This paper examines the impact of omitted variable bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into optimal design solutions and characterize properties of optimization problems that affect the magnitude of the resulting error in solutions. We then demonstrate the impact of OVB on optimal designs using a case study of automotive powertrain design optimization. In the case study, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%), which propagates up to 21% error in the optimal engine size.

As a kind of imprecise probabilistic model, probability-box (P-box) model can deal with both aleatory and epistemic uncertainties in parameters effectively. The P-box can generally be categorized into two classes, namely, parameterized P-box and non-parameterized P-box. Currently, the researches involving P-boxes mainly aim at the parameterized P-box, while the works handling the non-parameterized P-box are relatively inadequate. This paper proposes an efficient uncertainty propagation analysis method based on cumulative distribution function discretization (CDFD) for problems with non-parameterized P-boxes, through which the bounds of statistical moments and the cumulative distribution function (CDF) of a response function with non-parameterized P-box variables can be obtained. First, a series of linear programming models are established for acquiring the lower and upper bounds of the first four origin moments of the response function. Second, based on the bounds of the origin moments, the CDF bounds for the response function can be obtained using Johnson distributions fitting and an optimization approach based on percentiles. Finally, the accuracy and efficiency of the proposed method are verified by investigating two numerical examples.

Compared with conventional vehicles, autonomous vehicles (AVs) are featured by increased energy efficiency and road safety, yet hardly meet with much success without enough human trust. Designing appropriate interactions between AV and human, such as communication with pedestrians, could help enhance trust and public acceptance. In this work, we examine design characteristics of AV interface, including communication style, explicit display of vehicle speed, and adaptive strategy, and study their effects on pedestrians’ trust behaviors. It is found that any communication style could improve pedestrians’ trust in AV and decision alignment with AV expectations. Among the three communication styles, commanding and advisory are significantly better than informative, in terms of trust improvement (commanding versus informative: t = 3.61 and p < 0.001; advisory versus informative: t = 2.78, p = 0.005) and decision alignment ((a) in expected cross scenarios, commanding versus informative: t = 0.35 and p < 0.001; advisory versus informative: t = 11.71, p < 0.001; (b) in expected not cross scenarios, commanding versus informative: t = −7.61, p < 0.001; advisory versus informative: t = −6.40, p < 0.001). Adding speed information on top of explicit message communication does not change the relative effectiveness of individual styles, even though the display of speed-only information has significantly improved both measures (trust: F = 9.39 and p = 0.002; decision: F = 6.04 and p = 0.015). In addition, applying an adaptive communication strategy when yielding would significantly improve pedestrians’ trust (t = 9.33 and p < 0.001) and decision alignment (t = 14.78 and p < 0.001). This study demonstrates the influence of design characteristics on the formation of trust relationships between pedestrians and autonomous vehicles and paves the ways for developing more advanced AV communication mechanisms.

In this study, an end-of-life (EOL) model is included in the life cycle energy optimization (LCEO) methodology to account for the energy burdens and credits stemming from a vehicle’s EOL processing phase and balance them against the vehicle’s functional requirements and production and use-phase energies. The substitution with a correction factor allocation method is used to model the contribution of recycling to the EOL phase’s energy. The methodology is illustrated through the optimization of the design of a simplified vehicle subsystem. For the latter, multiple recycling scenarios with varying levels of assumed recycling induced material property degradation were built, and their impact on the vehicle subsystem’s optimal solutions was compared to that of scenarios based on landfilling and incineration with energy recovery. The results show that the vehicle subsystem’s optimal designs are significantly dependent on the EOL scenario considered. In particular, the optimal designs associated with the recycling scenarios are on average substantially heavier, and less life cycle energy demanding, than their landfilling or incineration with energy recovery-related counterparts, thus demonstrating how the inclusion of EOL modeling in the LCEO methodology can significantly alter material use patterns, thereby effecting the very mechanisms enabling the embodiment of the resulting life cycle energy optimal designs.

The assembly precision of wheel alignment parameters is vital to vehicle handling stability. Due to the vertical wheel displacement and compliant components in suspension systems, it is difficult to assemble qualified vehicles with proper wheel alignment parameters. In the assembly shop of the automobile factory, the adjustment of wheel alignment parameters is the most time-consuming process because it relies on trial and error. In order to provide a theoretical guidance to the precision control of wheel alignment parameters, this paper extends the theory of equilibrium equations of incremental forces (EEIF) to 3D compliant mechanisms. Constraint equations of kinematic joints are adopted to express the spatial relationships of different parts. A couple of fixed and floating joint coordinate systems (CSs) are used together to represent deviations of compliant components. The impacts of suspension part deviations on vertical wheel displacement and assembly deformations are well illustrated by such approach. Accuracy of the proposed method is verified by comparing with ADAMS simulation. The results show that the error rates of the 3D EEIF method are less than 5%. Furthermore, statistical assembly variation analysis of a Macpherson suspension is accomplished by using the proposed method and an optimized process strategy is put forward.

This paper develops a simulation-based mission mobility reliability (MMR) analysis framework to account for uncertainty in mobility prediction of off-road ground vehicles in mission planning. A concept of MMR is first proposed to quantify reliability of a mission path which passes through different types of soils. A single-loop Kriging surrogate modeling method is then employed to overcome the computational challenge in MMR assessment caused by expensive mobility simulations. Built upon the surrogate model-based MMR analysis, a dynamic updating scheme is proposed to update the MMR estimation using online mobility data, during the course of a specific mission and for a particular vehicle. The online dynamic updating of MMR allows us for effective and dynamic decision-making during the mission phase, thus proactively avoid rare events of immobility during the mission. A case study demonstrates the efficacy of the proposed MMR analysis and updating framework.

This paper describes the design, functional testing, and user feedback for a tractor specialized for small farms in low-resource settings, particularly India. The presented tractor is unique in its ability to compete with draft animals’ physical dimensions, pulling performance, and sale price, while retaining key tractor advantages such as compatibility with modern tools, low maintenance costs, and reduced drudgery. This tractor features motorcycle-like controls and seating, inline drive wheels, stabilization via an outrigger arm or a specially developed, novel balance board attachment, and the ability to attach implements ahead or behind the rear axle. The design was created to address unmet farmer requirements identified during interviews with Indian farming stakeholders. A prototype of the tractor demonstrated the completion of key farming operations in a Massachusetts farm where expert user feedback was obtained. In-person interviews on the tractor’s usefulness were then conducted with 24 small and marginal Indian farmers in Karnataka, Gujarat, and Tamil Nadu. The tractor was described to the farmers with help of pictures, videos, and local experts. Farmers generally reported that the prototype tractor would meet their needs and suggested being willing to purchase the vehicle for 123,000 INR, about 22% higher than the price target for which the tractor was designed. The interviewed farmers reported an average likelihood of 4.8/5 that they would use the vehicle for planting, inter-cultivation, and spraying, and an average likelihood of 3.8/5 that they would use the tractor for primary or secondary tillage.

Flexible design, often embodied as Real Options, has proven valuable in sustaining system performance under operational uncertainty. By building “options,” decision-makers buy the right, but not the obligation, to upgrade at a later time. Prior studies implicitly assume decision-makers can “call” the option in a timely manner, however, in most sociotechnical systems, this is far from certain. Budget cycles, supply chain logistics and separation of authority and budget lead to implementation delays. To test, we developed a Monte Carlo simulation of a representative Army vehicle project. We first replicate prior studies without implementation delays, then incorporate delays for comparison. Once added, the value of flexibility degrades rapidly. The rate of degradation varies based on the flexibility strategy. Our results suggest a need to consider implementation uncertainty in evaluating flexible design options and open a new path for matching flexible design strategies to anticipated sources of implementation uncertainty.

Substantial energy savings during the use phase of internal combustion and electric automobiles can be achieved by increasing eco-driving behavior, particularly reduced acceleration and braking. However, motivating widespread adoption of this behavior is challenging due to incompatibility with drivers’ values and priorities, and disassociation between drivers’ actions and observable consequences. Informational approaches, e.g., training programs and educational campaigns, are either difficult to scale up or largely ineffective, with drivers reluctant to make long-term changes. Alternatively, behavior can be influenced by redesigning the context within which the behavior occurs. Such an intervention must be effective across demographics and underlying behaviors to achieve ubiquity. The current study investigates the perceived effect on the driving style of a simple graphical dashboard display depicting an animated coffee cup. This display incorporates associative mental models and contextual relevance to increase the salience of inefficient vehicle movements and nudge drivers to adopt smoother driving. An online Amazon Mechanical Turk survey (92 participants) revealed a significant preference for the coffee-cup over a dial-gauge display when controlling for demographic variables. This result offers a preliminary indication that a behavioral nudge may be effective in influencing drivers to adopt eco-driving practices.

Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network-based approach for modeling and analyzing the evolution of product competitions using multi-year buyer survey data. The product co-consideration network, formed based on the likelihood of two products being co-considered from survey data, is treated as a proxy of products’ competition relations in a market. The separate temporal exponential random graph model (STERGM) is employed as the dynamic network modeling technique to model the evolution of network as two separate processes: link formation and link dissolution. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and the benefits of dynamic network models compared to the static network modeling approach based on an exponential random graph model (ERGM). The results show that since STERGM takes preexisting competition relations into account, it provides a pathway to gain insights into why a product may maintain or lose its competitiveness over time. These driving factors include both product attributes (e.g., fuel consumption) as well as current market structures (e.g., the centralization effect). With the proposed dynamic network-based approach, the insights gained from this paper can help designers better interpret the temporal changes of product competition relations to support product design decisions.

In vehicle collision accidents, an occupant restraint system (ORS) is crucial to protect the human body from injury, and it commonly involves a large number of design parameters. However, it is very difficult to quantify the importance of design parameters and determine them in the ORS design process. Therefore, an approach of the combination of the proposed approximate sensitivity analysis (SA) method and the interval multi-objective optimization design is presented to reduce craniocerebral injury and improve ORS protection performance. First, to simulate the vehicle collision process and obtain the craniocerebral injury responses, the integrated finite element model of vehicle-occupant (IFEM-VO) is established by integrating the vehicle, dummy, seatbelt, airbag, etc. Then, the proposed approximate SA method is used to quantify the importance ranking of design parameters and ignore the effects of some nonessential parameters. In the SA process, the Kriging metamodel characterizing the relationships between design parameters and injury responses is fitted to overcome the time-consuming disadvantage of IFEM-VO. Finally, according to the results of SA, considering the influence of uncertainty, an interval multi-objective optimization design is implemented by treating the brain injury criteria (BRIC, BrIC) as the objectives and regarding the head injury criterion (HIC) and the rotational injury criterion (RIC) as the constraints. Comparison of the results before and after optimization indicates that the maximum values of the translational and rotational accelerations are greatly reduced, and the ORS protection performance is significantly improved. This study provides an effective way to improve the protection performance of vehicle ORS under uncertainty.

A method for the optimal design of complex systems is developed by effectively combining multi-objective optimization and analytical target cascading techniques. The complex systems with high dimensionality are partitioned into manageable subsystems that can be optimized using dedicated algorithms. The multiple objective functions in each subsystem are treated simultaneously, and the interactions between subsystems are managed using linking variables and shared variables. The analytical target cascading algorithm ensures the convergence of the optimal solution that meets the system level targets while complying with the subsystem level constraints. A design optimization of electric vehicles with in-wheel motors is formulated as a two-level hierarchical scheme where the top level has a model representing the electric vehicle and the bottom level contains models of battery and suspension. The vehicle model includes an electric motor model and a power electronics model. Pareto-optimal solutions are derived holistically. The effectiveness of the proposed method for optimizing the complex systems is compared against the conventional all-in-one optimization approach.

This paper explains the origin and merits of the dominant farm tractor design, which has endured since the 1940s. Understanding the origins and rationale for this dominant design enables engineers to appreciate its merits, and understand its drawbacks, which could be addressed in future designs. Additionally, the methods used in this paper to study tractor evolution are applicable to the study of other products with a longstanding dominant design. Two themes are covered in this paper: first, the historical context that directed the farm tractor's design evolution is presented; and second, a terramechanics-based tractor model is used to analyze why the dominant design is conducive to good performance. The prominent characteristics of the dominant tractor design are its weight distribution, wheel layout, tool location, and construction. Its weight distribution maximizes drawbar pull by placing 70 to 80% of the total vehicle weight on the rear wheels. Shifting the weight forward reduces pulling force while shifting it backward produces a negligible increase in pulling capacity while dangerously increasing the risk of upending the tractor. The tractor has four wheels arranged in a rectangular pattern—the rear wheels are driven while the front ones are usually idle. Rear wheels are of large diameter to increase ground clearance and tractive efficiency. Front wheels are of small diameter to allow for a large steering angle despite a narrow track width. A narrow track width reduces the space required for making a U-turn at field ends and improves access to farm spaces. Inline front and rear wheels are desirable for ease of driving between rows and to best harness soil compaction. Attaching implements behind the rear axle leverages tillage forces to increase maximum drawbar pull and enables using large tools. The tractor's crankcase and transmission housing are structural components—this reduces mass and manufacturing complexity.

Consumer behavior can be modeled using a decision-making process termed “consideration” in which consumers form requirements, “consideration rules,” in order to narrow their options for further evaluation. One type of consideration rule is the conjunctive rule, where a consumer makes a list of requirements and a product must meet all of the requirements in order to be considered for purchase, such as “the vehicle must get 25 miles per gallon or more”; “it must be priced at $22,000 or less”; and “it must be a standard-sized sedan.” This paper offers a design framework for linking these consideration rules with design. We demonstrate the use of our framework with a case study, namely the Volkswagen (VW) “clean diesel” scandal, which investigates the design strategies used in response to the scandal by capturing considerations within the marketing product planning subproblem and assuring engineering feasibility within the engineering design subproblem.

Manufacturers must decide when to invest and launch a new vehicle segment or how to redesign vehicles existing segment under market uncertainties. We present an optimization framework for redesigning or investing in future vehicles using real options to address uncertainty in gas price and regulatory standards like the U.S. Corporate Average Fuel Economy (CAFE) standard. In a specific study involving a product of gasoline, hybrid electric, and electric vehicles (EV), we examine the relationship between gas price and CAFE uncertainties to support decisions by manufacturers on product mix and by policy makers on proposing standards. A real options model is used for the time delay on investment, redesign, and pricing, integrated with a robust design formulation to optimize expected net present value (ENPV) and net present value (NPV) robustness. Results for nine different scenarios suggest that policy makers should consider gas price when setting CAFE standards; and manufacturers should consider the trade-off between ENPV and robust NPVs. Results also suggest that change of product mix rather than vehicle redesign better addresses CAFE standards inflation.

Styling or product appearance is well known for holding great influence on its differentiation, branding, and overall success in the market. However, the styling process is difficult due to the intuitive and subjective way in which designers evaluate designs. In particular, negotiating iterations between designers and engineers is challenging since engineers have objective, data-driven approaches to rationalize decisions whereas designers rely on instinct and intuition. While the literature shows sustained interest in this issue and provides methods to analyze appearance objectively, many approaches rely on abstracted or simplified versions of a product's appearance as the basis for analyses, ignoring the holistic nature of product appearance. This article contributes by proposing an improvement employing digital shape comparison tools applied to three-dimensional (3D) geometry of products, and generating data on differentiation in product shape—that is, the holistic styling analysis (HSA). The HSA provides an objective assessment of difference in appearance to form the basis for designers to rationalize styling to other stakeholders during the design process. The HSA is tested through an automotive industry case study. Results show the method adds objectivity to decision-making by providing objective reference measures for differentiation in the styling of previous and competing products. Such measures can be used to inform styling goals and to identify intended degrees of difference in key features while highlighting areas to maintain consistency. As such, we contribute by providing a means for styling designers to use data to drive their activities in the same manner as other stakeholders.

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire–road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

Hydrodynamic drives, hydraulic drives, and friction drives have the common characteristics of continuously variable ratio but lower efficiency in comparison with gear meshing drive. A novel method for the design of power-cycling variable transmission (PCVT) has been proposed, which has the characteristics of continuously variable ratio and high efficiency in the whole working range. First, the power-cycling phenomena of powertrain system have been analyzed, and the basic configuration of PCVT system has been put forward. The feasible judgment criteria and the basic design principle of PCVT system are provided. The calculation formulas of speed ratio characteristic and efficiency characteristic of PCVT system are also derived. At last, the design patterns of PCVT system have been provided for illustrating the unique performance of PCVT system.

Car sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for the relationships among fleet operations, charging station (CS) operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four subsystem problems: fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. We also compare an AEV service and autonomous vehicle (AV) service with gasoline engines. A case study for an autonomous fleet operating in Ann Arbor, MI, is used to examine AEV and AV sharing systems profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers.

The design of complex systems often requires reliability assessments involving a large number of uncertainties and low probability of failure estimations (in the order of 10 −4 ). Estimating such rare event probabilities with crude Monte Carlo (CMC) is computationally intractable. Specific numerical methods to reduce the computational cost and the variance estimate have been developed such as importance sampling or subset simulation. However, these methods assume that the uncertainties are defined within the probability formalism. Regarding epistemic uncertainties, the interval formalism is particularly adapted when only their definition domain is known. In this paper, a method is derived to assess the reliability of a system with uncertainties described by both probability and interval frameworks. It allows one to determine the bounds of the failure probability and involves a sequential approach using subset simulation, kriging, and an optimization process. To reduce the simulation cost, a refinement strategy of the surrogate model is proposed taking into account the presence of both aleatory and epistemic uncertainties. The method is compared to existing approaches on an analytical example as well as on a launch vehicle fallout zone estimation problem.