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Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

Adoption of high-pressure common-rail (HPCR) fuel systems, which subject diesel fuels to higher temperatures and pressures, has brought into question the veracity of ASTM International specifications for biodiesel and biodiesel blend oxidation stability, as well as the lack of any stability parameter for diesel fuel. A controlled experiment was developed to investigate the impact of a light-duty diesel HPCR fuel system on the stability of 20% biodiesel (B20) blends under conditions of intermittent use and long-term storage in a relatively hot and dry climate. B20 samples with Rancimat induction periods (IPs) near the current 6.0-hour minimum specification (6.5 hr) and roughly double the ASTM specification (13.5 hr) were prepared from a conventional diesel and a highly unsaturated biodiesel. Four 2011 model year Volkswagen Passats equipped with HPCR fuel injection systems were utilized: one on B0, two on B20-6.5 hr, and one on B20-13.5 hr.

Development of the next generation internal combustion engines and automatic transmissions for automotive applications is a mandatory powertrain engineering activity required now and in the coming years to meet forthcoming global emissions regulations. This paper details a preliminary investigation into possible synergies for fuel consumption reduction considering emerging automotive technologies integrated into the next generation combustion engine and automatic transmission architectures. A range of hypothetical gasoline engines were created and paired with a generalized set of step gear automatic transmissions designed to meet the performance requirements of high volume longitudinal full size truck application. These designs were then run through a design of experiments orthogonal array for prediction of fuel consumption on the WLTP test schedule and stand still acceleration to 100 kph.

New seal cross-section development is a very tedious and time consuming process if conventional analysis methods are used, as it is very difficult to predict the dimensions of the seal that will satisfy the sealing performance targets. In this study, a generic cross-section is defined and the design constraints are specified. Isight then runs the FEA model, utilizing a custom python script for post-processing. Isight then updates the dimensions of the seal and continues running analyses. Isight was run using two different design exploration techniques. The first was a design of experiments (DOE) to discover how the seal’s response varies with its dimensions. Then, after the analyst examined the results, Isight was run in optimization mode focusing on feasible design areas as determined from the DOE. Thus, after the initial model setup, the user can run the analyses in the background and only needs to interact with the program after Isight has determined a list of feasible designs.

The quality of material model input files for finite element analysis (FEA) is a fundamental factor governing the fidelity and accuracy of simulations at a sub-system or a vehicle level, dictating an investment of due diligence in developing and validating the material models. Several material models conventionally employed for FEA typically allow accounting for only uniaxial tensile behavior of the material; however, the models may be required to predict component-level response in a complex loading scenario. Therefore in developing LSDYNA material input files for such models, it becomes critical to validate their performance in alternative loading scenarios. For out-ofplane loading, typically a three or four-point bending load-case is used for validation. Simulating three point bending (TPB), particularly in the quasi-static regime, requires detailed representation of the moving pin impacting the specimen, and sliding of the specimen on the stationary pins.

The Cadillac CT6 plug-in hybrid electric vehicle (PHEV) power-split transmission architecture utilizes two motors. One is an induction motor type while the other is a permanent magnet AC (PMAC) motor type referred to as motor A and motor B respectively. Bar-wound stator construction is utilized for both motors. Induction motor-A winding is connected in delta and PMAC motor-B winding is connected in wye. Overall, the choice of induction for motor A and permanent magnet for motor B is well supported by the choice of hybrid system architecture and the relative usage profiles of the machines. This selection criteria along with the design optimization of electric motors, their electrical and thermal performances, as well as the noise, vibration, and harshness (NVH) performance are discussed in detail. It is absolutely crucial that high performance electric machines are coupled with high performance control algorithms to enable maximum system efficiency and performance.

The Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project is a U.S. Department of Energy-funded, multi-year project which is aimed at developing a complete CAE tool set for the automotive battery pack design. This paper reports the application of the full field approach of the CAEBAT which is developed by the General Motors-led industry team, for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The simulation results using the Full Field Approach are found to have a very good agreement with the measurement data.

This paper describes the capabilities of a new two-motor plug-in hybrid-electric propulsion system developed for rear wheel drive. The PHEV system comprises a 2.0L turbocharged 4-cylinder direct-injected gasoline engine with the new hybrid transmission [1], a new traction power inverter module, a liquid-cooled lithium-ion battery pack, and on-board battery charger and 12V power converter module. The capability and features of the system components are described, and component performance and vehicle data are reported. The resulting propulsion system provides an excellent combination of electric-only driving, acceleration, and fuel economy.

Mahalanobis Distance (MD) is gaining momentum in many fields where classification, statistical pattern recognition, and forecasting are primary focus. It is a multivariate method and considers correlation relationships among parameters for computing generalized distance measure to separate groups or populations. MD is a useful statistic in multivariate analysis to test that an observed random sample is from a multivariate normal distribution. This capability alone enables engineers to determine if an observed sample is an outlier (defect) that falls outside the constructed (good) multivariate normal distribution. In Mahalanobis-Taguchi System (MTS), MD is suitably scaled and used as a measure of severity in abnormality assessment. It is obvious that computed MD depends on values of parameters observed on a random sample. All parameters may not equally impact MD. MD could be highly sensitive with respect to some parameters and less sensitive to some other parameters.

The need for improved vehicle energy efficiency has increased greatly in recent years along with regulatory fuel economy standards. One key aspect of energy efficiency for both conventional and alternative propulsion vehicles is the energy efficiency of the electrical architecture. In the design of electrical architectures there are several techniques available to increase the energy efficiency. One technique is to manage CAN serial data communication by using Partial Networks. This paper describes a model based approach for simulating the vehicle network behavior when CAN Partial Networking is used as the strategy for need based ECU activation. The simulation results will in turn provide ECU power consumption data to support various electrical architecture design decisions.

Several application developers are currently faced with the problem of moving a complex system from a single-core to a multicore platform. The problem encompasses several issues that go from modeling issues (the need to represent the system features of interest with sufficient accuracy) to analysis and optimization techniques, to the selection of the right formulations for constraints that relate to time. We report on the initial findings in a case study in which the application of interest is a fuel injection system. We provide an analysis on the limitations of AUTOSAR and the existing modeling tools with respect to the representation of the parameters of interest for timing analysis, and we discuss applicable optimization methods and analysis algorithms.

Development of the software using fixed-point arithmetic is known to be tedious and error-prone. Difficulty of selecting the correct data type can outwear software developers. The common retreats often sought after include manual calculation of the approximate ranges, exhaustive simulations with extreme input values and conservative development approach by using excessive word length. The first two retreats - manual calculation and exhaustive simulations - increase the software development time, and the third retreat - conservative development - leads to the excessive memory (RAM and ROM) utilization by the software. The model-based development environment such as the Simulink has graphical nature to the software with flow of data defined by connecting signal lines. The model-based software therefore gives an opportunity to trace signal flow in the software. Input-tracing method is presented to trace the flow of the input signals of the user selected block in the software model.

A GT-Power Fast Run Model simplified from detail model for HIL is verified with a bench test using the dSPACE Simulator. Firstly, the conversion process from a detailed model to FRM model is briefly described. Then, the spark timing, fuel pulse with control for FAR, and torque level control are developed for proof of concept. Moreover a series of FRM/Simulink co-simulation and HIL tests are conducted. In the summary, the test results are presented and compared with GT detailed model simulations. The test results show that the FRM/dSPACE HIL stays consistent in most variables of interest under 0.7-0.9 real-time factor condition between 1000 - 5000 RPM. The same steady-state can be reached by RCP controllers or with GT-Power internal controllers. The transient states are close using different control algorithm. The main purpose of HIL application is achieved, despite inconsistencies in performance data like fuel consumption.

Vehicle occupants, unlike building occupants, are exposed to continuously varying, non-uniform solar heat load. Automotive manufacturers use photovoltaic cells based solar sensor to measure intensity and direction of the direct-beam solar radiation. Use of the time of the day and the position - latitude and longitude - of a vehicle is also common to calculate direction of the direct-beam solar radiation. Two angles - azimuth and elevation - are used to completely define the direction of solar radiation with respect to the vehicle coordinate system. Although the use of solar sensor is common in today’s vehicles, the solar heat load on the occupants, because of their exposure to the direct-beam solar radiation remains the area of in-car subjective evaluation and tuning. Since the solar rays travel in parallel paths, application of the ray tracing method to determine solar insolation of the vehicle occupants is possible.

The ever-increasing regulatory requirement on CO2 emissions drives efficiency improvement of vehicle powertrain systems. In this context, three mega trends have been happening in the automotive transmission industry. First, future automatic transmissions will have more gear steps to offer a broader ratio spread and finer ratio steps, which may enable the engine to operate at its efficient regions more often. Second, engine downsizing with boosted power and flexible cylinder deactivation have been become the technology trend to achieve better thermal efficiency. These engine technologies demand improved transmission dampers with greater isolation capabilities to drive future transmission dampers to be equipped with softer springs. Third, future transmissions will be more efficient due to new architectures and incremental subsystem improvements.

GM has developed an all-new gasoline-electric hybrid powertrain for the model year 2016 Chevrolet Malibu Hybrid vehicle, which was designed to achieve excellent fuel economy, performance, and drive quality. The powertrain shares the transmission architecture with the 2016 Chevrolet Volt extended range electric vehicle, but includes changes to optimize the system for engine driven charge sustaining operation in the range of conditions represented by the US EPA 5 cycle fuel economy tests. In this paper, we describe the Malibu Hybrid propulsion system features and components, including the battery pack, transaxle, electric motors and power electronics, engine, and thermal system. The modifications between the Volt and Malibu Hybrid propulsion systems are discussed and explained as resulting from the differences between the primarily electric and gasoline powered applications.

Previously published research [1] covering the role of piston material properties in brake torque variation sensitivity and roughness concluded that phenolic pistons have significantly higher low-pressure range compliance than steel pistons, which promotes lower roughness propensity. It also determined that this property could be successfully characterized using a modern generation of direct-acting servo hydraulically actuated brake component compression test stands. This paper covers a subsequent block of research into the role of the caliper piston in brake torque variation sensitivity (BTV sensitivity) and thermal roughness of a brake corner. It includes measurements of hydraulic stiffness of pistons in a “wet” fixture, both with and without a brake pad and multi-layer bonded noise shim.

The headliner system in a vehicle is an important element in vehicle noise control. In order to predict the performance of the headliner, it is necessary to develop an understanding of the substrate performance, the effect of air gaps, and the contribution from any acoustic pads in the system. Current Statistical Energy Analysis (SEA) models for predicting absorption performance of acoustic absorbers are based on material Biot properties. However, the resources for material Biot property testing are limited and cost is high. In this paper, modeling parameters for the headliner substrate are identified from a set of standard absorption measurements on substrates, using curve fitting and optimization techniques. The parameters are then used together with thickness/design information in a SEA model to predict the vehicle headliner system absorption performance.

Response Surface Models are often used as a surrogate for expensive black-box functions during optimization to reduce computational cost. Often, the CAE analysis models are highly nonlinear and multi-modal. A response surface approximation of such analysis as a result is highly multi-modal; i.e. it contains multiple local optima. A gradient-based optimizer working with such a response surface will often converge to the nearest local optimum. There does not exist any method to guarantee a global optima for non-convex multi-modal functions. For such problems, we propose an efficient algorithm to find as many distinct local optima as possible. The proposed method is specifically designed to work in large dimensions (about 100 ∼ 1000 design variables and similar number of constraints) and can identify most of the locally optimal solutions in a reasonable amount of time.

Finite element dummy models have been more and more widely applied in virtual development of occupant protection systems across the automotive industry due to their predictive capabilities. H350 dyna dummy model [1] is a finite element representation of the Hybrid III male dummy [2], which is designed to represent the average of the United States adult male population. Lower extremity injuries continue to occur in front crash accidents despite increasing improvement of vehicle crashworthiness and occupant restraint system. It is therefore desirable to predict lower tibia injury numbers in front occupant simulations. Though lower tibia loading/index predictions are not studied as much as the FMVSS 208 regulated injury numbers, the tibia indices are injury criteria that need to be assessed during IIHS and Euro NCAP frontal offset occupant simulations. However during front crash simulations, it is very difficult to achieve good correlations or predictions of lower tibia loadings.