Effect of regenerative braking energy on battery current balance in a parallel hybrid gasoline-electric vehicle under FTP-75 driving mode

In recent years, a hybrid electric vehicle (HEV) has been considered a successful technology. Especially, in case of a full HEV, the motor can drive the vehicle by itself at low velocity or assist the engine at high load. To improve the hybrid electric vehicle’s efficiency, a regenerative braking system is also applied to recover from kinetic energy. In this study, an experimental control apparatus was set up with a parallel hybrid electric vehicle mounted on a chassis dynamometer to measure ECU (engine control unit) and MCU (motor control unit) signals, including the current and state of charge in the battery. In order to analyze regenerative braking characteristics, user define braking driving cycle was introduced and carried out using different initial velocities and braking times. The FTP 75 driving cycle was then adapted under different initial SOC (state of charge) levels. The experiment data was analyzed in accordance with the vehicle velocity, battery current, instant SOC level, motor RPM, engine RPM, and then vehicle driving mode was decided. In case of braking driving cycle, it was observed that SOC were increased up to 1.5 % when the braking time and the velocidy were 6 second and 60 km/h, respectively. In addition, using the FTP 75 driving cycle, mode 1 was most frequently operated at SOC 65 conditions in phase 1. In phase 2, due to frequent stop-go hills, percentage of mode 1 was increase by 22 %. Eventually, despite of identity, it was shown that the characteristics of phase 3 differed from phase 1 due to the evanishment of the effects of initial SOCs.     

Development of route information based driving control algorithm for a range-extended electric vehicle

A route information based driving control algorithm was developed for an RE-EV which consists of two motorgenerators, MG1 and MG2. A threshold power which controls the engine on/off to charge the battery was obtained by an optimization process using route information, such as the vehicle velocity and altitude. The threshold power allows the vehicle to travel to the final destination while making the final battery SOC close to SOC _low. Using the threshold power, route based control (RBC) was proposed by considering the driver’s characteristics and traffic conditions using the driving data base. In addition, a relationship between the threshold power and various initial battery SOC was obtained by off-line optimization. The performance of the RBC was evaluated by simulation and human-in-the-loop simulation (HILS) for city driving. It was found from the simulation and HILS results that the RBC achieved approximately 4 % to 12 % reduction in fuel consumption compared to the existing charge depleting/charge sustaining (CD/CS) driving control.     

Comparison of computational and analytical methods for evaluation of failure pressure of subsea pipelines containing internal and external corrosions

In the designing stage of subsea pipelines, the design parameters, such as pipe materials, thickness and diameters, are carefully determined to guarantee flow assurance and structural safety. However, once corrosion occurs in pipelines, the operating pressure should be decreased to prevent the failure of pipelines. Otherwise, an abrupt burst can occur in the corroded region of the pipeline, and it leads to serious disasters in the environment and financial loss. Accordingly, the relationship between the corrosion amount and failure pressure of the pipeline, i.e., the maximum operating pressure, should be investigated, and then, the assessment guideline considering the failure pressure should be identified. There are several explicit type codes that regulate the structural safety for corroded subsea pipelines, such as ASME B31G, DNV RF 101, ABS Building and Classing Subsea Pipeline Systems, and API 579. These rules are well defined; however, there are some limitations associated with describing precise failure pressure. Briefly, all of the existing rules cannot consider the material nonlinearity, such as elastoplasticity effect of the pipeline, as well as the actual three-dimensional corrosion shape. Therefore, the primary aim of this study is to suggest a modified formula parameter considering the above-mentioned pipeline and corrosion characteristics. As a result, the material nonlinearity as well as the corrosion configuration, i.e., axial/circumferential corrosion length, width and depth, is reflected in a set of finite element models and a series of finite element analysis considering the operation conditions are followed. Based on the comparative study between the simulation and analytical results, which can be obtained from the classification society rules, the modified formulae for failure pressure calculation are proposed.     

Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips

This paper analyzes transportation mode choice for short home-based trips using a 1999 activity survey from the Puget Sound region of Washington State, U.S.A. Short trips are defined as those within the 95th percentile walking distance in the data, here 1.40 miles (2.25 km). The mean walking distance was 0.4 miles (0.6 km). The mode distribution was automobile (75%), walk (23%), bicycle (1%), and bus (1%). Walk and bicycle are found less likely as the individual’s age increases. People are more likely to drive if they can or are accustomed to. People in multi-person families are less likely to walk or use bus, especially families with children. An environment that attracts people’s interest and provides activity opportunities encourages people to walk on short trips. Influencing people’s choice of transport mode on short trips should be an important part of efforts encouraging the use of non-automobile alternatives.

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.     

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.     

Optimal Design for the Rear-Glass Joint of an Automobile for Squeak and Rattle Noise Reduction

Ever since vehicle noise, vibration, and harshness (NVH) reduction technology made dramatic improvements, vehicle interior noises represented by Squeak and Rattle (S/R) becomes an ever more important factor to improve the emotional quality of vehicles. Generally, people detect S/R noises on automotive interior parts, brake system, suspension, Body in White (BIW), etc. Among them, the rear-glass joint is a major source for vehicle interior noise, and can cause S/R noises under a variety of environmental and driving conditions. This study uses, two approaches, experimental and numerical approaches, to define the cause of S/R noise at the rear-glass section. Based on these two approaches, this study confirms that S/R noises generate through the contact between bottom side of molding and BIW. The sealant penetration length, panelmolding distance, and sealant width are the parameters affecting noise generation. In addition, this study created an optimal design with Design of Experiments (DOE) of the rear-glass joint. The design maximized the sealant penetration length, which is a parameter that majorly affects noise. The optimal design comprises of two steps: sealant injections shape optimization and rear-glass joint parameter optimization. Each step is carried out with FEA and validated by sealant penetration experiments. Through these optimizations, this study obtained an optimum combination of design parameters and fignificantly reduced the noise generated by rear-glass section.     

Design of a compressor-power-based exhaust manifold pressure estimator for diesel engine air management

In accordance with the development of hardware configurations in diesel engines, research on model-based control for these systems has been conducted for years. To control the air management system of a diesel engine, the exhaust manifold pressure should be selected as one of the control targets due to its internal dynamic stability and its physical importance in model-based control. However, it is difficult to measure exhaust pressure using sensors due to gas flow oscillation in the exhaust manifold in a reciprocated diesel engine. Moreover, the sensor is too costly to be equipped on production engines. Hence, the estimation strategies for exhaust manifold pressure have been regarded as a primary issue in diesel engine air management control. This paper proposes a new estimation method for determining the exhaust manifold pressure based on compressor power dynamics. With its simple and robust structure, this estimation leads to improved control performance compared with that of general observers. To compensate for the compressor efficiency error that varies with turbine speed, some correction maps are adopted in the compressor power equation. To verify the control system performance with the new estimator, a HiLS (hardware in the loop simulation) of the NRTC mode is performed. Experimental verification is also conducted using a test bench for the C1-08 mode.     

Fatigue life estimation of injection mold core using simulation-based approach

The purpose of this study is to present a simulation-based approach capable of predicting the fatigue life of a mold core with the help of CAE technology based on injection molding simulation, core stress analysis and fatigue life prediction. Injection molding simulation is performed in order to obtain the non-uniform pressure acting on a mold core due to the polymer melt flow during injection molding process. Subsequently, the melt pressure profile is used as loading condition for core stress analysis via one-way coupling technique. The lifetime of a mold core is then predicted by fatigue analysis with stress-life approach. For verification of simulation-based approach to the fatigue life prediction, tests were also performed.     

Development of estimation algorithms for vehicle’s mass and road grade

In controlling the longitudinal motion of electrified vehicles such as hybrid vehicles and PHEV (Plug-in Hybrid Electric Vehicles), the variation of the driving resistance loads (or driving loads) such as road grade and actual vehicle mass, is the most influential factor which limits the control performance. Measuring the driving load is not impossible, but it is costly since additional sensors have to be mounted on the vehicle. In this study, methods for estimating vehicle mass and road grade are designed to compensate for the driving loads. The proposed methods are verified using simulation tools and then evaluated experimentally.