Experimental study on performance characteristics of cold storage heat exchanger for ISG vehicle

The ISG (Idle Stop and Go) system isvery useful in the automobile industry because it increases fuel consumption and reduces green house gas emissions. However, when the engine is on standby, the air-conditioning system does not work due to compressor inactivity, causing thermal discomfort to passengers. This study examines the thermal storage system, which is a cold storage heat exchanger integrated with a current evaporator. The experiments were conducted for an optimum cold storage heat exchanger design with various fin heights and densities, a number of stacking evaporator plates, refrigerant flow circuits inside the evaporator, and PCMs (Phase Change Materials) in the heat exchanger. The effects of coldness-release performance were examined with various ambient temperatures and air flow volume rates to the cold storage heat exchanger. The visualization of PCM’s freezing and melting was conducted with the cold storage heat exchanger. From the results, we found that the air discharge temperature of the air-conditioning system that was applied to the optimum cold storage heat exchanger was delayed around 540 seconds compared to the current air-conditioning system to reach 24 °C. Thus we can say that the cold storage heat exchanger integrated with an evaporator is an effective solution for ISG vehicles in maintaining thermal comfort in vehicle cabins during short engine stops.     

MPC-BASED steering control for backward-driving vehicle using stereo vision

We propose a steering control algorithm for autonomous backward driving in a narrow corridor. Passable spaces are detected using a stereo camera, and the steering angle is controlled by a model predictive controller (MPC). For passable space detection, an UV-disparity map is calculated from the original disparity map. Information regarding passable spaces collected by the stereo camera is used in steering control. Backward driving requires the driver’s preemptive actions, which can be learned by experience because of the non-intuitive responses (the initial motion of the vehicle is opposite to the driver’s steering angle input). This occurs because a backward-driving vehicle is a non-minimum phase system. One of the most popular steering control algorithms is Stanley method, which is based on the feedback of lateral displacement error and heading angle error. The method is very intuitive and works well for forward driving, but it exhibits significant undershoot for backward driving cases. Furthermore, the method does not explicitly consider any constraints on control inputs and states. We designed a steering controller based on the MPC technique that requires future information but can handle constraints explicitly. Because we have near-future information from the stereo camera under limited passable spaces, MPC can be effectively implemented. We performed several simulations and experiments to show the performance and superiority of the suggested method over a simple feedback-based control algorithm.     

Direct tire force generation algorithm based on non-iterative nonlinear inverse tire model

The function of vehicle dynamics control system is adjusting the yaw moment, the longitudinal force and lateral force of a vehicle body through several chassis systems, such as brakes, steering and suspension. Individual systems such as ESC, AFS and 4WD can be used to achieve desired performance by controlling actuator variables. However, integrated chassis control systems that have multiple objectives may not simply achieve the desired performance by controlling the actuators directly. Usually those systems determine the required tire forces in an upper level controller and a lower level controller regulates the tire forces through the actuators. The tire force is controlled in a recursive way based on vehicle state measurement, which may not be sufficient for fast response. For immediate force tracking, we introduce a direct tire force generation method that uses a nonlinear inverse tire model, a pseudo-inverse model of vehicle dynamics and the relationship between longitudinal force and brake pressure.     

Multi-Component Model of Diesel Sprays Under High Injection Pressure

The combustion efficiency of a diesel engine depends not only on spray characteristics but also on fuel-air mixing characteristics. Based on the original spray model, a new spray model is established in this paper to accurately predict the diesel spray, and then a multi-component evaporation model is added into it. The model takes the influence of component concentration gradient and species on its evaporation rate in the liquid phase into account. This paper studies the spray characteristics (spray penetration, spray angle and spray morphology) and fuel-air mixing characteristics (spray area, spray volume and air entrainment mass) using the spray model, and the results are compared with the experimental results. The comparison shows that the simulated spray penetration and spray angle are close to the experimental results with the average deviations less than 3%. Moreover, this paper studies the spray area, spray volume and air entrainment using empirical formula under different conditions. And the maximum deviations of the spray volume, spray area and air entrainment mass are less than 5% as compared with the test values. Overall, this spray model can predict the diesel spray characteristics and fuel-air mixing characteristics under high injection pressure accurately.     

Feature Selection,Deep Neural Network and Trend Prediction

The literature generally agrees that longer-horizon (over a month) predictions make more sense than short-horizon ones. However, it’s an especially challenging task due to the lack of data (in unit of long horizon) and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis (SRA) algorithm to constrained SRA (cSRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network (DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.     

Moving between mobility cultures: what affects the travel behavior of new residents?

This paper analyzes the complex interdependencies between residential relocation and daily travel behavior by focusing on modal change. To help explain changes in daily travel patterns after a long distance move between cities the concept of urban mobility cultures is introduced. This comprehensive approach integrates objective and subjective elements of urban mobility, such as urban form and socio-economics on the one hand, and lifestyle orientations and mode preferences on the other, within one socio-technical framework. Empirically, the study is based on a survey conducted among people who recently moved between the German cities Bremen, Hamburg and the Ruhr area. Bivariate analyses and linear multiple regression models are applied to analyze changes in car, rail-based and bicycle travel. This is done by integrating variables that account for urban mobility cultures and controlling for urban form, residential preferences and socio-demographics. A central finding of this study is, that changes in the use of the car and rail-based travel are much more dependent on local scale, such as neighborhood type and residential preferences, whereas cycling is more affected by city-wide attributes, which we addressed as mobility culture elements.     

A multiscale superpixel-level salient object detection model using local-global contrast cue

The goal of salient object detection is to estimate the regions which are most likely to attract human’s visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the effectiveness of the proposed model.     

The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study

Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.     

Capacity constrained accessibility of high-speed rail

This paper proposes an enhanced measure of accessibility that explicitly considers circumstances in which the capacity of the transport infrastructure is limited. Under these circumstances, passengers may suffer longer waiting times, resulting in the delay or cancellation of trips. Without considering capacity constraints, the standard measure overestimates the accessibility contribution of transport infrastructure. We estimate the expected waiting time and the probability of forgoing trips based on the M/G^B/1 type of queuing and discrete-event simulation, and formally incorporate the impacts of capacity constraints into a new measure: capacity constrained accessibility (CCA). To illustrate the differences between CCA and standard measures of accessibility, this paper estimates the accessibility change in the Beijing–Tianjin corridor due to the Beijing–Tianjin intercity high-speed railway (BTIHSR). We simulate and compare the CCA and standard measures in five queuing scenarios with varying demand patterns and service headway assumptions. The results show that (1) under low system loads condition, CCA is compatible with and absorbs the standard measure as a special case; (2) when demand increases and approaches capacity, CCA declines significantly; in two quasi-real scenarios, the standard measure overestimates the accessibility improvement by 14–30 % relative to the CCA; and (3) under the scenario with very high demand and an unreliable timetable, the CCA is almost reduced to the pre-BTIHSR level. Because the new CCA measure effectively incorporates the impact of capacity constraints, it is responsive to different arrival rules, service distributions, and system loads, and therefore provides a more realistic representation of accessibility change than the standard measure.     

Who escort children: mum or dad? Exploring gender differences in escorting mobility among parisian dual-earner couples

The present article looks to pinpoint explanatory factors for the sharing of escorting of children in dual-earner families. It proposes a detailed analysis of inequalities and interactions in dual-earner families when it comes to escorting children by taking into account the characteristics of trips to and from school for children, the characteristics of the parents’ occupations, and the characteristics of the household. Compared with earlier research, the model considers more detailed data about the escorts’ jobs, such as specific working hours, which provide a better understanding of the constraints on parents and insight into the choices made when both parents are in a position to escort their children. The findings depart somewhat from those of earlier work on the question because more specific data are considered. They show a marked gender inequality in escorting because mothers in dual-earner families do more than two-thirds of the escorting. But the factors explaining the sharing of escorting act almost symmetrically for both parents, with the effect of work starting and finishing times being preponderant. These models confirm that the inequality kicks in ahead of this: mothers in dual-earner households are more often than fathers in jobs with short working hours and which are more compatible with escorting.