Numerical methodology for evaluation the combustion and emissions characteristics on WLTP in the light duty dual-fuel diesel vehicle

The worldwide aim of reducing environmental impact from internal combustion engines bring more and more stringent emission regulations. In 2017 by EU has been adopted new harmonized test procedure called WLTP. In general terms this test was designed for determining the levels of harmful emissions and fuel consumption of traditional and hybrid cars. This procedure contains specific driving scenarios which representing real-life driving patterns. Test cycles contain vehicle velocity versus time profiles and directly in powertrain analysis on the test benches cannot be used. In order to back calculate drive cycles to engine rpm versus torque profiles a simple longitudinal vehicle dynamics method was used in this paper. Moreover, in order to determine most representative engine operation points duing WLTP a density based grid clustering method was implemented. The experimental part of the study focuses on the comparative evaluation of the effect of various diesel to LPG substitution ratios (0% LPG, 10% LPG, 20% LPG and 30% LPG) on combustion and emission characteristics of dual-fuel diesel engine.

The Correlation Between the Statistical Pass-By Index Values and the Total Number of Vehicle Passes

In this study, the traffic noise degradation in asphalt pavements was analysed using the ‘Statistical Pass-By method’. The sound levels of two surfaces were monitored during 9 and 12 years of service, respectively. By comparing the dependencies of the maximum A-weighted sound pressure level on logarithm of vehicle velocity, an increase in the sound level was found at all recorded speeds. Following an analysis of sound levels, as combined with the statistical pass-by index (SPBI) calculated versus age (expressed in vehicles), it was determined that the noise is an increasing power function of SPBI values on vehicle passes, based on an approximation of noise level adjustment to a reference temperature of 20 °C (using a coefficient of 0.06 for asphalt concrete surface AC11 and - 0.03 for mastic asphalt SMA11). The adjusted traffic noise degradation model showed that the SMA11 surface has a higher resistance to acoustic degradation than AC11 surface.

A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting

Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast; and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

A Fast Calculation Method for Improving the Steering Arm of Mining Trucks with Macpherson Suspension

The steering arm has recently been frequently broken in a kind of mining truck with Macpherson suspension. To accelerate replacing the broken parts and minimize the economic cost, a fast calculation method for improving the steering arm is proposed in this paper. In this method, the forces on the steering arm are calculated by quasi-static analysis under a low vehicle velocity. Dynamic characteristics of the tire and road are partly included by considering the ranges of the rolling resistance coefficient and friction coefficient from the empirical values, which determines the torque on the steering arm under extreme conditions. The rigid–flexible coupling model for the left steering mechanism in ANSYS Workbench is established and solved to obtain the distribution stress on the steering arm under extreme conditions. Then, the reliability of the simulation results based on this fast calculation method is verified by the experiment. After determining an improvement scheme considering the economic and time cost, the satisfactory strength is obtained. The results illustrate that the strength of the improved steering arm has nearly doubled. Finally, the effectiveness of the improved steering arm is demonstrated by the users’ feedback after it is manufactured, installed, and used.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.