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The use of model-based software development in automotive applications has increased in recent years. Current vehicles contain millions of lines of code, and millions of dollars are spent each year fixing software issues. Most new features are software controlled and many times include distributed functionality, resulting in increased vehicle software content and accelerated complexity. To handle rapid change, OEMs and suppliers must work together to accelerate software development and testing. As development processes adapt to meet this challenge, model-based design can provide a solution. Model-based design is a broad development approach that is applied to a variety of applications in various industries. This paper reviews a project using the MATLAB/Simulink/Stateflow environment to complete a functional model of a low cost radio.

Inverse numerical acoustics is a method which reconstructs the source surface normal velocity from the sound measured in the near-field around the source. This is of particular interest when the source is rotating or moving, too light or too hot to be instrumented by accelerometers. The use of laser vibrometers is often of no remedy due to the complex shape of the source. The Inverse Numerical Acoustics technique is based on the inversion of transfer relations (Acoustic Transfer Vectors) using truncated Singular Value Decomposition (SVD). Most of the time the system is underdetermined which results in a non unique solution. The solution obtained by the truncated SVD is the minimal solution in the RMS sense. This paper is investigating the impact of non homogeneities in the mesh density (local mesh refinement) on the retrieved solution for underdetermined systems. It will be shown that if transfer quantities are inverted as such, big elements get a higher weight in the inversion.

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.

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.

In this paper, a systems engineering approach is explored to evaluate the effect of design parameters that contribute to the performance of the embedded Automatic Speech Recognition (ASR) engine in a vehicle. This includes vehicle designs that influence the presence of environmental and HVAC noise, microphone placement strategy, seat position, and cabin material and geometry. Interactions can be analyzed between these factors and dominant influencers identified. Relationships can then be established between ASR engine performance and attribute performance metrics that quantify the link between the two. This helps aid proper target setting and hardware selection to meet the customer satisfaction goals for both teams.

This paper describes two case studies in which multiple microphone processing (beamforming) and microphone location were evaluated to determine their impact on improving embedded automatic speech recognition (ASR) in a vehicle hands-free environment. While each of these case studies was performed using slightly different evaluation set-ups, some specific and general conclusions can be drawn to help guide engineers in selecting the proper microphone location and configuration in a vehicle for the improvement of ASR. There were some outcomes that were common to both dual microphone solutions. When considering both solutions, neither was equally effective across all background noise sources. Both systems appear to be far more effective for noise conditions in which higher frequency energy is present, such as that due to high levels of wind noise and/or HVAC (heating, ventilation and air conditioning) blower noise.

This paper describes a method to validate in-vehicle speech recognition by combining synthetically mixed speech and noise samples with batch speech recognition. Vehicle cabin noises are prerecorded along with the impulse response from the driver's mouth location to the cabin microphone location. These signals are combined with a catalog of speech utterances to generate a noisy speech corpus. Several factors were examined to measure their relative importance on speech recognition robustness. These include road surface and vehicle speed, climate control blower noise, and driver's seat position. A summary of the main effects from these experiments are provided with the most significant factors coming from climate control noise. Additionally, a Signal to Noise Ratio (SNR) experiment was conducted highlighting the inverse relationship with speech recognition performance.

Nowadays, the vehicle design is highly ruled by the increasing customer demands and expectations. In addition to ride comfort and vehicle handling, the Noise, Vibration and Harshness (NVH) behavior of the powertrain is also a critical factor that has a big impact on the customer experience. To evaluate the powertrain NVH characteristics, the NVH error states should be studied. A typical NVH event could be decoupled into 3 parts: source, path, and receiver. Take-off shudder, which evaluates the NVH severity level during vehicle take-off, is one of the most important NVH error states. The main sources of Front Wheel Drive (FWD) take-off shudder are the plunging Constant Velocity Joints (CVJ) on the left and right half shafts. This is because a plunging CVJ generates a third order plunging force with half shaft Revolution Per Minute (RPM), which is along the slip of the plunging CVJ.

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.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

In recent years, the design of automotive components and assemblies have resulted in an over-reliance on advanced CAE tools especially the Finite Element Analysis. An emphasis on cost reduction and commonization of components in automotive industry has made it necessary to use the CAE tools in innovative ways. Use of FEA as a effective product development tool can be greatly enhanced if it provides a high degree of correlation with physical tests, thereby greatly limiting the investment in expensive prototypes and testing. This paper will discuss a robustness based methodology to realize effective correlation of finite element models with actual physical tests on automotive seat structure assembly, at a component, sub-system, and systems level. Based on a parameter design approach, the various factors that affect the degree of correlation between CAE models and physical tests will be described.

A successful piston design requires eliminate the following failure modes: structure failure, skirt scuffing and piston unusual noise. It also needs to deliver least friction to improve engine fuel economy and performance. Traditional approach of using hardware tests to validate piston design is technically difficult, costly and time consuming. This paper presents an up-front CAE tool and an analytical process that can systematically address these issues in a timely and cost-effectively way. This paper first describes this newly developed CAE process, the 3D virtual modeling and simulation tools used in Ford Motor Company, as well as the piston design factors and boundary conditions. Furthermore, following the definition of the piston design assessment criteria, several piston design studies and applications are discussed, which were used to eliminate skirt scuffing, reduce piston structure dynamic stresses, minimize skirt friction and piston slapping noise.

This paper discusses the optimization of an automotive hydraulic steering pump pulley design for improved in-vehicle pump NVH performance. Levels of steering pump whine noise heard inside a vehicle were deemed objectionable. Vehicle and component transfer path analyses indicated that the dominant noise path for the whine noise was airborne in nature. Subsequent experimental modal analysis indicated that the steering pump pulley was a major contributor to the amount of radiated noise produced by the pump/pulley system. CAE analysis was used to further analyze the dynamic behavior of the pulley and develop an optimized design with decreased noise radiation efficiency. The results predicted with the CAE analysis were verified in-vehicle, resulting in a vehicle with acceptable steering pump whine noise performance.

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties.

Sunroof is placed in certain high-end vehicles to give user a better driving experience. All automakers are searching alternatives to reduce weight and cost in the vehicle, in which sunroofs are also impacted. Some alternatives are already applied, as a honeycomb paper used in some sunshades that presents benefits, as less weight and with a good cost reduction. Although, due the reduced weight for this part produced in this material, it shows more susceptibility to reproduce the vibration that vehicle propagates in movement, especially in bad condition roads. The sunroof assembly is dependent of the roof reinforcement and roof skin, but in this special case, the validation could be done in the components itself because the interaction of the sunshades is directly dependent of the other sunroof parts, as rails and front frame.

Optimization of heavy materials like steel, in order to create a lighter vehicle, it is a major goal among most automakers, since heavy vehicles simply cannot compete with a lightweight model's fuel economy. Thinking this way, this paper shows a case study where the Size Optimization technique is applied to a front floor reinforcement. The reinforcement is used by two different vehicles, a subcompact and a crossover Sport Utility Vehicle (SUV), increasing the problem complexity. The Size Optimization technique is supported by Finite Element Method (FEM) tools. FEM in Computer Aided Engineering (CAE) is a numerical method for solving engineering problems, and its use can help to optimize prototype utilization and physical testing.

Squeak and rattle are two undesirable occurrences during component operation and during vehicle driving condition, resulting in one of the top complains from costumers. One common grievance could happen during the user exterior handle operation and during side door closing. The exterior handle system during the operation could generate a squeak between interface parts, if materials and geometric tolerances was not been carefully designed. Also, vibration generated during door closing effort, might generate squeak between parts since the reinforcement for exterior handle touches the outer sheet metal internally. For this reason several guidelines might be included to avoid potential noise condition for this system during vehicle lifetime as correct material reduce friction between parts, taking into consideration the geometric condition between parts. Plus, coupling system on handles two pieces should also be evaluated to avoid squeak during use.

The influence of the pin/main bearing journal diameters and the counter weight orientation to crankshaft axial vibration were examined using a fully flexible finite element engine model. The simulation, performed with AVL/Excite, incorporates modal contributions of all interfacing engine components and enables the study of interactions among the components. The simulation successfully predicted crankshaft axial vibration and was validated by measurements. The correlated CAE model was used to study modifications in crankshaft pin and main bearing journal diameters and in the orientation of the crankshaft counter weights. Design direction on how to minimize axial vibration was provided.

A measurement technique is presented to detect and quantify piston pin tick noise, thereby aiding optimization of piston pin bushing design. Furthermore, the characteristics of two types of piston pin noise are described. The first is caused by excessive clearance between the pin and the connecting rod bushing. A noncircular clearance between the pin and the connecting rod bushing causes the second type of the piston pin tick noise. Finally, a process is discussed to consider pin tick in the design and verification of the piston and connecting rod assembly. The method presented could also be used to investigate other unusual engine noises.

In this paper, a new technology for the design of silent transaxles is developed, where topography optimization is adopted and an artificial parameter called β is proposed as an objective function, representing an upper bound of the surface velocity. The strategy of the optimization is to minimize β while getting the surface velocities less than β. as the constraints. A numerical example of reducing transaxle's radiated noise by using the new optimization technology is given in the paper. In the example, an entire Ford transaxle system was modeled numerically, where most internal components were included. First a modal frequency velocity analysis was conducted. Then an acoustic power analysis based on the Acoustic Transfer Vector (ATV) was carried out. Finally, a topography optimization based on the β - method for the transaxle was performed to minimize the radiated noise.