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A small hybrid system was developed for the 2009 model hybrid vehicle. The Intelligent Power Unit (IPU), which consists of a high-voltage battery and a Power Control Unit (PCU), occupies 19% less volume and is 28% lighter than the previous model(1). In order to reduce the size and weight of the IPU, the number of nickel-metal hydride battery modules was reduced, enabling the battery box to be made smaller and lighter. In order to provide the necessary output with fewer battery modules, the length of the battery electrodes was increased, thus raising the output from each battery module. The volume and weight of the PCU were reduced by integrating the inverter, DC-DC converter, and ECU into a single package. The size reduction of the IPU enabled the IPU to be installed at the bottom of the luggage compartment. As a result, the available space in the luggage compartment is the same as that of a conventional vehicle.

NVH refinement is an important aspect of the powertrain development and vehicle integration process. The depletion of fossil-based fuels and increase in price of gasoline have prompted most vehicle manufacturers to embrace propulsion technologies with varying degrees and types of hybridization. Many different hybrid vehicle systems are either on the market, or under development, even up to all-electric vehicles. Each hybrid vehicle configuration brings unique NVH challenges that result from a variety of sources. This paper begins with an introductory discussion of hybrid propulsion technologies and associated unique vehicle NVH challenges inherent in the operation of such hybrid vehicles. Following this, the paper outlines a two-dimensional landscape of typical customer vehicle maneuvers mapped against hybrid electric vehicle (HEV) operational modes.

Since 1992, Toyota Motor Corporation (TMC) has been working on the development of fuel cell system technology. TMC is designing principal components in-house, including fuel cell stacks, high-pressure hydrogen storage tank systems, and hybrid systems. TMC developed the ‘02 model TOYOTA FCHV, the world-first market-ready fuel cell vehicle, and started limited lease of the vehicles in December 2002. In June 2008, TMC developed a new TOYOTA FCHV-adv which obtained vehicle type certification in Japan, and is currently available for leasing in Japan and the United States. In the development of the TOYOTA FCHV-adv, TMC has improved the cruising range and cold start/drive capability from the previous TOYOTA FCHV. The TOYOTA FCHV-adv has achieved an actual cruising range of over 500 km, which is equivalent to that of current gasoline vehicles. In addition, the TOYOTA FCHV-adv has proven starting/driving capability at -30°C temperature.

Integrating the seemingly divergent objectives of aircraft seat configuration is a difficult task. Aircraft manufacturers look to design seats to maximize customer satisfaction and in-flight safety, but these objectives can conflict with the profit motive of airline companies. In order to boost revenue by increasing the number of passengers per aircraft, airline companies may increase seat height and decrease seat pitch. This results in disaccommodation of a greater percentage of the passenger population and is a reason for rising customer dissatisfaction. This paper describes an effort to bridge this gap by incorporating digital human models, layout optimization, and a profit-maximizing constraint into the aircraft seat design problem. A simplified aircraft seat design experiment is conceptualized and its results are extrapolated to an airline passenger population.

For actuation of high lift surfaces in modern airplanes, complex mechanical shaft transmission systems powered by central drive units are deployed. The design of mechanical actuation systems, which have a major share in the weight of secondary flight controls, is a complex and challenging engineering task. Especially for specification of essential component and system design parameters within the preliminary design phase, engineering skill and experience are of significant importance owing to many uncertainties in component data and boundary conditions. Extensive trade-offs, as well as an evaluation of the system requirements and constraints lead to an iterative and time-consuming design process. Utilizing an integrated design assistance tool, mathematical functions and constraints can be modeled on system and component level and formalized as a constraint satisfaction problem (CSP). Thus, automated consistency checking and pruning of the solution space can be achieved.

In several industrial fields, the integration of functions is a key technology to enhance the efficiency of components in terms of performance to mass/volume/cost ratio. Concerning the space industry, in the last few years the trend in spacecraft design has been towards smaller, light-weight and higher performance satellites with sophisticated payloads and instrumentation. Increasing power density figures are the common feature of such systems, constituting a challenging task for the Thermal Control System. The traditional mechanical and thermal design concepts are evidencing their limits with reference to such an emerging scenario.

Aviation battery maintenance is trending toward on-condition maintenance. Nickel-Cadmium (NiCd), Valve Regulated Lead-Acid (VRLA), or prospective Li-ion batteries are used to start engines, provide emergency back-up power, and assure ground power capability for maintenance and pre-flight checkout. As these functions are mission essential, State of Health (SoH) recognition is critical. SoH includes information regarding battery energy, power and residual cycle life. This paper describes an SoH recognition technique for on-board aviation batteries and presents a passive diagnostic device (PDD). The PDD monitors on-board system battery current, voltage and ambient temperature and utilizes no active signals to the battery which can be restricted or even prohibited in order to avoid any interference with the vehicle electrical system.

The Cessna Citation CJ4, certified on March 12, 2010, is believed to be the first civil aircraft with a Lithium Ion main battery. The 26.4VDC, 44Ah Lithium Ion main battery weighs 54 lbs, a 35% weight saving over a Nickel-Cadmium battery. Using phosphate-based Lithium Ion cells, which have no positive feedback thermal runaway failure mode, system integration of the battery and aircraft architecture design is simpler. Electronics and software are needed to optimize life only, not to ensure safety. Emergency discharge with failed electronics is enabled with the selection of a less volatile chemistry, the use of an analog Module Management System for cell balancing and protection, and the use of a microcontroller-based digital Central Monitoring System that reports health. System safety failure hazard assessment is considered Major, and the battery software is certified to the requirements of RTCA DO-178B, Design Assurance Level C.

Lithium-ion (Li-ion) batteries are becoming widely used high-energy sources and a replacement of the Nickel Metal Hydride batteries in electric vehicles (EV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). Because of their light weight and high energy density, Li-ion cells can significantly reduce the weight and volume of the battery packs for EVs, HEVs and PHEVs. Some materials in the Li-ion cells have low thermal stabilities and they may become thermally unstable when their working temperature becomes higher than the upper limit of allowed operating temperature range. Thus, the cell working temperature has a significant impact on the life of Li-ion batteries. A proper control of the cell working temperature is crucial to the safety of the battery system and improving the battery life. This paper outlines an approach for the thermal analysis of Li-ion battery cells and modules.

Electric vehicle development is at a crossroads. Consumers want vehicles that offer the same size, performance, range, reliability and cost as their current vehicles. OEMs must make a profit, and the government requires compliance with emissions standards. The result - low volume, compromised vehicles that consumers don't want, with questionable longevity and minimal profitability. In-wheel motor technology offers a solution to these problems; providing power equivalent to ICE alternatives in a package that does not invade chassis, passenger and cargo space. At the same time in-wheel motors can reduce vehicle part count, complexity and cost, feature integrated power electronics, give complete design freedom and the potential for increased regenerative braking (reducing battery size and cost, or increasing range).

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

With the introduction of plug-in electric vehicles (PEVs), the conventional mindset of “fill-up time” will be challenged as customers top off their battery packs. For example, using a standard 120VAC outlet, it may take over 10hrs to achieve 40-50 miles of EV range-making range anxiety a daunting reality for EV owners. As customers adapt to this new mindset of charge time, it is critical that automotive OEMs supply the consumer with accurate charge time estimates. Charge time accuracy relies on a variety of parameters: battery pack size, power source, electric vehicle supply equipment (EVSE), on-board charging equipment, ancillary controller loads, battery temperature, and ambient temperature. Furthermore, as the charging events may take hours, the initial conditions may vary throughout a plug-in charge (PIC). The goal of this paper is to characterize charging system sensitivities and promote best practices for charge time estimations.

In this paper, we present a parameter-free, or a node-based optimization method for finding the smooth optimal free-form of automotive shell structures, including global and local curvature distributions such as beads or embossed ribs. The design problems dealt with in this paper involve a stiffness problem. Stiffness is maximized using the compliance as an objective functional. The optimum design problem is formulated as a distributed-parameter, or non-parametric, shape optimization problem under the assumptions that the shell is varied in the normal direction to the surface and the thickness is constant. The shape gradient function and the optimality conditions are then theoretically derived. The optimum free-form, or optimal curvature distribution, is determined by applying the derived shape gradient function in the normal direction to the shell surface as pseudo external forces to vary the surface and to minimize the objective functional.

EuroSID-2 and EuroSID-2re are among the most frequently used side impact dummies in vehicle crash safety. Radioss is one of most widely applied finite element codes for crash safety analysis. To meet the needs of crash safety analysis and to exploit the potential of the Radioss code, a new generation of EuroSID-2 (ES2) and EuroSID-2re (ES2_RE) Radioss dummies was developed at First Technology Safety System (FTSS) in collaboration with Altair. This paper describes in detail the development of the ES2/ES2_RE dummies. Firstly whole dummy meshes were created based on CAD data and intensive efforts were made to obtain penetration/intersection-free models. Secondly FTSS finite element certificate tests at component level were conducted to obtain satisfactory component performances. These tests include the head drop test, the neck pendulum test, the lumbar pendulum test and the thorax drop test [ 1 , 2 ].

Axiomatic design is utilized to identify the design characteristics of an On-Line Electric Vehicle and to manage the design information. The On-Line Electric Vehicle, which is being developed at the Korea Advanced Institute of Science and Technology, is a different concept of an electric vehicle from conventional electric vehicles which use the electric power of a charged battery(s). It is operated by an electric power supplied by the contactless power transmission technique between the roadway side and the vehicle. In other words, the power is transmitted based on the principle of an electric transformer. The On-Line Electric Vehicle can overcome the limitations of conventional electric vehicles such as the weight of the battery and driving distance problems. Because designers have little experience and knowledge about the On-Line Electric Vehicle in the developmental stage, an appropriate design guide is needed. The axiomatic approach is employed for the design process.

In this paper a diagnostic design process is proposed for developmental vehicles where mainstream design process is not well-suited. First a review of current practice in on-board vehicle fault diagnostics design is presented with particular focus on the application of this process to the development of the Ford Escape Hybrid Electric Vehicle (HEV) program and a demonstration Fuel Cell Electric Vehicle (FCEV) program. Based on the review and evaluation of these experiences, a new tool for diagnostics design is proposed that promises to make the design more traceable, to reduce the repetition of work, and to improve understandability and reuse.

As world-wide container volume increases and very large container ships emerge as a dominant player in the maritime cargo transport market, functional capabilities of container ports need to be greatly enhanced. To address this problem, KAIST is undertaking a project to design a novel container transport system, namely Mobile Harbor. Mobile Harbor refers to a system that can go out to a large container ship anchoring in the open sea, load and unload containers between the container ship and the Mobile Harbor, and transport them to their destinations. Designing Mobile Harbor presents a number of challenges as with many other large-scale engineering projects, especially at the beginning stage of the project.

As the number of electrical devices in modern vehicles increases, the battery becomes more critical component for the operation of vehicles. To ensure the startability of the vehicle, battery conditions such as state of charge and state of health should be properly monitored and maintained. To reduce walk-home incidents due to no-start situation, appropriate warning should be issued to the driver to advise necessary actions such as replacing or re-charging the battery. For the last couple of years, General Motors has studied and developed several battery health monitoring methods based on different battery health signatures. Yet, it is found that relying on a single method may lead to false alarm or misdetection due to lack of information or uncertainty. This paper develops the algorithm for more robust and reliable battery health monitoring and prognosis, by applying Evidence Theory to fuse different battery health signatures.