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The development of complex engineering systems often encounters various challenges in terms of meeting New Product Development (NPD) assigned budget, launch time, and system performance goals. Most of the NPD processes have been experiencing challenges to meet these goals within an increasingly competitive global market environment. These challenges become more complicated to manage when the development process is long with different sources of uncertainty. Despite decades of industrial experience and academic research efforts in managing NPD processes, it is observed that designing and developing increasingly complex systems, e.g., automotive, is still subjected to significant cost overrun, schedule delays, and functional issues during early design stages. To provide a Reliability Growth Planning (RGP) model, several inputs are required, e.g., the initial reliability estimation, the reliability goal, test recourses, and the duration of the design or test period.

Acoustic comfort of automotive cabins has progressively become one of the key attributes of passenger comfort within vehicle design. Wind noise and the heating, ventilation, and air conditioning (HVAC) system noise are two of the key contributors to noise levels heard inside the car. The increasing prevalence of hybrid technologies and electrification has an associated reduction in powertrain noise levels. As such, the industry has seen an increasing focus on understanding and minimizing HVAC noise, as it is a main source of noise in the cabin particularly when the vehicle is stationary. The complex turbulent flow path through the ducts, combined with acoustic resonances can potentially lead to significant noise generation, both broadband and tonal.

Simplified load path models (SLMs) of the body in white (BIW) are an important tool in the body structure design process providing a highly flexible, idealized concept model to explore the design space through load path evaluation, material selection, and section optimization with rapid turnaround. In partnership with Altair Engineering, the C123 process was used to create and optimize SLMs for BIW models at FCA US LLC. These models help structures engineers to develop efficient load paths, sections, and joints for improved NVH as ultra-high-strength steels enable thinner gauges throughout the body structure. A few key differences in the SLM modeling method are contrasted to previous simplified BIW modeling methods. One such example is the parameterization of cross sections through response surface models rather than using contemporary finite element descriptions of arbitrary cross sections.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.

This paper describes a simplified CAE simulation for the calculation of peak strut force in a damped tailgate system. Tailgate systems in the past included a torsion rod for lift assist, but did not include damping in the opening/drop direction. Newer tailgate systems include a strut to provide damping during drop and possibly lift assist. These tailgates may also be designed to experience a free-fall for a few degrees after being unlatched and before the strut engages. When the latch is released, the tailgate accelerates as it falls freely and then encounters a rapidly increasing damping force as the strut engages. The deceleration of the gate depends upon the damping force, yet the damping force depends upon the deceleration of the tailgate, making a precise calculation of the damping force difficult. Damping characteristics of the strut depend on the volume, the viscosity of oil and its flow restrictions across the piston. These parameters are not known early in the design process.

The latest trend in transmission hydraulic controls development ise body integrated direct acting control solenoid, ted by multiple automotive OEMs. The advantages of integrated direct acting control solenoids are key enablers for OEMs to meet more and more stringent fuel economy requirement and competitive environment. In the meantime, there are unique challenges in both designing and manufacturing of such solenoids, due to the fact the solenoid armature can only push the spool valve with limited force and limited stroke. Through analytical methods, this paper explains design guidelines to overcome the challenges and quantifies the impact of design decision to critical functional objectives. Multiple valve design configurations, including both normally low and normally high functionality, are covered in the analysis. Unique manufacturing process concerns are also addressed.

Mixture formation and combustion dynamics are the primary contributors to the performance and emission characteristics of direct-injected spark ignition (SI) engines. This requires assessing the benefits and tradeoffs of the design and control factors that influence mixing and the subsequent combustion event. In this study, Taguchi's L18 orthogonal array design of experiment (DoE) methodology has been applied to assess contributions and tradeoffs of varied compression ratio, piston bowl design, intake port tumble design, injector spray pattern, injection timing, injection pressure, exhaust gas recirculation (EGR) rate, and intake valve closing timing in a single-cylinder boosted gasoline direct injection (GDI) SI engine. This multiparameter study has been carried out across three speed-load conditions representative of typical automotive application operating ranges.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.

Vehicle multi-zone automatic Heating, Venting and Air Conditioning (HVAC) is the advanced form of the traditional air conditioning. The advantage of multi-zone automatic HVAC is that it allows the passengers of a vehicle to set a desired temperature for their own zone within the vehicle compartment. This desired temperature is then maintained by the HVAC system, which determines how best to control the available environment data to provide optimal comfort for the passengers. To achieve overall thermal comfort of the occupants in a vehicle, multi-zone HVAC takes things a step further by adding heated steering wheel and heated/vented seats to the overall HVAC control strategy. The heating and cooling of the occupants by this integrated system is performed by complex control algorithms in form of embedded software programs and Private LIN network. This paper describes the approach and tools used to develop, simulate and validate the multi-zone integrated climate control system.

Vibrations affect vehicle occupants and should be prevented early in design process. Powertrain (PT) wiggle is one of the well-known issues. It is the 3rd order lateral vibration, forced by half shaft inner LH/RH plunging tripod joints [1,2]. Lateral PT resonance (7-15Hz) occurs at certain vehicle speed during acceleration and may excite lateral, pitch and roll PT modes. Typically, PT wiggle occurs in speed range of 5-25kph. Vibration is noticeable on driver and passenger seats mostly in lateral direction. The inner half shaft joints are the major source of vibration. Unfortunately, existing MBD tools like Adams [3] are missing detailed tripod joint representation because of complex mechanical interactions inside the joint. At least three sliding contacts between tripod rollers and joint housing, lubricant inside the can and combination of rotation and plunging make the modeling too complicated.

Designing an efficient transient thermal system model has become a very important task in improving fuel economy. As opposed to steady-state thermal models, part of the difficulty in designing a transient model is optimizing a set of input parameters. The first objective in this work is to develop an engine compatible physics-based 1D thermal model for fuel economy and robust control. In order to capture and study the intrinsic thermo-physical nature, both generic “Three Mass” and “Eight Mass” engine models are developed. The models have been correlated heuristically using Simulink. This correlation and calibration process is challenging and time consuming, especially in the case of the 8-mass model. Hence, in this work a Particle Swarm Optimizer (PSO) method has been introduced and implemented on a simple 3-mass and more complex 8-mass engine thermal model in order to optimize the input parameters.

A vehicle’s exterior fit and finish, in general, is the first system to attract customers. Automotive exterior engineers were motivated in the past few years to increase their focus on how to optimize the vehicle’s exterior panels split lines quality and how to minimize variation in fit and finish addressing customer and market required quality standards. The design engineering’s focus is to control the deviation from nominal build objective and minimize it. The fitting process follows an optimization model with the exterior panel’s location and orientation factors as independent variables. This research focuses on addressing the source of variation “contributed factors” that will impact the quality of the fit and finish. These critical factors could be resulted from the design process, product process, or an assembly process. An empirical analysis will be used to minimize the fit and finish deviation.

Nowadays development of automotive HVAC is a challenging task wherein thermal comfort and safety are very critical factors to be met. HVAC system is responsible for the demisting and defrosting of the vehicle’s windshield and for creating/maintaining a pleasing environment inside the cabin by controlling airflow, velocity, temperature and purity of air. Fog or ice which forms on the windshield is the main reason for invisibility and leads to major safety issues to the customers while driving. It has been shown that proper clear visibility for the windshield could be obtained with a better flow pattern and uniform flow distribution in the defrost mode of the HVAC system and defrost duct. Defroster performance has received significant attention from OEMs to meet the specific global performance standards of FMVSS103 and SAE J902. Therefore, defroster performance is seriously taken into consideration during the design of HVAC system and defroster duct.

The automotive door structure experience various static and dynamic loading conditions while going through an opening and closing operation. A typical swing door is attached to the body with two hinges and a check strap. These mechanisms carry the loads while the door is opened. Similarly, while closing the door, the latch/striker mechanism along with the seal around the periphery of the door react all loads. Typically, computer aided engineering (CAE) simulations are performed considering a nominal manufacturing (or build) tolerance condition, that results in one loading scenario. But while assembling the door with the body, the build variations in door mechanisms mentioned above can result in different loading scenarios and it should be accounted for design evaluation. This paper discusses various build tolerances and its effect on door durability performances to achieve a robust door design.

Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise.

Simplified Loadpath Models (SLMs) of the advanced body in white (BIW) design concept provide a highly flexible and rapid platform to explore body structure loadpath alternatives and conduct performance:weight optimization. The SLM modelling process combines higher order Beam and Bush finite elements with coarsened Shell-meshed panels to represent the body structure. While the benefits of loadpath optimization through Beam element parameter variation is well-documented and applied extensively for these types of models, this paper covers another valuable benefit of the SLMs; to provide a better understanding of the sensitivities and influence of joint stiffnesses on key body structure attributes. This data provides valuable information that can be leveraged to support more intelligent and efficient body structure joint designs.

In an Internal Combustion (IC) Engine, the exhaust manifold has the primary function of channeling products of combustion from cylinder head runners to the emissions system through a collector. Exhaust manifolds must endure severe thermal loads and high strain caused by channeling extremely hot gases and fastener loads, respectively. The combination of these two loads can lead to Thermomechanical Fatigue (TMF) failures after repeated operational cycles if they are not assessed and addressed adequately during the design process. Therefore, it is vital to have a methodology in place to evaluate the life of an engine component (such as the exhaust manifold) using a TMF damage prediction model. To accomplish this, spatial temperature prediction and maximum value attained, as well as temporal distribution, are the most important input conditions.

For the designing of world class vehicles, ride comfort is one of the criteria that vehicle manufacturers are constantly trying to improve. The automotive seating system is an important sub-system in a vehicle that contributes to the ride comfort of the vehicle occupants. Seat vibrations are perceived by the occupants and make them feel uncomfortable during driving conditions. These vibrations are majorly transferred from engine and road excitation loads. For road excitation loads, the road testing may not be accurately repeatable, and measurements based on four post shakers are used to assess the discomfort. The major challenges for the vehicle manufactures is the availability of physical prototypes at an early stage of vehicle development and any changes in the design due to test validation leads to huge cost and time.