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In this paper, the microstructure-based finite element modeling method is used in investigating the loading path dependence of formability of transformation induced plasticity (TRIP) steels. For this purpose, the effects of different loading path on the forming limit diagrams (FLD) of TRIP steels are qualitatively examined using the representative volume element (RVE) of a commercial TRIP800 steel. First, the modeling method was introduced, where a combined isotropic/kinematic hardening rule is adopted for the constituent phases in order to correctly describe the cyclic deformation behaviors of TRIP steels during the forming process with combined loading paths which may include the unloading between the two consecutive loadings. Material parameters for the constituent phases remained the same as those in the authors' previous study [ 1 ] except for some adjustments for the martensite phase due to the introduction of the new combined hardening rule.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

Improvements in highway fuel economy require clever design and novel methods to reduce the drag coefficient. The integration of active flow control devices into vehicle design shows promise for greater reductions in drag coefficient. This paper examines the use of fluidic oscillators for separation control at the rear of an Ahmed vehicle model. A fluidic oscillator is a simple device that generates a sweeping jet output, similar to some windshield wiper spray nozzles, and is increasingly recognized as an efficient means to control separation. In this study, fluidic oscillators were used to blow unsteady air jets and control flow separation on rear boat-tail flaps, achieving drag reductions greater than 70 counts. The method appears to scale favorably to a larger model, and realistic effects such as a rolling road appear to have a small impact on the oscillator's control authority.

Engine downsizing and super/turbocharging is currently the most followed trend in order to reduce CO2 emissions and increase the powertrain efficiency. A key challenge for achieving the desired fuel economy benefits lies in optimizing the design and control of the engine boosting system, which requires the ability to rapidly sort different design options and technologies in simulation, evaluating their impact on engine performance and fuel consumption. This paper presents a scalable modeling approach for the characterization of flow and efficiency maps for automotive turbochargers. Starting from the dimensional analysis theory for turbomachinery and a set of well-known control-oriented models for turbocharged engines simulation, a novel scalable model is proposed to predict the flow and efficiency maps of centrifugal compressors and radial inflow turbines as function of their key design parameters.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

Dynamic programming (DP) has been used for optimal control of hybrid powertrain and vehicle speed optimization particularly in design phase for over a couple of decades. With the advent of autonomous and connected vehicle technologies, automotive industry is getting closer to implementing predictive optimal control strategies in real time applications. The biggest challenge in implementation of optimal controls is the limitation on hardware which includes processor speed, IO speed, and random access memory. Due to the use of autonomous features, modern vehicles are equipped with better onboard computational resources. In this paper we present a comparison between multiple hardware options for dynamic programming. The optimal control problem considered, is the optimization of travel time and fuel economy by tuning the torque split ratio and vehicle speed while maintaining charge sustaining operation.

Simulation-based design of connected and automated hybrid-electric vehicles is a challenging problem. The design space is large, the systems are complex, and the influence of connected and autonomous technology on the process is a new area of research. The Ohio State University EcoCAR Mobility Challenge team developed a comprehensive design and simulation approach as a solution. This paper covers the detailed simulation work conducted after initial design space reduction was performed to arrive at a P0-P4 hybrid vehicle with a gasoline engine. Two simulation environments were deployed in this strategy, each with unique advantages. The first was Autonomie, which is a commercial software tool that is well-validated through peer-reviewed studies. This allowed the team to evaluate a wide range of components in a robust simulation framework.

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication and automated driving capability. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green, utilizing V2I and vehicle longitudinal automation to modify the speed profile of a mid-size generic vehicle to decrease fuel consumption has been studied. Pass at Green (PaG) uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and remaining time that the phase stays active.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

More stringent emissions regulations are continually being proposed to mitigate adverse human health and environmental impacts of internal combustion engines. With that in mind, it has been proposed that vehicular particulate matter (PM) emissions should be regulated based on particle number in addition to particle mass. One aspect of this project is to study different sample handling methods for number-based aerosol measurements, specifically, two different methods for removing volatile organic compounds (VOCs). One method is a thermodenuder (TD) and the other is an evaporative chamber/diluter (EvCh). These sample-handling methods have been implemented in an engine test cell with a spark-ignited direct injection (SIDI) engine. The engine was designed for stoichiometric, homogeneous combustion.

This paper examines some key aspects of the manufacturing process that “ Transformation Induced Plasticity” (TRIP) steels would be exposed to, and systematically evaluate how the forming and thermal histories affect final strength and ductility of the material. We evaluate the effects of in-service temperature variations, such as under hood and hot/cold cyclic conditions, to determine whether these conditions influence final strength, ductility and energy absorption characteristics of several available TRIP steel grades. As part of the manufacturing thermal environment evaluations, stamping process thermal histories are included in the studies. As part of the in-service conditions, different pre-straining levels are included. Materials from four steel suppliers are examined. The thermal/straining history versus material property relationship is established over a full range of expected thermal histories and selected loading modes.

Microstructure level inhomogeneities between the harder martensite phase and the softer ferrite phase render the dual phase (DP) steels more complicated failure mechanisms and associated failure modes compared to the conventionally used low alloy homogenous steels. This paper examines the failure mode DP780 steel under different loading conditions using finite element analyses on the microstructure levels. Micro-mechanics analyses based on the actual microstructures of DP steel are performed. The two-dimensional microstructure of DP steel was recorded by scanning electron microscopy (SEM). The plastic work hardening properties of the ferrite phase was determined by the synchrotron-based high-energy X-ray diffraction technique. The work hardening properties of the martensite phase were calibrated and determined based on the uniaxial tensile test results. Under different loading conditions, different failure modes are predicted in the form of plastic strain localization.

Recently, several studies conducted by automotive industry revealed the tremendous advantages of Advanced High Strength Steels (AHSS). TRansformation Induced Plasticity (TRIP) steel is one of the typical representative of AHSS. This kind of materials exhibits high strength as well as high formability. Analyzing the crack behaviour in TRIP steels is a challenging task due to the microstructure level inhomogeneities between the different phases (ferrite, bainite, austenite, martensite) that constitute these materials. This paper aims at investigating the fracture resistance of TRIP steels. For this purpose, a micromechanical finite element model is developed based on the actual microstructure of a TRIP 800 steel. Uniaxial tensile tests on TRIP 800 sheet notched specimens were also conducted and tensile properties and R-curves (Resistance curves) were determined.

In this paper, results of finite element crash simulation are presented for a TRIP steel side rail with and without considering the phase transformation during forming operations. A homogeneous phase transformation model is adapted to model the mechanical behavior of the austenite-to-martensite phase. The forming process of TRIP steels is simulated with the implementation of the material model. The distribution and volume fraction of the martensite in TRIP steels may be greatly influenced by various factors during forming process and subsequently contribute to the behavior of the formed TRIP steels during the crushing process. The results indicate that, with the forming induced phase transformation, higher energy absorption of the side rail can be achieved. The phase transformation enhances the strength of the side rail.

This paper presents development of a multi-scale material model for a 980 MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning heat treatment (QP980), based on integrated computational materials engineering principles (ICME Model). The model combines micro-scale material properties defined by the crystal plasticity theory with the macro-scale mechanical properties, such as flow curves under different loading paths. For an initial microstructure the flow curves of each of the constituent phases (ferrite, austenite, martensite) are computed based on the crystal plasticity theory and the crystal orientation distribution function. Phase properties are then used as an input to a state variable model that computes macro-scale flow curves while accounting for hardening caused by austenite transformation into martensite under different straining paths.

Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

A noticeable degree of inconsistent forming behaviors has been observed for the 1st generation advanced high strength steels (AHSS) in production, and they appear to be associated with the inherent microstructural-level inhomogeneities for various AHSS. This indicates that the basic material property requirements and screening methods currently used for the mild steels and high strength low alloys (HSLA) are no longer sufficient for qualifying today's AHSS. In order to establish more relevant material acceptance criteria for AHSS, the fundamental understandings on key mechanical properties and microstructural features influencing the local formability of AHSS need to be developed. For this purpose, in this study, DP980 was selected as model steels and eight different types of DP980 sheet steels were acquired from various steel suppliers.

Advanced Driver Assistance System (ADAS) has become a common standard feature assisting greater safety and fuel efficiency in the latest automobiles. Yet some ADAS systems fail to improve driving comfort for vehicle occupants who expect human-like driving. One of the more difficult situations in ADAS-assisted driving involves instances with cut-in vehicles. In vehicle control, determining the moment at which the system recognizes a cut-in vehicle as an active target is a challenging task. A well-designed comprehensive threat assessment developed for cut-in vehicle driving scenarios should eliminate abrupt and excessive deceleration of the vehicle and produce a smooth and safe driving experience. This paper proposes a novel methodology for threat assessment for driving instances involving a cut-in vehicle. The methodology takes into consideration kinematics, vehicle dynamics, vehicle stability, road condition, and driving comfort.

This paper examines the effects of fusion zone size on failure modes, static strength and energy absorption of resistance spot welds (RSW) of advanced high strength steels (AHSS). DP800 and TRIP800 spot welds are considered. The main failure modes for spot welds are nugget pull-out and interfacial fracture. Partial interfacial fracture is also observed. The critical fusion zone sizes to ensure nugget pull-out failure mode are developed for both DP800 and TRIP800 using the limit load based analytical model and the microhardness measurements of the weld cross sections. Static weld strength tests using cross-tension samples were performed on the joint populations with controlled fusion zone sizes. The resultant peak load and energy absorption levels associated with each failure mode were studied using statistical data analysis tools. The results of this study show that the conventional weld size of can not produce nugget pull-out mode for both the DP800 and TRIP800 materials.