Modeling and control strategy development for fuel cell hybrid vehicles

Using MATLAB/Simulink, we constructed a comprehensive simulation model for the fuel cell hybrid vehicle (FCHV) power train in parallel with a power control strategy that uses a logic threshold approach implemented with a hybrid control unit (HCU). The simulation implements power flow and power distribution under different vehicle operating modes using the accelerator and decelerator pedal positions deduced from the driving schedule as primary inputs. The HCU control strategy also incorporates regenerative braking and recharging for recovery of battery capacity. Using the D-optimality method for selection of the optimal experiment values, three control threshold variables for the HCU are selected to maximize the hydrogen fuel economy under certain driving cycles. The proposed method provides the optimal configuration of the FCHV model, which has the capability of achieving the requested drive power while also meeting the vehicle driving schedule and recovery needs of the state of charge (SOC) battery, with lower fuel consumption levels.     

Effect of fuel choice on nanoparticle emission factors in LPG-gasoline bi-fuel vehicles

Nanoparticle and gas-phase emission factors are presented for a liquefied petroleum gas (LPG) passenger vehicle and are compared to gasoline operation. A bi-fuel LPG-gasoline vehicle certified for use on either fuel was used as the test vehicle so that a direct comparison of the emissions could be made based on fuel choice. These values were considered along with previous studies to determine the relative change in particulate emissions due to fuel choice over a wide range of vehicles and operating conditions. The vehicle examined in this study was tested on a chassis dynamometer for both steadystate and transient conditions. Transient test cycles included the US FTP72 driving cycle, Japanese driving cycle and modified Indian driving cycle while steady-state tests were done at vehicle speeds ranging from 10–90 km/hr in various transmission gears. Exhaust particle size distributions were measured in real-time using a differential mobility spectrometer (DMS50), and particle number and particle mass emission factors were calculated. For both fuels, the majority of the particles ranged from 5 to 160 nm in terms of particle diameter, with typically more than 85% of the particles in the nucleation mode (between 5–50 nm). In most cases, the vehicle produced a greater fraction of larger (accumulation mode) particles when fuelled on LPG. Using the data in the literature as well as the data in the current study, gasoline fuel produces 4.6 times more particles in terms of number and 2.1 times more particles in terms of mass.     

Changing ownership structures in the Dutch fleet

The new Dutch shipping policy has given rise to high expectation and curiosity in the world of shipping. This, together with the obvious difficulties of an ex-post study of the effectiveness of such innovative policy, has prompted the authors to take an interest in attempting to investigate changes in ownership of the Dutch fleet 2 years after the new policy has been introduced. To do this, a rather new concept of 'flagging out' has been adopted, which is interpreted as a process leading to different degrees of 'foreignness' in a shipping operation. In this paper, a method to structure is presented, and ownership information, and changes in it are presented, that are essentially based on the theory of stochastic processes. The data used is the Dutch fleet data, which includes information on the nationality of the flag, parent ownership and management over the period 1994-1998. The results yield interesting insights in the shifts in the ownership and operational structure of shipping companies induced by the new policy. In particular, they allow one to establish the origin of new vessels, in which stage of ownership/operation it is most likely that shifts occur, and to determine the relationship between the different stages of the 'flagging out' process. These results, thus, enable an evaluation of the areas in which the policy can be expected to yield results.     

Small area estimates of daily person-miles of travel: 2001 National Household Transportation Survey

The National Household Transportation Survey (NHTS) was designed at the national level, and for most states it does not have a large enough sample to produce reliable estimates, especially for subdomains (e.g., age groups) within a state. Using the 2001 NHTS, we produced small area estimates (SAEs) of the percentage of persons among four age groups (17 or younger, 18–39, 40–54, and 55 or older) having high daily person-miles of travel (more than 87.5 miles a day, which is the 90th percentile for daily person-miles traveled) and associated prediction intervals for all 50 states and the District of Columbia. The survey weighted hierarchical Bayes (Folsom et al., Proc of the Sect on Surv Res Methods of the Am Stat Assoc 371–375, 1999) small area estimation (SAE) methodology was used to produce state-level SAEs. This paper describes the methodology and shows that SAE can be an effective technique for producing reliable state-level estimates from large, national surveys like the NHTS. In particular, the prediction interval relative widths for SAEs were, on average, 31–48% narrower than the corresponding design-based confidence interval widths, whereas for small states the reduction was around 47–63%.     

A return period based plastic design approach for ice loaded side-shell/bow structures

Based on the design ice load that corresponds to a certain return period, a practical approach for designing the frames behind the side-shell or the bow of a ship subject to ice loading is proposed. Four typical types of section profiles have been selected for modelling and numerical analysis. Non-linear finite element analyses that involve both the geometric and material non-linearity were performed. From the analyses, the relationships between the loads and the beam's cross-section properties were obtained for different permanent set requirements. These relationships were then used for finding the section modulus from which the cross-section of the beam can be decided. Finally, a design example is presented for illustration.     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.     

Fatigue features extraction of road load time data using the S-transform

This paper presents the algorithm development of a new fatigue data editing technique using S-T approach. In general, the S-transform (S-T) is a time-frequency spectral localization method which performs a multi-resolution analysis on signal. This method represents a better time-frequency resolution especially for non-stationary signal analysis. This technique was developed to produce shortened fatigue data for fatigue durability testing. The S-T method was applied to detect the damaging events contained in the fatigue signals due to high S-T spectrum location. The damaging events were extracted from an original fatigue signal to produce the shortened edited signal which has equivalent fatigue damage. Three types of road load fatigue data were used for simulation purpose, pave track, highway and country road. In this study, an algorithm was developed, to detect the damaging events in the original fatigue signal. The algorithm can be used to extract the fatigue damaging events and these events were combined in order to produce a new edited signal which neglect the low amplitude cycles. The edited signal consists of the majority of the original fatigue damage in the shortened signal with 15–25% time reduction. Thus, it has been suggested that this shortened signal can then be used in the laboratory fatigue testing for the purpose of accelerated fatigue testing.     

Determinants of the damage cost and injury severity of ferry vessel accidents

This study investigates determinants of the property damage cost and injury severity of ferry vessel accidents. Detailed data of individual ferry vessel accidents for the 11-year timeperiod 1991–2001 that were investigated by the U.S. Coast Guard are used to estimate ferry-vessel accident property damage cost and injury severity equations. Tobit regression is used to estimate the former equation and the ordered probit model is used to estimate the latter. Property damage costs include damage costs to the vessel itself, its cargo and contents, and other-property damage (e.g., damage to pier structures and waterfront facilities). Injury severity for a ferry vessel accident is measured as an ordinal variable — no injuries, non-fatal injuries and fatal injuries. Damage cost and injury severity of individual ferry vessel accidents are expressed as functions of the type of vessel accident, vessel characteristics, vessel operation phase, weather/visibility conditions, type of waterway, type of vessel propulsion, type of vessel hull construction and cause of vessel accident. The property damage estimation results suggest that allision, collision and fire ferry vessel accidents incur more vessel property damage cost per vessel gross ton than other types of accidents. The injury severity estimation results suggest that injury severity is greater when the ferry vessel accident is caused by human error as opposed to vessel and environmental factors.     

URANS analysis of a broaching event in irregular quartering seas

Ship motions in a high sea state can have adverse effects on controllability, cause loss of stability, and ultimately compromise the survivability of the ship. In a broaching event, the ship losses control, naturally turning broadside to the waves, causing a dangerous situation and possibly capsizing. Classical approaches to study broaching rely on costly experimental programs and/or time-domain potential or system-based simulation codes. In this paper the ability of Reynolds averaged Navier–Stokes (RANS) to simulate a broaching event in irregular waves is demonstrated, and the extensive information available is used to analyze the broaching process. The demonstration nature of this paper is stressed, as opposed to a validated study. Unsteady RANS (URANS) provides a model based on first principles to capture phenomena such as coupling between sway, yaw, and roll, roll damping, effects of complex waves on righting arm, rudders partially out of the water, etc. The computational fluid dynamics (CFD) method uses a single-phase level-set approach to model the free surface, and dynamic overset grids to resolve large-amplitude motions. Before evaluating irregular seas two regular wave cases are demonstrated, one causing broaching and one causing stable surf riding. A sea state 8 is imposed following an irregular Bretschneider spectrum, and an autopilot was implemented to control heading and speed with two different gains for the heading controller. It is concluded that the autopilot causes the ship to be in an adverse dynamic condition at the beginning of the broaching process, and thus is partially responsible for the occurrence of the broaching event.