Design factor optimization of 3D flash lidar sensor based on geometrical model for automated vehicle and advanced driver assistance system applications

Sensor technologies have been innovated and enhanced rapidly for highly automated vehicle and advanced driver assistance systems (ADAS) in automotive industry; however, in order to adopt sensors into mass production vehicle in near future, various requirements should be satisfied such as cost, durability, and maintainance without any loss of overall performance of the sensors. In this sense, a 3D flash lidar is one of primising range sensors because of no moving parts, compact package, and precise measure for distance by using a laser. In spite of the several advantages, the 3D flash lidar is not commonly used in automotive industry because it is quite expensive for adoption and it is manufactured with only general purpose currently; therefore, the cost reduction and optimal design to satisfy various purposes of ADAS or autonomous driving should be accomplished. In this paper, we propose a novel approach for design factor optimization of the 3D flash lidar based on a geometrical model by using structural similarity between the 3D flash lidar and 2D digital camera. In particular, focal length and area of a receiver (focal plane array and read-out integrated circuit) which directly affect on sensor performance (field of view and maximum detection range) are optimized as the design factors. From the optimization results in simulation, we show that optimal design factors according to various purposes required in ADAS could be easily determined and the sensor performances could be evaluated before manufacturing. It will reduce temporal and economic burdens for design and manufacturing in development process.     

Investigation of soot formation in Diesel-GTL fuel blends under quiescent conditions

In this study, a visual investigation of sprays and flames is performed, and soot formation in Diesel-GTL fuel blends is studied in a specially designed quiescent constant-volume chamber under various ambient gas temperatures and O₂ concentrations. Similar to the case of soot formation during diesel fuel combustion, the sooting zone during the mixing-controlled combustion of Diesel-GTL blends is located in the leading portion of the jet boundaries. Auto-ignition delay and soot concentration decrease with an increase of GTL content in the fuel blend. Soot also decreases with lower O₂ concentration, higher injection pressure, and lower ambient gas temperature. The lack of soot formation at lower O₂ concentrations and lower temperatures suggests that Diesel-GTL fuel blends can be successfully utilized in low-temperature diesel combustion technologies that are currently being developed. Furthermore, this mixing controlled combustion method with Diesel-GTL blends can be used to modulate various engine operation parameters, and therefore to simultaneously reduce the formation of soot and NOx within a wide range of diesel engine loads.     

Optimal design of an engine mount using an enhanced genetic algorithm with simplex method

This study provides an analysis of the applications of optimization routines for designing fluid mounts. After summarizing the concept of fluid mounts and their dynamic characteristics, we review the importance of the notch and resonance peak that occur in dynamic stiffness of fluid mounts. Fluid mounts are tuned for specific application so that their notch frequency coincides with the disturbance frequency, by selecting the proper parameters for the mount. Additionally, the mount parameters are selected such that the notch remains as deep (close to zero) as possible and the resonance peak is kept as short as possible. The notch depth and resonance peak present opposing requirements for the selection of mount parameters in the sense that lowering one will result in increasing the other. Using a bond graph model, this study will evaluate the effect of various parameters on the mount notch depth and resonance peak height characteristics. The results show that different parameters can have a varying effect on the notch frequency and depth, as well as the resonance frequency and peak height. The results of the study are extended by examining the effectiveness of two different optimization methods—namely, the Enhanced Genetic Algorithm (EGA) and Sequential Quadratic Programming (SQP)—for selecting the combination of parameters that can yield the deepest notch and shortest resonance peak. Using two different design cases, the study shows that SQP exhibits much more sensitivity to the initial conditions that are selected for the mount parameters than EGA. Both methods, however, are able to converge to an optimal solution within the constraints that are selected for the parameters. For both cases, EGA is able to converge to the set of parameters that provide a deep notch and a short resonance peak.     

Experimental study on the characteristics of nano-particle emissions from a heavy-duty diesel engine using a urea-SCR system

Particulate matter in diesel engine exhaust, particularly nano-particles, can cause serious human health problems including diseases such as lung cancer. Because diesel nano-particle issues are of global concern, regulations on particulate matter emissions specify that not only the weight of particulate matter emitted but also the concentration of nanoparticles must be controlled. This study aimed to determine the effects on nano-particle and PM emissions from a diesel engine when applying a urea-SCR system for NO_x reduction. We found that PM weight increases by approximately 90% when urea is injected in ND-13 mode over the emission without urea injection. Additionally, PM weight increases as the NH₃/NO_x mole ratio is increased at 250 °C. In SEM scans of the collected PM, spherical particles were observed during urea injection, with sizes of approximately 200 nm to 1 μm. This study was designed to determine the conditions under which nano-particles and PM are formed in a urea-SCR system and to relate these conditions to particle size and shape via a quantitative analysis in ND-13 mode.     

A comparative study on the power characteristics and control strategies for plug-in hybrid electric vehicles

A comparative study was performed on two types of plug-in hybrid electric vehicles (PHEVs): the GM Volt and the Toyota Prius Plug-in Hybrid. First, the powertrain models of the two vehicles were derived. Based on the dynamic models, a detailed component control algorithm was developed for each PHEV. Specifically, a control algorithm was proposed for motor generator 1 (MG1) and MG2 to achieve optimal engine operation. Additionally, an energy management strategy for selecting the operation mode was developed from the viewpoint of fuel economy, battery state of charge and vehicle velocity. Using the dynamic model of the control algorithm for each PHEV, simulations were performed, and the simulation results were verified by comparing them with those obtained using the Powertrain System Analysis Toolkit simulator for the plug-in Prius. Based on the simulation results, a comparative study was performed, and it was found that the role and capacity of MG1 and MG2 and the mode selection algorithm must be determined depending on the configuration of the PHEV.