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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.

One approach of reducing weight of vehicles is using composite materials, and short glass fiber reinforced polypropylene is one of most popular composite materials. To more accurately predict durability performance of structures made of this kind of composite material, static and fatigue performance of coupons and components made of a short glass fiber reinforced polypropylene has been physically studied. CAE simulations have been conducted accordingly. This paper described details of CAE model setup, procedures, analysis results and correlations to test results for static, fiber orientation flow and fatigue of coupons and a battery tray component. The material configurations include fiber orientations (0, 20 and 90 degrees), and mean stress effect (R = -1.0, -0.5, -0.2, 0.1 and 0.4). The battery tray component samples experience block cycle loading with loading ratio of R = -0.3 and 0.3. The CAE predictions have reasonable correlations to the test results.

The performance and durability of the lead acid battery is highly dependent on the internal battery temperature. The changes in internal battery temperatures are caused by several factors including internal heat generation and external heat transfer from the vehicle under-hood environment. Internal heat generation depends on the battery charging strategy and electric loading. External heat transfer effects are caused by customer duty cycle, vehicle under-hood components and under-hood ambient air. During soak conditions, the ambient temperature can have significant effect on battery temperature after a long drive for example. Therefore, the temperature rise in a lead-acid battery must be controlled to improve its performance and durability. In this paper a thermal model for lead-acid battery is developed which integrates both internal and external factors along with customer duty cycle to predict battery temperature at various driving conditions.

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.

As CAFE requirements increase, automotive OEMs are pursuing innovative methods to lightweight their Body In Whites (BIWs). Within FCA US, this lightweighting research and development activity often occurs through Decoupled Innovation projects. A Decoupled Innovation team comprised of engineers from the BIW Structures Group, in collaboration with Tier 1 supplier Magna Exteriors, sought to re-design a loadbearing component on the BIW that would offer significant weight savings when the current steel component was replaced with a carbon fiber composite. This paper describes the design, development, physical validation and partnership that resulted in a composite Rear Package Shelf Assembly solution for a high-volume production vehicle. As the CAFE requirements loom closer and closer, these innovation-driven engineering activities are imperative to the successful lightweighting of FCA US vehicles.

One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.

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.

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.

The test cycle average drag coefficient is examined for the variation of allowable EPA coastdown ambient conditions. Coastdown tests are ideally performed with zero wind and at SAE standard conditions. However, often there is some variability in actual ambient weather conditions during testing, and the range of acceptable conditions is further examined in detail as it pertains to the effect on aerodynamic drag derived from the coastdown data. In order to “box” the conditions acceptable during a coastdown test, a sensitivity analysis was performed for wind averaged drag (CD¯) as well as test cycle averaged drag coefficients (CDWC) for the fuel economy test cycles. Test cycle average drag for average wind speeds up to 16 km/h and temperatures ranging from 5C to 35C, along with variation of barometric pressure and relative humidity are calculated. The significant effect of ambient cross winds on coastdown determined drag coefficient is demonstrated.

This paper will explore the effect that non-constant function CD (as observed during wind tunnel testing) would have on the coastdown derived drag coefficient and other regulatory drive cycles. It is common in wind tunnel testing to observe road vehicle drag coefficients that vary with speed. These varying CD values as a function of velocity will be expressed as CD(V) in this paper. Wind tunnel testing for product development is generally conducted at 110 km/h (68.3 mph) which are similar speeds and typical of the United States (US), European, and Asian highway speeds. Reported values of CD are generally gathered at these speeds. However, coastdown testing by definition takes place over a large range of speeds mostly lower than the wind tunnel test speeds. This paper will explore the effect that six typical functions of CD(V) have on the coastdown derived CD. One of the six functions is a constant, to represent a wind tunnel reported CD.

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.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

Threaded fasteners or bolted joints are used extensively in automotive assemblies. There are standard procedures to evaluate joint performance under block cycles or road loads. The deciding load case for such joint design is slippage analysis of the joint. There are studies done to evaluate the theoretical and experimental behavior of these joints. There are different ways of understanding the interaction between the bolt and the nut under different loading scenarios. However, none have provided a satisfactory method of quantifying bolt loosening or loss of clamp load under cyclic loading, where no slippage is observed. Under varying loads, initial relaxation of the joint is followed by a loss of clamping load. Below a critical value, complete loss of clamping load progresses very rapidly and this results in a loose joint.

It is a challenging task to find an optimal design concept for a truck frame structure given the complexity of loading conditions, vehicle configurations, packaging and other requirements. In addition, there is a great emphasis on light weight frame design to meet stringent emission standards. This paper provides a framework for fast and efficient development of a frame structure through various design phases, keeping durability in perspective while utilizing various weight reduction techniques. In this approach frame weight and stiffness are optimized to meet strength and durability performance requirements. Fast evaluation of different frame configurations during the concept phase (I) was made possible by using DFSS (Design for Six Sigma) based system synthesis techniques. This resulted in a very efficient frame ladder concept selection process.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

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.

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.

Driveline launch shudder is a second-order vibration phenomenon excited by the driveline system in vehicles. It is experienced as low-frequency tactile vibrations at the vehicle seat track and is further deteriorated by a high torque demand from the engine. These vibrations are unwanted and affect the vehicle ride quality. A virtual method has been developed in ADAMS/Car to simulate the driveline launch shudder event for solid axle suspension architecture vehicles. Detailed modeling of the full vehicle system with appropriate boundary conditions has been presented. The simulated driveline launch shudder event has been quantified in the form of axle windup and accelerations at the driveline pinion, center bearing and seat track locations. A physical test correlation case study has been performed to validate the developed virtual method. This virtual method is also successfully applied to provide a driveline launch shudder mitigation enabler to improve vehicle ride 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.