Search Results

Control of vehicle powertrain thermal management systems is becoming more challenging as the number of components is growing, and as a result, advanced control methods are being investigated. Model predictive control (MPC) is particularly interesting in this application because it provides a suitable framework to manage actuator and temperature constraints, and can potentially leverage preview information if available in the future. In previous SAE publications (2015-01-0336 and 2016-01-0215), a robust MPC control formulation was proposed, and both simulation and powertrain thermal lab test results were provided. In this work, we discuss the controller deployment in a vehicle; where controller validation is done through road driving and on a wind tunnel chassis dynamometer. This paper discusses challenges of linear MPC implementation related to nonlinearities in this over-actuated thermal system.

Designing a control system that can robustly detect faulted emission control devices under all environmental and driving conditions is a challenging task for OEMs. In order to gain confidence in the control strategy and the values of tunable parameters, the test vehicles need to be subjected to their limits during the development process. Complexity of modern powertrain systems along with the On-Board Diagnostic (OBD) monitors with multidimensional thresholds make it difficult to anticipate all the possible scenarios. Finding optimal solutions to these problems using traditional calibration processes can be time and resource intensive. A possible solution is to take a data driven calibration approach. In this method, a large amount of data is collected by collaboration of different groups working on the same powertrain. Later, the data is mined to find the optimum values of tunable parameters for the respective vehicle functions.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

In the automobile industry, the reliability and predictive capabilities of computer models for a dynamic system need to be assessed quantitatively. Quantitative validation allows engineers to assess and improve model reliability and quality objectively and ultimately lead to potential reduction in the number of prototypes built and tests. A good metric, which is essential in model validation, requires considering uncertainties in both testing and computer modeling. In addition, it needs to be able to compare multiple responses simultaneously, as multiple quantities are often encountered at different spatial and temporal points of a dynamic system. In this paper, a state-of-the-art validation technology is developed for multivariate complex dynamic systems by exploiting a probabilistic principal component analysis method and Bayesian statistics approach.

Described in this paper is a component test methodology to evaluate the door trim armrest performance in an Insurance Institute for Highway Safety (IIHS) side impact test and to predict the SID-IIs abdomen injury metrics (rib deflection, deflection rate and V*C). The test methodology consisted of a sub-assembly of two SID-IIs abdomen ribs with spine box, mounted on a linear bearing and allowed to translate in the direction of impact. The spine box with the assembly of two abdominal ribs was rigidly attached to the sliding test fixture, and is stationary at the start of the test. The door trim armrest was mounted on the impactor, which was prescribed the door velocity profile obtained from full-vehicle test. The location and orientation of the armrest relative to the dummy abdomen ribs was maintained the same as in the full-vehicle test.

An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described. Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations.

This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process.

This paper starts describing the in-mold grain lamination and bilaminated film cover when applied to instrument panels with seamless passenger air bag doors. It then offers a comparison between two different PAB door weakening processes, the laser scoring and the scoring by milling. It further discuss the scoring by milling process and analyses its implementation on a real case instrument panel. In the implementation case, the scoring pattern is checked against a pre-defined engineering specification and correlated to the results of a drop tower test, which shows the force necessary to break the PAB door. Three iterations are performed until the results for scoring pattern and breaking force are achieved. The breaking force results are then statistically validated against the specification and capability analysis.

Customer perception of vehicle quality and safety is based on many factors. One important factor is the customers impression of the sounds produced by body and interior components such as doors, windows, seats, safety belts, windshield wipers, and other similar items like sounds generated automatically for safety and warning purposes. These sounds are typically harmonic or constant, and the relative level of perception, duration, multiplicity, and degree of concurrence of these sounds are elements that the customer will retain in an overall quality impression. Chime sounds are important to the customer in order to alert that something is not accomplished in a right way or for safe purposes. The chimes can be characterized by: sound level perception, frequency of the signal, shape of the signal, duration of the “beep” and the silence duration.

This study examines the biofidelity, repeatability, and reproducibility of various anthropomorphic devices in rear impacts. The Hybrid III, the Hybrid III with the RID neck, and the TAD-50 were tested in a rigid bench condition in rear impacts with ΔVs of 16 and 24 kph. The results of the tests were then compared to the data of Mertz and Patrick[1]. At a AV of 16 kph, all three anthropomorphic devices showed general agreement with Mertz and Patrick's data [1]. At a AV of 24 kph, the RID neck tended to exhibit larger discrepancies than the other two anthropomorphic devices. Also, two different RID necks produced significantly different moments at the occipital condyles under similar test conditions. The Hybrid III and the Hybrid III with the RID neck were also tested on standard production seats in rear impacts for a AV of 8 kph. Both the kinematics and the occupant responses of the Hybrid III and the Hybrid III with the RID neck differed from each other.

In order to quantify the dynamic restraint capability of a passenger airbag, a sub-system test method has been developed. The sub-system included a passenger airbag, an adjustable generic instrument panel (IP) and an adjustable windshield. The test was called the Passenger Air Bag Linear Impactor Test (PABLIT). This test method could be used for not only A to B comparisons, but also to evaluate the performance of any PAB module design in general, including performance variability of airbag systems. A variety of impactor, pendulum and drop tower test methods are currently used by inflatable restraint suppliers. PABLIT was aimed to standardize airbag testing and data analysis. Various production hardware designs were tested to investigate the characteristics of the sub-assemblies that provide restraint capability.

Head restraint has become an important element in seat design due to the severity of neck injuries in rear-end collisions. The objective of this paper is to present an analytical and efficient approach to assist engineers in analyzing the design parameters of the seat and head restraint system. The CAE simulation models with Bio-RID dummy were assembled to correlate to 10 mph rear impact sled tests. The correlated models were then adopted in Design of Experiment (DOE) studies to explore all the significant design parameters influencing occupant neck injuries. Based on the results from the DOE studies, we are able to improve the seat and head restraint designs for reducing the risk of neck injuries in rear-end impacts.

This paper examines the neck tension force (Fz) of the BioRid II dummy in the IIHS (Insurance Institute of Highway Safety) rear impact mode. The kinematics of the event is carefully reviewed, followed by a detailed theoretical analysis, paying particular attention to the upper neck tension force. The study reveals that the neck tension should be approximately 450N due to the head inertia force alone. However, some of the tests conducted by IIHS had neck tension forces as high as 1400N. The theory of head hooking and torso downward pulling is postulated in the paper, and various publicly available IIHS rear impact tests are examined against the theory. It is found in the analysis that in many of those tests with high neck tension forces, the locus of the head restraint reaction force travels on the dummy's skull cap, and eventually moves down underneath the skull cap, which causes “hooking” of the head on the stacked-up head restraint foam.

An assessment tool was developed for rollover CAE signals evaluation to assess primarily the qualities of CAE generated sensor waveforms. This is a key tool to be used to assess CAE results as to whether they can be used for algorithm calibration and identify areas for further improvement of sensor. Currently, the method is developed using error estimates on mean, peak and standard deviation. More metrics, if necessary, can be added to the assessment tool in the future. This method has been applied to various simulated signals for laboratory-based rollover test modes with rigid-body-based MADYDO models.

There are a wide variety of approaches to model the automotive seat and occupant interaction. This paper traces the studies conducted for simulating the occupant to seat interaction in frontal and/or rear crash events. Starting with an initial MADYMO model, a MADYMO-LS/DYNA coupled model was developed. Subsequently, a full Finite Element Analysis model using LS/DYNA was studied. The main objective of the studies was to improve the accuracy and efficiency of CAE models for predicting the dummy kinematics and structural deformations at the restraint attachment locations in laboratory tests. The occupant and seat interaction was identified as one of the important factors that needed to be accurately simulated. Quasi-static and dynamic component tests were conducted to obtain the foam properties that were input into the model. Foam specimens and the test setup are discussed. Different material models in LS/DYNA were evaluated for simulating automotive seat foam.

A passenger airbag is an important part of a vehicle restraint system which provides supplemental protection to an occupant in a crash event. New Federal Motor Vehicle Safety Standards No. 208 requires considering multiple crash scenarios at different speeds with various sizes of occupants both belted and unbelted. The increased complexity of the new requirements makes the selection of an optimal airbag shape a new challenge. The aim of this research is to present an automated optimization framework to facilitate the airbag shape design process by integrating advanced tools and technologies, including system integration, numerical optimization, robust assessment, and occupant simulation. A real-world frontal impact application is used to demonstrate the methodology.

The Federal Motor Vehicle Safety Standard or FMVSS 208 requires passenger cars, multi-purpose vehicles, trucks with less than unloaded vehicle weight of 2,495 kg either to have an automatic suppression feature or to pass the injury criteria specified under low risk deployment test requirement for a 1 year old dummy in rearward and forward facing restraints as well as a forward facing 3 and 6 year old dummy. A convertible child seat was installed in a sub-system test buck representing a passenger car environment with a one-year- old dummy in it at the passenger side seat and a passenger side airbag was deployed toward the convertible child seat. A MADYMO model was built to represent the test scenario and the model was correlated and validated to the results from the experiment.

This study applies a detailed finite element model of the human body to simulate occupant knee impacts experienced in vehicular frontal crashes. The human body model includes detailed anatomical features of the head, neck, chest, thoracic and lumbar spine, abdomen, and lower and upper extremities. The material properties used in the model for each anatomic part of the human body were obtained from test data reported in the literature. The total human body model used in the current study has been previously validated in frontal and side impacts. Several cadaver knee impact tests representing occupants in a frontal impact condition were simulated using the previously validated human body model. Model impact responses in terms of force-time and acceleration-time histories were compared with test results. In addition, stress distributions of the patella, femur, and pelvis were reported for the simulated test conditions.

The relation cost versus performance in the design of an automobile is crucial for its success. These two characteristics, much like the project development timing, are closely related to the attributes that the new design must achieve (e.g. weight, fuel economy, torsional stiffness, NVH, safety, etc.). In this respect, the design optimization of body reinforcements (i.e. part thickness, quantity of reinforcements, and number of spot welds) contributes greatly to a sound and robust project concept. This paper describes one application of 6-Sigma methodology to evaluate the performance of different configurations of seat belt reinforcements resulting in an optimized concept that achieved the proposed performance targets with weight and sub-assembly complexity reduction. Using a Design of Experiments (DOE) and Finite Element Analysis (FEA), each proposal was evaluated for its resistance to plastic deformation.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality.