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Impact of driving patterns on fuel economy is significant in hybrid electric vehicles (HEVs). Driving patterns affect propulsion and braking power requirement of vehicles, and they play an essential role in HEV design and control optimization. Driving pattern conscious adaptive strategy can lead to further fuel economy improvement under real-world driving. This paper proposes a real-time driving pattern recognition algorithm for supervisory control under real-world conditions. The proposed algorithm uses reference real-world driving patterns parameterized from a set of representative driving cycles. The reference cycle set consists of five synthetic representative cycles following the real-world driving distance distribution in the US Midwestern region. Then, statistical approaches are used to develop pattern recognition algorithm. Driving patterns are characterized with four parameters evaluated from the driving cycle velocity profiles.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

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.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

This paper proposes a novel probabilistic approach for multidisciplinary design optimization (MDO) under uncertainty, especially for systems with feedback coupled analyses with multiple coupling variables. The proposed approach consists of four components: multidisciplinary analysis, Bayesian network, copula-based sampling, and design optimization. The Bayesian network represents the joint distribution of multiple variables through marginal distributions and conditional probabilities, and updates the distributions based on new data. In this methodology, the Bayesian network is pursued in two directions: (1) probabilistic surrogate modeling to estimate the output uncertainty given values of the design variables, and (2) probabilistic multidisciplinary analysis (MDA) to infer the distributions of the coupling and output variables that satisfy interdisciplinary compatibility conditions.

Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.

In an automotive cooling circuit, the wax melting process determines the net and time history of the energy transfer between the engine and its environment. A numerical process that gives insight into the mixing process outside the wax chamber, the wax melting process inside the wax chamber, and the effect on the poppet valve displacement will be advantageous to both the engine and automotive system design. A fully three dimensional, transient, system level simulation of an inlet controlled thermostat inside an automotive cooling circuit is undertaken in this paper. A proprietary CFD algorithm, Simerics-Sys®/PumpLinx®, is used to solve this complex problem. A two-phase model is developed in PumpLinx® to simulate the wax melting process. The hysteresis effect of the wax melting process is also considered in the simulation.

Three computational gas and fluid dynamic methods, CV/UP (Control Volume/Uniform Pressure), CPM (Corpuscular Particle Method), and ALE (Arbitrary Lagrangian and Eulerian), were investigated in this research in an attempt to predict the responses of side crash pressure sensors. Acceleration-based crash sensors have been used extensively in the automotive industry to determine the restraint system firing time in the event of a vehicle crash. The prediction of acceleration-based crash pulses by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crush zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side crash applications.

Hybrid Electric Vehicles (HEV) utilize a High Voltage (HV) battery pack to improve fuel economy by maximizing the capture of vehicle kinetic energy for reuse. Consequently, these HV battery packs experience frequent and rapid charge-discharge cycles. The heat generated during these cycles must be managed effectively to maintain battery cell performance and cell life. The HV battery pack cooling system must keep the HV battery pack temperature below a design target value and maintain a uniform temperature across all of the cells in the HV battery pack. Herein, the authors discuss some of the design points of the air cooled HV battery packs in Ford Motor Company’s current model C-Max and Fusion HEVs. In these vehicles, the flow of battery cooling air was required to not only provide effective cooling of the battery cells, but to simultaneously cool a direct current high voltage to low voltage (DC-DC) converter module.

The airflow that enters the front grille of a ground vehicle for the purpose of component cooling has a significant effect on aerodynamic drag. This drag component is commonly referred to as cooling drag, which denotes the difference in drag measured between open grille and closed grille conditions. When the front grille is closed, the airflow that would have entered the front grille is redirected around the body. This airflow is commonly referred to as cooling interference airflow. Consequently, cooling interference airflow can lead to differences in vehicle component drag; this component of cooling drag is known as cooling interference drag. One mechanism that has been commonly utilized to directly influence the cooling drag, by reducing the engine airflow, is active grille shutters (AGS). For certain driving conditions, the AGS system can restrict airflow from passing through the heat exchangers, which significantly reduces cooling drag.

At various milestones during a vehicle’s development program, different CAE models are created to assess NVH error states of concern. Moreover, these CAE models may be developed in different commercial CAE software packages, each one with its own unique advantages and strengths. Fortunately, due to the wide spread acceptance that the Functional Mock-up Interface (FMI) standard gained in the CAE community over the past few years, many commercial CAE software now support cosimulation in one form or the other. Cosimulation allows performing multi-domain/multi-resolution simulations of the vehicle, thereby combining the advantages of various modeling techniques and software. In this paper, we explore cosimulation of full 3D vehicle model developed in MSC ADAMS with 1D driveline model developed in LMS AMESim. The target application of this work is investigation of vehicle NVH error states associated with both hybridized and non-hybridized powertrains.

Fuel cell vehicles are entering the automotive market with significant potential benefits to reduce harmful greenhouse emissions, facilitate energy security, and increase vehicle efficiency while providing customer expected driving range and fill times when compared to conventional vehicles. One of the challenges for successful commercialization of fuel cell vehicles is transitioning the on-board fuel system from liquid gasoline to compressed hydrogen gas. Storing high pressurized hydrogen requires a specialized structural pressure vessel, significantly different in function, size, and construction from a gasoline container. In comparison to a gasoline tank at near ambient pressures, OEMs have aligned to a nominal working pressure of 700 bar for hydrogen tanks in order to achieve the customer expected driving range of 300 miles.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

High-strength aluminum alloys such as 7075 can be formed using advanced manufacturing methods such as hot stamping. Hot stamping utilizes an elevated temperature blank and the high pressure stamping contact of the forming die to simultaneously quench and form the sheet. However, changes in the thermal history induced by hot stamping may increase this alloy’s stress corrosion cracking (SCC) susceptibility, a common corrosion concern of 7000 series alloys. This work applied the breaking load method for SCC evaluation of hot stamped AA7075-T6 B-pillar panels that had been artificially aged by two different artificial aging practices (one-step and two-step). The breaking load strength of the specimens provided quantitative data that was used to compare the effects of tensile load, duration, alloy, and heat treatment on SCC behavior.

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

The Composite Hybrid Automotive Suspension System Innovative Structures (CHASSIS) is a project to develop structural commercial vehicle suspension components in high volume utilising hybrid materials and joining techniques to offer a viable lightweight production alternative to steel. Three components are in scope for the project:- Front Subframe Front Lower Control Arm (FLCA) Rear Deadbeam Axle

Selection of the DC-link capacitance value in an HEV/EV e-Drive power electronic system depends on numerous factors including required voltage/current ratings of the capacitor, power dissipation, thermal limitation, energy storage capacity and impact on system stability. A challenge arises from the capacitance value selection based on DC-link stability due to the influence of multiple hardware parameters, control parameters, operating conditions and cross-coupling effects among them. This paper discusses an impedance-based methodology to determine the minimum required DC-link capacitance value that can enable stable operation of the system in this multi-dimensional variable space. A broad landscape of the minimum capacitance values is also presented to provide insights on the sensitivity of system stability to operating conditions.

The National Highway Traffic Safety Administration (NHTSA) published a Request for Comments (RFC) on proposed changes to the New Car Assessment Program (NCAP) in 2015 and 2017. One potential change was the introduction of a frontal Oblique Impact (OI) crash test. The Test device for Human Occupant Restraint (THOR) in the front left seat was used in the proposed OI test. The motivations behind the current study were a) determine if OI sled tests can be simplified, b) study the sensitivity of the THOR chest deflection to the shoulder belt layout in OI and c) assess the NHTSA-proposed THOR thoracic injury risk curves. In the current study, eleven oblique sled impact tests were conducted. The environment was representative of a generic mid-sized sedan. The buck was mounted on a rigid plate that allowed the pre-test rotation of the buck relative to the sled axis. A generic mid-sized OI pulse was used. The pulse was applied in the longitudinal direction of the sled.

To evaluate vehicle crash performance in the early design stages, a reliable fracture model is needed in crash simulations to predict material fracture initiation and propagation. In this paper, a generalized incremental stress state dependent damage model (GISSMO) in LS-DYNA® was calibrated and validated for a 780-MPa third generation advanced high strength steels (AHSS), namely 780 XG3TM steel that combines high strength and ductility. The fracture locus of the 780 XG3TM steel was experimentally characterized under various stress states including uniaxial tension, shear, plane strain and equi-biaxial stretch conditions. A process to calibrate the parameters in the GISSMO model was developed and successfully applied to the 780 XG3TM steel using the fracture test data for these stress states.