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Degradation of the deNOx performance has been found in in-use heavy-duty vehicles with a urea-SCR system in Japan. The causes of the degradation were studied, and two major reasons are suggested here: HC poisoning and deactivation of pre-oxidation catalysts. Hydrocarbons that accumulated on the catalysts inhibited the catalysis. Although they were easily removed by a simple heat treatment, the treatment could only partially recover the original catalytic performance for the deNOx reaction. The unrecovered catalytic activity was found to result from the decrease in conversion of NO to NO2 on the pre-oxidation catalyst. The pre-oxidation catalyst was thus studied in detail by various techniques to reveal the causes of the degradation: Exhaust emission tests for in-use vehicles, effect of heat treatment on the urea-SCR systems, structural changes and chemical changes in active components during the deactivation were systematically investigated.

Off-road dump truck body is exposed to abrasive wear during handling of granular materials. The wear rate of body of dump truck has direct influence on maintenance and replacement during its service process. In this paper the discrete element method (DEM) is used to simulate the granular materials of dump truck. The wear of body floor during one dumping process can be achieved by cosimulation of FEM-DEM. The wear depth variation of body has the stochastic characteristic which can be modeled by Geometric Brownian Motion (GBM). The two parameters in the stochastic differential equation, drift coefficient and diffusion coefficient, can be estimated by the wear depth measuring data. It is possible to quantitatively predict the wear evolution of every grid point of the body floor by solving this stochastic differential equation. The simulation result of the wear model is helpful to optimize design of off-road dump truck body.

Partially automated driving involves the relinquishment of longitudinal and/or latitudinal control to the vehicle. Partially automated systems, however, are fallible and require driver oversight to avoid all road hazards. Researchers have expressed concern that automation promotes extended eyes-off-road (EOR) behavior that may lead to a loss of situational awareness (SA), degrading a driver’s ability to detect hazards and make necessary overrides. A potential countermeasure to visual inattention is the orientation of the driver’s glances towards potential hazards via cuing. This method is based on the assumption that drivers are able to rapidly identify hazards once their attention is drawn to the area of interest regardless of preceding EOR duration. This work examined this assumption in a simulated automated driving context by projecting hazardous and nonhazardous road scenes to a participant while sitting in a stationary vehicle.

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc.

This paper presents payload estimation based on experimental friction coefficients identification. To estimate exact payload mass, dynamic mathematical model such as actuator dynamics and front linkage dynamics is derived by using Newton-Euler method. From the dynamic equation, nonlinear terms are analyzed and transformed. And a friction model is derived from the experiments with various conditions which have three states; boom joint angle, head and rod chamber pressures. It can identify friction coefficients and compensate friction forces. In addition, the accuracy of payload estimation system is verified through the field test.