert analysis (HEA) and training of ML models. When compared to HEA, both LSTM-AE and TranAD models exhibit superior performance individually in APS failure detection, achieving F1 scores of 0.75 and 0.79 respectively, and the same accuracy of 91.4%. Further, the integration of HEA with those ML models enhances model effectiveness in all experimental results, especially in reducing false alarms that cause customer dissatisfaction. The TranAD model combined with human expert analysis achieved the best performance with an unprecedented 0.82 F1 score and 92.8% accuracy. In addition to presenting a new methodology for failure detection, this paper suggests a way for more efficient and reliable predictive maintenance practices for HDVs

Fuel consumption classification for heavy-duty vehicles: a novel approach to identifying driver behavior and system anomalies

In this paper, we propose a fuel consumption classification system for heavy-duty vehicles (HDVs) based on two machine learning models that categorize sections of driving data as normal or high and inlier or outlier fuel consumption. A dataset of 606 naturalistic driving records collected from 57 different heavy-duty trucks with varying carry loads is generated and utilized. Proposed models are trained to categorize driving sections taking into consideration of vehicle weight and road slope, which are the two major factors affecting the fuel consumption of a heavy-duty truck. Results show an accuracy of 92.2% in high fuel consumption prediction and an F1 score of 0.78 in outlier prediction using the bagged decision trees models. The proposed approach provides an advanced categorization of driving data in terms of fuel economy. It has substantial potential to determine driving behavior anomalies or system faults that may cause excessive energy consumption and emissions in HDVs

Air pressure system failures detection using LSTM-autoencoder

The reliability of Heavy-Duty Vehicles (HDVs) is critical for continuous operations in sectors like transportation and logistics. However, the complexity of these vehicles’ subsystems, including the Air Pressure System (APS), poses significant challenges where failures lead to costly downtimes and safety risks. This paper introduces a novel semi-supervised anomaly detection approach based on a Long Short-Term Memory Autoencoder (LSTM-AE) model to identify APS failures in HDVs. Leveraging 30 days of operational time-series data from 140 vehicles, of which 30 experienced APS failures, our study presents a semi-supervised formulation of the problem bypassing the limitations of supervised classification and addressing the scarcity of labeled data in the real-world scenarios. After applying several essential preprocessing steps, the proposed model was rigorously trained and validated to ensure robustness. It achieved an F1 score of 0.75 with a corresponding accuracy of 91.4%. The proposed framework in this research promotes enhanced vehicle uptime and improved safety standards, providing practical implications for both HDV manufacturers and operators