Jeremy J. Michalek

Valuation of plug-in vehicle life-cycle air emissions and oil displacement benefits.

We assess the economic value of life-cycle air emissions and oil consumption from conventional vehicles, hybrid-electric vehicles (HEVs), plug-in hybrid-electric vehicles (PHEVs), and battery electric vehicles in the US. We find that plug-in vehicles may reduce or increase externality costs relative to grid-independent HEVs, depending largely on greenhouse gas and SO2 emissions produced during vehicle charging and battery manufacturing. However, even if future marginal damages from emissions of battery and electricity production drop dramatically, the damage reduction potential of plug-in vehicles remains small compared to ownership cost. As such, to offer a socially efficient approach to emissions and oil consumption reduction, lifetime cost of plug-in vehicles must be competitive with HEVs. Current subsidies intended to encourage sales of plug-in vehicles with large capacity battery packs exceed our externality estimates considerably, and taxes that optimally correct for externality damages would not close the gap in ownership cost. In contrast, HEVs and PHEVs with small battery packs reduce externality damages at low (or no) additional cost over their lifetime. Although large battery packs allow vehicles to travel longer distances using electricity instead of gasoline, large packs are more expensive, heavier, and more emissions intensive to produce, with lower utilization factors, greater charging infrastructure requirements, and life-cycle implications that are more sensitive to uncertain, time-sensitive, and location-specific factors. To reduce air emission and oil dependency impacts from passenger vehicles, strategies to promote adoption of HEVs and PHEVs with small battery packs offer more social benefits per dollar spent.

Balancing Marketing and Manufacturing Objectives in Product Line Design

The product development process involves communication and compromise among interacting and often competing objectives from marketing, design, and manufacturing perspectives. Methods for negotiating these perspectives play an important role in the process. For example, design for manufacturing (DFM) analyses aim to incorporate manufacturing requirements into product design decision making to reduce product complexity and cost, which generally increases profitability. However, when design characteristics have market consequences, it is important to quantify explicitly the tradeoffs between the reduced cost and reduced revenue resulting from designs that are less expensive to manufacture but also less desirable in the marketplace. in this article we leverage existing models for coordinating marketing and design perspectives by incorporating quantitative models of manufacturing investment and production allocation. The resulting methodology allows a quantitative assessment of tradeoffs among product functionality, market performance, and manufacturing costs to achieve product line solutions with optimal profitability.

Architectural layout design optimization

This article presents an optimization model of the quantifiable aspects of architectural floorplan layout design, and a companion article presents a method for integrating mathematical optimization and subjective decision making during conceptual design. The model presented here offers a new approach to floorplan layout optimization that takes advantage of the efficiency of gradient-based algorithms, where appropriate, and uses evolutionary algorithms to make discrete decisions and do global search. Automated optimization results are comparable to other methods in this research area, and the new formulation makes it possible to integrate the power of human decision-making into the process.

Optimal Plug-In Hybrid Electric Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption, and Greenhouse Gas Emissions

Plug-in hybrid electric vehicle (PHEV) technology has the potential to reduce operating cost, greenhouse gas (GHG) emissions, and petroleum consumption in the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an optimization model integrating vehicle physics simulation, battery degradation data, and U.S. driving data. The model identifies optimal vehicle designs and allocation of vehicles to drivers for minimum net life cycle cost, GHG emissions, and petroleum consumption under a range of scenarios. We compare conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent size and performance (similar to a Toyota Prius) under urban driving conditions. We find that while PHEVs with large battery packs minimize petroleum consumption, a mix of PHEVs with packs sized for 25– 50 miles of electric travel under the average U.S. grid mix (or 35– 60 miles under decarbonized grid scenarios) produces the greatest reduction in life cycle GHG emissions. Life cycle cost and GHG emissions are minimized using high battery swing and replacing batteries as needed, rather than designing underutilized capacity into the vehicle with corresponding production, weight, and cost implications. At 2008 average U.S. energy prices, Li-ion battery pack costs must fall below

A study of fuel efficiency and emission policy impact on optimal vehicle design decisions

590/kW h at a 5% discount rate or below

AN EFFICIENT WEIGHTING UPDATE METHOD TO ACHIEVE ACCEPTABLE CONSISTENCY DEVIATION IN ANALYTICAL TARGET CASCADING

410/kW h at a 10% rate for PHEVs to be cost competitive with HEVs. Carbon allowance prices offer little leverage for improving cost competitiveness of PHEVs. PHEV life cycle costs must fall to within a few percent of HEVs in order to offer a cost-effective approach to GHG reduction. DOI: 10.1115/1.4002194

Diagonal Quadratic Approximation for Parallelization of Analytical Target Cascading

Recent environmental legislation, such as the European Union Directive on End-of-Life Vehicles and the Japanese Home Electric Appliances Recycling law, has had a major influence on product design from both an engineering and an economic perspective. This article presents a methodology for studying the effects of automobile fuel efficiency and emission policies on the long-term design decisions of profit-seeking automobile producers competing in an oligopoly market. Mathematical models of engineering performance, consumer demand, and manufacturing costs are developed for a specific market segment, and game theory is utilized to simulate competition among firms to predict design choices of producers at market equilibrium. Several policy scenarios are evaluated for the small car market, including corporate average fuel economy (CAFE) standards, carbon dioxide (CO 2 ) emissions taxes, and diesel technology quotas. The results indicate that leveraging CO 2 taxes on producers for expected life cycle emissions yields diminishing returns on fuel efficiency improvement per regulatory dollar as the taxes increase, while CAFE standards achieve higher average fuel efficiency per regulatory dollar. Results also indicate that increasing penalties for violation of CAFE standards can result in lower cost to producers and consumers because of the effects of competition, and penalties based on fuel economy or emissions alone may not be sufficient incentive for producers to bring more costly alternative fuel vehicles into the market. The ability to compare regulations and achieve realistic trends suggests that including engineering design and performance considerations in policy analysis can yield useful predictive insight into the impact of government regulations on industry, consumers, and the environment.

Effects of Regional Temperature on Electric Vehicle Efficiency, Range, and Emissions in the United States

Weighting coefficients are used in Analytical Target Cascading (ATC) at each element of the hierarchy to express the relative importance of matching targets passed from the parent element and maintaining consistency of linking variables and consistency with designs achieved by subsystem child elements. Proper selection of weight values is crucial when the top level targets are unattainable, for example when “stretch” targets are used. In this case, strict design consistency cannot be achieved with finite weights; however, it is possible to achieve arbitrarily small inconsistencies. This article presents an iterative method for finding weighting coefficients that achieve solutions within user-specified inconsistency tolerances and demonstrates its effectiveness with several examples. The method also led to reduced computational time in the demonstration examples.

Regional Variability and Uncertainty of Electric Vehicle Life Cycle CO2 Emissions across the United States

Analytical Target Cascading (ATC) is an effective decomposition approach used for engineering design optimization problems that have hierarchical structures. With ATC, the overall system is split into subsystems, which are solved separately and coordinated via target/response consistency constraints. As parallel computing becomes more common, it is desirable to have separable subproblems in ATC so that each subproblem can be solved concurrently to increase computational throughput. In this paper, we first examine existing ATC methods, providing an alternative to existing nested coordination schemes by using the block coordinate descent method (BCD). Then we apply diagonal quadratic approximation (DQA) by linearizing the cross term of the augmented Lagrangian function to create separable subproblems. Local and global convergence proofs are described for this method. To further reduce overall computational cost, we introduce the truncated DQA (TDQA) method that limits the number of inner loop iterations of DQA. These two new methods are empirically compared to existing methods using test problems from the literature. Results show that computational cost of nested loop methods is reduced by using BCD and generally the computational cost of the truncated methods, TDQA and ALAD, are superior to other nested loop methods with lower overall computational cost than the best previously reported results.© 2007 ASME

INTERACTIVE DESIGN OPTIMIZATION OF ARCHITECTURAL LAYOUTS

We characterize the effect of regional temperature differences on battery electric vehicle (BEV) efficiency, range, and use-phase power plant CO2 emissions in the U.S. The efficiency of a BEV varies with ambient temperature due to battery efficiency and cabin climate control. We find that annual energy consumption of BEVs can increase by an average of 15% in the Upper Midwest or in the Southwest compared to the Pacific Coast due to temperature differences. Greenhouse gas (GHG) emissions from BEVs vary primarily with marginal regional grid mix, which has three times the GHG intensity in the Upper Midwest as on the Pacific Coast. However, even within a grid region, BEV emissions vary by up to 22% due to spatial and temporal ambient temperature variation and its implications for vehicle efficiency and charging duration and timing. Cold climate regions also encounter days with substantial reduction in EV range: the average range of a Nissan Leaf on the coldest day of the year drops from 70 miles on the Pacific Coast to less than 45 miles in the Upper Midwest. These regional differences are large enough to affect adoption patterns and energy and environmental implications of BEVs relative to alternatives.