Robust Scalable Vehicle Control via Non-Dimensional Vehicle Dynamics

A temporal and spatial re-parameterization of the linear vehicle Bicycle Model is presented utilizing non-dimensional ratios of vehicle parameters called p-groups. Investigation of the p-groups using compiled data from 44 published sets of Vehicle Dynamics reveals a normal distribution about a line through p-space. The normal distribution suggests numerical-values for an 'average' vehicle and maximum perturbations about the average. A state-feedback controller is designed utilizing the p-space line and the expected p-perturbations to robustly stabilize all vehicles encompassed by the normal distribution of vehicle parameters. Experimental verification is obtained using a scaled vehicle.     

Bayesian updating of trip generation data: Combining national trip generation rates with local data

With traffic impact analyses and impact fee assessment becoming more popular, the need for accurately estimating the trip generation rate of a proposed development is becoming more important. An overwhelming percentage of state transportation agencies depend either partly or entirely on the ITETrip Generation Report to predict the traffic that will be attracted to and/or produced from a proposed development. However, the rates obtained from the ITE publication have been derived from data collected throughout the United States. They represent a national average and fail to take into account the local trip generation characteristics that the site under consideration might have. This paper establishes a methodology for obtaining more reliable local trip generation rates using Bayesian statistics. In this method, the ITE rates are assumed to be the prior information, which are updated using limited local trip generation data that are available. The method also allows for temporal updating, incorporating subjective judgment and using borrowed data in the updating procedure. Sample calculations in this paper illustrate the developed methodology.     

Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey

The debate over electric vehicles (EVs) pivots largely on issues of market demand: will consumers purchase a vehicle that provides substantially less driving range, yet can be refueled at home, than an otherwise comparable gasoline vehicle? Also, what role do other unique attributes of EVs play in the purchase decision? Most previous studies find that limited driving range is a serious market barrier; many of those same studies ignore or under-value other novel attributes. To probe these future consumer decision processes deeply and robustly, we first devised and conducted detailed, interactive and experiment-oriented interviews. Then, incorporating what we learned, we designed an innovative mail survey and administered it to 454 multi-car households in California. The four-stage mail survey included a video of EV use and recharging and other informational material, completion of a 3-day trip diary and map of activity locations, and vehicle choice experiments. In addition to propulsion systems, respondents made choices of body styles, driving ranges, and other features. We formalized and tested what we call the hybrid household hypothesis: households who choose EVs will be purposefully diversifying their vehicle holdings to achieve the unique advantages of different propulsion systems. The hypothesis is supported, given the assumptions in our experimental design. In fact, a significantly larger number of EVs are chosen than the minimum number that would support our hypothesis. We find that purchases of battery-powered EVs by hybrid households would account for between 7 and 18% of annual light duty vehicle sales in California. EVs sold to fleets and other households would be in addition to those identified by this study.     

Optimal Control of a Vehicle Suspension Incorporating the Time Delay between Front and Rear Wheel Inputs

An optimal control law for a vehicle suspension is developed using a discrete linear quadratic regulator framework. The time delay between the disturbance due to the road at the front and rear wheels is incorporated into the model, and it is shown that the optimal control law requires information gathered at the front wheels. A comparison is made between the optimal control law and a suboptimal one which does not incorporate front wheel road information.     

Optimization of blending oil with non-esterified biodiesel fuel using design of experiment at partial engine loads

Non-esterified bio-diesel fuel is more economically feasible than esterified one because of simple manufacturing process that only consists of filtering. Applicability of this fuel on diesel engine with electronic control system was tried and accomplished in a previous research. In this study, optimization adopting a fractional factorial design and response surface methodology was carried out at 25 % and 50 % of engine load in order to verify effectiveness of design of experiment for performance optimization of diesel engine. Pcr and IT mainly affected responses as specific fuel oil consumption and nitrogen oxides regardless of engine load according to the fractional factorial design. Estimations were 310.3 g/kWh of specific fuel oil consumption and 237 ppm of nitrogen oxides at 25 % load, and 233.2 g/kWh of specific fuel oil consumption and 730 ppm of nitrogen oxides at 50 % load according to the response surface methodology. As the results of verification tests, specific fuel oil consumption and NOx were respectively 300.4 g/kWh and 277 ppm at 25 % load, and 236.8 g/kWh and 573 ppm at 50 % load. Since there were small differences between estimations and verifications, adopting Box-Behnken method of the response surface methodology for performance optimization of diesel engine should be considered carefully.     

Fatigue and buckling analysis of automotive components considering forming and welding effects

Some vehicle components are developed by setting target weights to the gram level at their design stages to accomplish a lightweight design. Recently, there have been many studies that have focused on lightweight design through the use of ultra-high-strength steels. However, a lightweight design can face many challenges if the reliability of the analysis is not also secured at the design stage. Such challenges include difficulties in coupled analyses when the file formats are different among PAM-STAMP, ABAQUS, and NASTRAN. In this study, we developed a mapping interface that enables mapping between the file formats of various software programs. Buckling analysis was coupled to the forming analysis, in which pre-strain test data were applied in considering the material’s strain hardening, to evaluate the rigidity of the front lower control arm that controls the wheels and transfers loads. The influence of forming effects on endurance was evaluated, and residual stresses around the weld zone were calculated. A comparison of experimental and analytical results indicated that the proposed analysis was highly reliable.     

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.     

Effects of altitude on combustion characteristic during cold start of heavy-duty diesel engine

Altitude has a significant effect on combustion of heavy-duty diesel engines, especially during cold start. An experimental study on a heavy-duty diesel engine operating at different altitudes was conducted. Tests were based on a direct injection (DI) turbocharged diesel engine with intake and exhaust pressure controlled by the plateau simulation test system to stimulate altitude conditions including 0 m, 1000 m, 2000 m, 3000 m and 4000 m. Results indicated that the compression and expansion resistance moment reduced and the speed increased during the cranking period. The peak pressure of several cycles was increased during the start-up period; however, the expansion pressure dropped more and the indicated mean effective pressure (IMEP) reduced as the altitude rose. While at an altitude of over 2000 m, the peak pressure fluctuated obviously during the start-up period. The higher the altitude was, the more the fluctuation amplitude and cycle number increased and combustion instability enhanced, which resulted the start-up period time increasing at high altitude. When the altitude rose, the cycle-to-cycle variation of the peak pressure and speed fluctuation increased during the idle, the ignition and CA50 were delayed and the combustion duration was shortened. The effect of altitude on combustion characteristics of the diesel engine was more significant during the start-up period than during its idle period.     

Pedaling torque sensor-less power assist control of an electric bike via model-based impedance control

This paper proposes a method to assist human force acting on electric bike without using costly torque sensors via a model-based impedance control technique. In general, electric bikes are classified into two categories, i.e., pedelec electric bikes and throttle electric bikes. We focus on the system called a pedelec electric bike. It assists human pedaling force using the pedaling information, e.g., pedaling force or speed. To obtain the human’s pedaling information in real-time, it needs physical sensors such as a torque sensor and a velocity sensor. But, these sensors are expensive and weak against external loads. Also, since these sensors are fixed directly to the forced component in a bike system, there are the risks of damage. For these reasons, sensor-less control methods based on a disturbance observer have been studied so far. In this paper, we have proposed a pedaling torque sensor-less power assist method and have applied it to the experimental pedelec electric bike. A power assist control algorithm, designed by employing an impedance model, consists of a PI-type feedback controller, an inverse model-based feedforward controller, and a pedaling torque observer. Finally, we performed experiments and confirmed the effectiveness of a proposed power assist control method.