Search Results

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

Developing lightweight, stiff and crash-resistant vehicle body structures requires a balance between part geometry and material properties. High strength materials suitable for crash resistance impose geometry limitations on depth of draw, radii and wall angles that reduce geometric efficiency. The introduction of 3rd generation Advanced High Strength Steels (AHSS) can potentially change the relationship between strength and geometry and enable simultaneous improvements in both. This paper will demonstrate applicability of 3rd generation AHSS with higher strength and ductility to replace the 780 MPa Dual Phase steel in a sill reinforcement on the current Jeep Cherokee. The focus will be on formability, beginning with virtual simulation and continuing through a demonstration run on the current production stamping tools and press.

Engine air induction shell noise is a structure borne noise that radiates from the surface of the air induction system. The noise is driven by pulsating engine induction air and is perceived as annoying by vehicle passengers. The problem is aggravated by the vehicle design demands for low weight components packaged in an increasingly tight under hood environment. Shell noise problems are often not discovered until production intent parts are available and tested on the vehicle. Part changes are often necessary which threatens program timing. Shell noise should be analyzed in the air induction system design phase and a good shell noise analytical process and targets must be defined. Several air induction clean side ducts are selected for this study. The ducts shell noise is assessed in terms of material strength and structural stiffness. A measurement process is developed to evaluate shell noise of the air induction components. Noise levels are measured inside of the clean side ducts.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

During driving conditions, when it is needed to transition from Electric Vehicle (EV) to Hybrid Vehicle operation, synchronization of the engine with the shaft and transmission is essential to enable clutch engagement and, subsequently, providing engine power to the wheels. Challenges arise when the engine must generate power to move itself and cannot rely on electric motors for precision. Cost-effective hybrid vehicle propulsion architectures which utilize small 12V belt-starter generators (BSGs) to initiate engine activation are inherently affected. In these situations, a speed profile that balance rapid response and control effort while considering system limitations to mitigate undesirable overshoots and delays, is required. This paper presents a Linear Quadratic Integral (LQI) approach to formulate a speed reference profile that ensures optimal engine behavior.

Understanding planetary gear efficiency is more involved than understanding efficiency of external gears because of the recirculating power that is inherent in planetary gear operation. There have been several publications going back several decades on this topic. However, many of these publications are mathematical in their approach and tend to be overlooked by practicing engineers. This paper brings a new, more visual and more intuitive approach to the problem. It uses lever diagrams, which have been a standard tool in the transmission engineer’s arsenal for almost four decades, to visualize the power flow and develop analytical expressions for the efficiency of simple and compound planetary gears. It then extends the approach to more complex gear trains.

In Aluminum Alloy, AA, sheet metal forming, the through thickness cracking at the edge of cut out is one of the major fracture modes. In order to prevent the edge cracking in production forming process, practical edge stretch limit criteria are needed for virtual forming prediction and early stamping trial evaluations. This paper proposes new methods for determining the edge stretching limit of the sheet coupons, with and without pre-stretching, based on the Digital Image Correlation (DIC) technique. A numbers of sets of notch-shaped smaller coupons with three different pre-stretching conditions (near 5%, 10% and fractured) are cut from the prestretched large specimens. Then the notch-shaped smaller coupons are stretched by uniaxial tension up to through edge cracking observed. A dual-camera 3D-DIC system is utilized to measure both coupon face strain and thickness strain in the notch area at the same time.

For a light duty truck, the frame is a structural system and it must go through a series of proving ground events to meet fatigue performance requirement. Nowadays, in order to meet stringent CAFE standards, auto manufacturers are seeking to keep the vehicle weight as light as possible. The weight reduction on the frame is a challenging task as it still needs to maintain the strength, safety, and durability fatigue performance. CAE fatigue simulation is widely used in frame design before the physical proving ground tests are performed. A typical frame durability fatigue analysis includes both the base metal fatigue analysis and seam weld fatigue analysis. Usually the gauges of the frame components are dictated by the seam weld fatigue performance so opportunities for weight reduction may exist in areas away from the welds. One method to reduce frame weight is to cut lightening holes in the areas that have little impact on the frame fatigue performance.

The front-end air flow conditions have a substantial impact on the cool down performance of a vehicle Heating, Ventilation and Air-Conditioning (HVAC) system. The performance of a mobile HVAC system is analyzed by conducting tests on the vehicle in a drive cell, subjecting it to different drive cycles. This now can be done virtually using system level simulation or one-dimensional (1D) tools. Target values for condenser air inlet velocity and temperature for these HVAC performance focused drive cycles needs to be established during the development phase to meet the cool down functional objectives of the vehicle. Thus, in the early stages of development, 1D tools play a major role. Condenser air flow should be sufficient and the temperature should be as low as possible at different vehicle operating conditions to have good air-conditioning (AC) performance.

This paper presents a novel Kalman filter based road grade estimation method using measurements from an accelerometer, a gyroscope and a velocity sensor. The accelerometer measures the longitudinal proper acceleration of the vehicle, and the accelerometer measurement is almost drift free but it is heavily corrupted by the accelerometer noise. The gyroscope measures the pitch rate of the vehicle, and the gyroscope measurement is quite clean but it is substantially disturbed by the gyroscope bias. The velocity sensor measures the longitudinal velocity of the vehicle, and the velocity sensor measurement is also considerably corrupted by the measurement noise. The developed Kalman filter based estimation method uses the models of the sensors and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The simulation results show that the developed method is very effective in producing an accurate road grade estimate.

The demand for vehicles with electrified powertrain systems is increasing due to government regulations on fuel economy. The battery systems in a PHEV (Plug-in Hybrid-electric Vehicle) have achieved tremendous efficiency over past few years. The system has become more delicate and complex in architecture which requires sophisticated thermal management. Primary reason behind this is to ensure effective cooling of the cells. Hence the current work has emphasized on developing a “Physics based” thermal management modeling framework for a typical battery system. In this work the thermal energy conservation has been analyzed thoroughly in order to develop necessary governing equations for the system. Since cooling is merely a complex process in HEV battery systems, the underlying mechanics has been investigated using the current model. The framework was kept generic so that it can be applied with various architectures. In this paper the process has been standardized in this context.

A cargo box is a key structure in a pickup truck which is used to hold various items. Therefore, a cargo box must be durable and robust under different ballast conditions when subjected to road load inputs. This paper discusses a Design for Six Sigma (DFSS) approach to improve the durability of cargo box panel in its early development phase. Traditional methods and best practices resulted in multiple iterations without an obvious solution. Hence, DFSS tools were proposed to find a robust and optimum solution. Key control factors/design parameters were identified, and L18 Orthogonal Array was chosen to optimize design using CAE tools. The optimum design selected was the one with the minimum stress level and the least stress variation. This design was confirmed to have significant improvement and robustness compared to the initial design. DFSS identified load paths which helped teams finally come up with integrated shear plate to resolve the durability concern.

In recent years, there is increasing demand for every CAE engineer on their confidence level of the virtual simulation results due to the upfront robust design requirement during early stage of an automotive product development. Apart from vehicle feel factor NVH characteristics, there are certain vibration target requirements at system or component level which need to be addressed during design stage itself in order to achieve the desired functioning during vehicle operating conditions. Vehicle passive safety system is one which primarily consists of acceleration sensors, control module and air-bag deployment system. Control module’s decision is based on accelerometer sensor signals so that its mounting locations should meet the sufficient inertance or dynamic stiffness performance in order to avoid distortion in signals due to its structural resonances.

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.

Multiaxial loading on mechanical products is very common in the automotive industry, and how to design and analyze these products for durability becomes an important, urgent task for the engineering community. Due to the complex nature of the fatigue damage mechanism for a product under multiaxial state of stresses/strains which are dependent upon the modes of loading, materials, and life, modeling this behavior has always been a challenging task for fatigue scientists and engineers around the world. As a result, many multiaxial fatigue theories have been developed. Among all the theories, an existing equivalent stress theory is considered for use for the automotive components that are typically designed to prevent Case B cracks in the high cycle fatigue regime.

Simplified vehicle dynamics models used to study the driveline durability are typically limited to the longitudinal dynamics and do not account for vertical and pitch dynamics. The influence of suspension on the vehicle ride and handling characteristics is studied extensively in the literature but its impact on the driveline torques is often not considered. In this paper, an effort is made to investigate the influence of suspension compliance on the driveline torque using a planar (longitudinal, pitch and vertical) vehicle dynamics model. An AWD vehicle is studied to understand its impact on the torque levels of both axles (primary and secondary). Subsequently the planar dynamics is explored in the context of anti-squat/anti-dive suspension. The primary focus of the paper is to predict the driveline torque.

This paper describes a CAE fatigue life prediction technique for a tailgate on pickup truck cargo box with inertial forces and moments applied at mass center of the tailgate as input loads. The inertial forces and moments are calculated from the accelerations measured at the corners of the tailgate as the truck is being driven over a durability schedule at the test proving grounds. All the dynamic responses of the tailgate on cargo box, including any dynamic interactions at the pivot joints between the tailgate and box sides, are captured in the acquired data and also in the inertial forces and moments computed at the mass center. Correspondingly, all the dynamic responses are included in the CAE fatigue life predictions. The dynamic interactions at the pivot joints are simulated by using two identical CAE models, one with lateral translational constraint applied at the left pivot only and the other at the right pivot only.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.

As the automotive industry is quickly changing towards electric vehicles, we can highlight the importance of aerodynamics and its critical role in reaching extended battery ranges for electric cars. With all new smooth underbodies, a lot of attention has turned into the effects of rim designs and tires brands and the management of these tire wakes with the vehicle. Tires are one of the most challenging areas for aerodynamic drag prediction due to its unsteady behavior and rubber deformation. With the simulation technologies evolving fast regarding modeling spinning tires for aerodynamics, this paper takes the prior work and data completed by the authors and investigates the impact on the flow fields and aerodynamic forces using the most recent developments of an Immerse Boundary Method (IBM). IBM allows us to mimic realistically a rotating and deformed tire using Lattice Boltzmann methods.