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A variety of structures resonate when they are excited by external forces at, or near, their natural frequencies. This can lead to high deformation which may cause damage to the integrity of the structure. There have been many applications of external devices to dampen the effects of this excitation, such as tuned mass dampers or both semi-active and active dampers, which have been implemented in buildings, bridges, and other large structures. One of the active cancellation methods uses centrifugal forces generated by the rotation of an unbalanced mass. These forces help to counter the external excitation force coming into the structure. This research focuses on active force cancellation using centrifugal forces (CFG) due to mass imbalance and provides a virtual solution to simulate and predict the forces required to cancel external excitation to an automotive structure. This research tries to address the challenges to miniaturize the CFG model for a body-on-frame truck.

Water fording events are one of the most challenging situations that vehicles undergo during their lifetime. During these events the underbody components (e.g. Front fascia, Bellypan, wheel liner etc.) are subject to very high loads. Typically, vehicle water fording tests are performed for various depths of water at prescribed vehicle speeds. Water fording tests are usually carried out during the proto phase of the vehicle development program to ensure acceptable performance. If issues are discovered, making changes to the fascia or body panels are typically very expensive. To avoid late changes, a fully virtual methodology was developed to facilitate vehicle water fording performance. The simulation is targeted to evaluate multiple aspects such as air induction system and estimation of hydrodynamic loads on body panel components.

Many chassis and powertrain components in the transportation and automotive industry experience multi-axial cyclic service loading. A thorough load-history leading to durability damage should be considered in the early vehicle production steps. The key feature of rubber fatigue analysis discussed in this study is how to define local critical location strain time history based on nominal and complex load time histories. Material coupon characterization used here is the crack growth approach, based on fracture mechanics parameters. This methodology was utilized and presented for a truck engine mount. Temperature effects are not considered since proving ground (PG) loads are generated under isothermal high temperature and low frequency conditions without high amounts of self-heating.

The Los Angeles City Traffic (LACT) brake test is well known acclaimed procedure used by many vehicle manufacturers to assess the brake pad wear behavior and to investigate the Noise, Vibration and Harness (NVH) performance of the brake system. The LACT driving route consists of a set of real-world driving conditions, which has been considered representative of the US passenger vehicle market. The scope of this study is to mimic the LACT test using finite element analysis (FEA) to calculate the wear displacement based on Rhee’s theory. The Leading-edge and trailing edge of the brake pad’s wear tendency is also predicted from the simulation. The finite element model for wear simulation consists of brake system viz., Rotor, Knuckle, Pads, Anchor bracket, Piston, and Caliper.

Fasteners, commonly used in automotive industry, play an important role in the safety and reliability of the vehicle structural system. In practical application, bolted joints would never undergo fully reversed loading; there always will be positive mean stress on bolt. The mean stress has little influence on the fatigue life if the maximum stress is lower than a threshold which is near the yield stress of the bolt. However, when the sum of the mean stress and the stress amplitude exceeds the threshold, the endurance limit stress amplitude decreases fast as the mean stress increases. The purpose of this paper is to research the fatigue endurance limit of a fastener and establish the threshold for safe design in automotive application. In order to obtain the fatigue endurance limit at different mean stress levels, various mechanical tests were performed on M12x1.75 and M16x1.5 Class 10.9 fasteners using MTS test systems.

Reducing emissions and the carbon footprint of our society have become imperatives requiring the automotive industry to adapt and develop technologies to strive for a cleaner sustainable transport system and for sustainable economic prosperity. Electrified hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV) and range extender powertrains provide potential solutions for reducing emissions, but they present challenges in terms of thermal management. A key requirement for meeting these challenges is accurately to predict the thermal loading and temperatures of an internal combustion engine (ICE) quickly under multiple full-load and part-load conditions. Computational Fluid Dynamics (CFD) and thermal survey database methods are used to derive thermal loading of the engine structure and are well understood but typically only used at full-load conditions.

Cargo box is one of the indispensable structures of a pickup truck which makes it capable of transporting heavy cargo weights. This heavy cargo weight plays an important role in durability performance of the box structure when subjected to road load inputs. Finite element representation for huge cargo weight is always challenging, especially in a linear model under dynamic proving ground road load durability analysis using a superposition approach. Any gap in virtual modeling technique can lead to absurd cargo box modes and hence durability results. With the existing computer aided engineering (CAE) approach, durability results could not correlate much with physical testing results. It was crucial to have the right and robust CAE modeling technique to represent the heavy cargo weight to provide the right torsional and cargo modes of the box structure and in turn good durability results.

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.

Accurate prediction of in-vehicle powertrain bounce mode is necessary to ensure optimum responses are achieved at driver’s touch points during 4post shake or rough road shake events. But, during the early stages of vehicle development, building a detailed vehicle finite element (FE) model is not possible and often powertrain bounce modes are computed assuming the powertrain to be a stand-alone unit. Studies conducted on FE models of a large SUV with body on frame architecture showed that the stand-alone approach overestimates the powertrain bounce mode. Consequently, there is a need for a simplified version of vehicle model which can be built early on to compute powertrain modes. Previously, representing all the major components as rigid entities, simplified unibody vehicle models have been built to compute powertrain modes. But such an approach would be inaccurate here, for a vehicle with body on frame architecture due to the flexible nature of the frame (even at low frequencies).

In this study we collect and analyze data on how hands-free automated lane centering systems affect the controllability of a hazardous event during an operational situation by a human operator. Through these data and their analysis, we seek to answer the following questions: Is Level 2 and Level 3 automated driving inherently uncontrollable as a result of a steering failure? Or, is there some level of operator control of hazardous situations occurring during Level 2 and Level 3 automated driving that can reasonably be expected, given that these systems still rely on a driver as the primary fall back. The controllability focus group experiments were carried out using an instrumented MY15 Jeep® Cherokee with a prototype Level 2 automated driving system that was modified to simulate a hands-free steering system on a closed track with speeds up to 110kph. The vehicle was also fitted with supplemental safety measures to ensure experimenter control.

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.

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.

Design for Six Sigma (DFSS) is an essential tool and methodology for innovation projects to improve the product design/process and performance. This paper aims to present an application of the DFSS Taguchi Method for an automotive/vehicle component. High-Pressure Vacuum Assist Die Casting (HPVADC) technology is used to make Cast Aluminum Front Shock Tower. During the vehicle life, Shock Tower transfers the road high impact loads from the shock absorber to the body structure. Proving Ground (PG) and washout loads are often used to assess part strength, durability life and robustness. The initial design was not meeting the strength requirement for abusive washout loads. The project identified eight parameters (control factors) to study and to optimize the initial design. Simulation results confirmed that all eight selected control factors affect the part design and could be used to improve the Shock Tower's strength and performance.

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.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

Artificial intelligence (AI) is dramatically changing multiple industries. AI’s potential to transform Computer-Aided Engineering (CAE) cannot be overlooked. Conventionally, Finite Element Analysis (FEA) is the simulation of any given physical phenomenon to obtain an approximate solution to a group of problems governed by Partial Differential Equations (PDE). Implementation of AI methods in this area combines human intelligence with numerical solutions to make them more efficient. This paper attempts to develop a Deep Neural Network (DNN) model to solve an elasto-plastic impact problem of a symmetric short crush tube made of three materials impacted by a moving wall. A structured learning database was established to train and validate the model using finite element simulations. Tube size, gauge and elasto-plastic material properties were used as input attributes or features. The maximum axial displacement of the tube is the target label to predict.

Accuracy of efficiency maps for permanent magnet synchronous machines depends on loss and torque calculations. Losses that occur in the stator or rotor core material as well as copper loss that occurs in the winding at high speeds are non-linear, and can be predicted by Finite Element Analysis (FEA) precisely. Though prediction is precise with FEA, the technique introduces substantial computational overhead. Replacing FEA with analytical estimates of the losses expedites loss modeling, but reduces precision of loss estimates, making efficiency calculations less precise as well. The prediction method proposed in this paper potentially offers accuracy similar to that attained using FEA while reducing the computational overhead typically required.

Multiphase machines continue to increase in popularity in high power applications due to their proven benefits compared to their three-phase counterparts. However, with the increased phase number and, therefore, the increased number of degrees of freedom, the complexity of both modelling and control strategies significantly increases. This paper proposes a dynamic modelling method for six-phase interior permanent magnet machines using the vector space decomposition transformation, which can be extended to machines with any number of phases. The proposed technique considers the nonlinear characteristics of the machine, such as spatial harmonics, magnetic saturation, and cross-coupling, which are based on flux linkage look-up tables from finite element analysis. The main contribution of this paper is the consideration of the effect of harmonic components and asymmetries within the machine windings on losses.

Currently the On-Board Diagnostics (OBD) requirements enforced by government agencies do not cover electric vehicles. Although the California Air Resources Board (CARB) mandates all light and medium duty vehicles and heavy duty engine dynamometer certified engines equipped with fossil fuel-powered engines, including all hybrid vehicles, must follow the OBD requirements in California Code of Regulation (CCR) 1968.2 and 1971.1, Battery Electric vehicles (BEVs), are exempted from OBD requirements. The legislators, such as CARB, have started to make proposals for on-board systems to monitor electric propulsion system health. In addition, there may be customer needs to obtain standard vehicle service information and the Original Equipment Manufacturers (OEMs) may also have the desire for common diagnostic strategies across different vehicle applications to lower the development costs.

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.