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An overview of Daimler?s progression to advance powertrain technology in a growth industry shows many different solutions to improvement in transportation. Daimler continues to make breakthroughs in technology development and application building on 125 years of automotive development. Optimization of current powertrains will enable a significant gain in CO2/mi reductions, that dependent on product mix can be augmented with additional technologies. There is however no bypass to some form of electrification, enabling efficiency gains and alternative forms of power supply. Development of hybrid powertrains continues in an established manner and enhanced development of further electrified powertrains are in development. Organizationally and technically, significant skills and adjustments need to continue to be undertaken enabling OEMs and in particular the supply base to develop optimized solutions efficiently. The outlook is bright for novel component development and innovation.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

The objective of this paper focused on the modeling of an adaptive energy absorbing steering column which is the first phase of a study to develop a modeling methodology for an advanced steering wheel and column assembly. Early steering column designs often consisted of a simple long steel rod connecting the steering wheel to the steering gear box. In frontal collisions, a single-piece design steering column would often be displaced toward the driver as a result of front-end crush. Over time, engineers recognized the need to reduce the chance that a steering column would be displaced toward the driver in a frontal crash. As a result, collapsible, detachable, and other energy absorbing steering columns emerged as safer steering column designs. The safety-enhanced construction of the steering columns, whether collapsible, detachable, or other types, absorb rather than transfer frontal impact energy.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

In this paper a rule-based energy management for parallel hybrid electric vehicles (HEVs) is presented, which is based on the principles describing the optimal control behavior. Therefore we first show the general relations that can be used to describe the optimal limit of electric driving as well as the optimal torque split among the two propulsion systems. Subsequently these relations are employed to derive maps, which represent the optimal behavior depending on several input parameters. These maps are then used as inputs for the rules in the proposed energy management. This not only makes it possible to automatically calibrate the rule-based controller but also gives the optimal control in every driving situation. Given it is not fuel-efficient to turn the internal combustion engine (ICE) on or off for short intervals, it is further shown how this approach allows to adjust the established limit for electric driving by additional rules.

The ISO TC22/SWG2 - Brake Lining Committee established a task force to determine and analyze root causes for variability during dynamometer brake performance testing. SAE paper 2010-01-1697 “Brake Dynamometer Test Variability - Analysis of Root Causes” [1] presents the findings from the phases 1 and 2 of the “Test Variability Project.” The task force was created to address the issue of test variability and to establish possible ways to improve test-to-test and lab-to-lab correlation. This paper presents the findings from phase 3 of this effort-description of factors influencing test variability based on DOE study. This phase concentrated on both qualitative and quantitative description of the factors influencing friction coefficient measurements during dynamometer testing.

Design optimization techniques are widely used to drive designs toward a global or a near global optimal solution. However, the achieved optimal solution often appears to be the only choice that an engineer/designer can select as the final design. This is caused by either problem topology or by the nature of optimization algorithms to converge quickly in local/global optimal or both. Problem topology can be unimodal or multimodal with many local and/or global optimal solutions. For multimodal problems, most global algorithms tend to exploit the global optimal solution quickly but at the same time leaving the engineer with only one choice of design. The paper explores the application of genetic algorithms (GA), simulated annealing (SA), and mixed integer problem sequential quadratic programming (MIPSQP) to find multiple local and global solutions using single objective optimization formulation.

Hybrid electric vehicle (HEV) systems offer significant improvements in vehicle fuel economy and reductions in vehicle generated greenhouse gas emissions. The widely accepted power-split HEV system configuration couples together an internal combustion engine with two electric machines (a motor and a generator) through a planetary gear set. This paper describes a methodology for analysis and optimization of alternative HEV power-split configurations defined by alternative connections between power sources and transaxle. The alternative configurations are identified by a matrix of kinematic equations for connected power sources. Based on the universal kinematic matrix, a generic method for automatically formulating dynamic models is developed. Screening and optimization of alternative configurations involves verification of a set of design requirements which reflect: vehicle continuous operation, e.g. grade test; and vehicle dynamic operation such as acceleration and drivability.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.

The Reverse Braking Assist (RBA) feature is designed to automatically activate full braking in a backing vehicle. When this feature activates, a backing vehicle is suddenly stopped or may slide to a stop. During this process, an understanding of the driver's behavior may be useful in the design of an appropriate human-machine-interface (HMI) for the RBA. Several experimental studies were done to examine driver behavior in response to an unexpected and automatic braking event while backing [1]. Two of these studies are reported in this paper. A 7-passenger Crossover Utility Vehicle was fitted with a rear-view camera, a center-stack mounted LCD screen, and ancillary recording devices. In the first study, an object was suddenly placed in the path of a backing vehicle. The backing vehicle came to a sudden and complete stop. The visual image of the backing path on the LCD prominently showed that an obstacle was present in the backing path of the vehicle.

The integration of a HIL system involves many software and hardware elements. During the integration process, various errors may occur. In this paper, the Design For Six Sigma (DFSS) methodology is applied to develop an integration process for a generic type of HIL system. The development follows the DCOV (Design, Characterize, Optimize and Verify) four-phase approach. The result is an efficient process proven to meet the customer's needs, reduce integration errors and deliver a high quality HIL system within less time.

A model of Ford's current FTP based OBD-II catalyst monitor has been developed and used in determining the optimal monitored catalyst volume for several LEV applications. The model predictions were found to agree reasonably well with the available experimental data. Furthermore, the results of this study indicate that the optimal monitored catalyst volume for meeting LEV requirements is vehicle application specific. As a result, it is concluded that a general guideline for sizing of the monitored catalyst volume for LEVs will most likely be inadequate and could result in grossly suboptimal catalyst monitor function for some applications. The model which is described in this paper offers a potentially more effective means of determining the best monitored catalyst volume for a given vehicle application. It should be possible to utilize this model during the early phase of a vehicle program in order to provide for the optimal packaging of the catalyst monitor sensor (CMS).

Rapid Prototyping, Fabrication and Tooling is a process that blends a series of technologies (machines, tools, and methods) capable of generating physical objects directly from a CAD database. The process dramatically reduces the time spent during product development by allowing for fast visualization, verification, iteration, optimization, and fabrication of parts and tools. Many new techniques of tooling have been and are being developed by using rapid fabricated parts. These are having a dramatic impact on both timing and costs throughout the automotive industry. One area that these methods can be utilized to their full potential is motorsports. Of particular interest is the growing use of bridge tooling to provide first article through production intent parts that promote cost effective changes.

Multidisciplinary design optimization (MDO) methods are commonly used for weight reduction in automotive industry. The design variables for MDO are often selected based on critical parts, which usually are close to optimal after many design iterations. As a result, the real weight reduction benefit may not be fully realized due to poor selection of design parameters. In addition, most applications require running design of experiments (DOE) to explore the full design space and to build response surfaces for optimization. This approach is often too costly if too many design variables are simultaneously considered. In this research, an alternative approach to address these issues is presented. It includes two optimization phases. The first phase uses critical parts for design iterations and the second phase use non-critical for weight reduction. A vehicle body structure is used to demonstrate the proposed strategy to show its effectiveness.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

Beginning in 2010, Daimler's well-known Diesel Sprinter van has to fulfill the new and clearly tighter NOx emission standards of NAFTA10 (EPA, CARB). This requires an integrated approach of further engine optimizations and the implementation of an innovative exhaust aftertreatment technology. The goal was to develop an overall concept which meets simultaneously the tightened emission standards (including OBD limits) and the increasing customer demands of more power and torque without losing the high fuel efficiency of the small and highly efficient 3-liter V6 diesel engine OM642, which already has been installed in the NAFTA07 Sprinter. In the early stages of the concept phase, the most appropriate NOx aftertreatment technology and certification form (engine or vehicle) had to be selected for this specific vehicle class in the van segment with enhanced requirements to durability, economical efficiency and specific driving behavior.