CMU/Ford study assesses optimal mix of conventional, hybrid, plug-in hybrid and electric vehicles for minimizing GHG and cost
|Breakdown of (a) equivalent annualized life cycle cost and (b) life cycle GHG emissions for four independently cost-optimized vehicle designs. Traut et al. Click to enlarge.|
In a new study, a team from Carnegie Mellon University (CMU) and Ford Research and Advanced Engineering set out to determine the optimal mix for the fleet of mid-size personal vehicles in the US—while maintaining current driving patterns—with the goals of minimizing greenhouse gas emissions (GHG) or cost. They also addressed the question of GHG or cost reduction with and without a workplace charging infrastructure.
Their study, they suggested in a paper analyzing the best possible outcomes and published in the journal Energy Policy, is a step towards understanding what should be incentivized by policy makers.
They developed an optimization problem to minimize life cycle cost or GHG emissions over the personal vehicle fleet by jointly determining (1) the optimal design of each CV (conventional vehicle), HEV (hybrid electric vehicle), PHEV (plug-in hybrid electric vehicle), and BEV (battery-electric vehicle); (2) the optimal allocation of each vehicle design in the fleet based on annual vehicle miles traveled (VMT); and (3) the optimal allocation of workplace charging infrastructure to PEVs in the fleet. Within the fleet, they considered only vehicles of similar size and acceleration performance to the Toyota Prius.
They also incorporated vehicle design constraints to ensure comparable acceleration performance and vehicle allocation constraints to ensure BEVs are assigned only if they have sufficient range to accommodate the vehicle’s driving distance on most days (base case 95% of days).
Prior studies compare and select among a small set of fixed vehicle configurations based on selected commercially available vehicles or a small set of simulated vehicle alternatives. However, interactions among engine sizing, motor sizing, and battery sizing can be important in comparing vehicle characteristics, and optimal battery sizing represents a compromise among drivers with different travel patterns. We follow Shiau et al. (2010) [earlier post] and pose a mixed-integer nonlinear programming (MINLP) formulation to determine the best configuration of vehicles in the design space in order to compare the best design of each conventional vehicle (CV), HEV, PHEV, and BEV model under acceleration performance constraints that ensure vehicles are comparable. We further incorporate charging infrastructure decisions that determine which of the PEVs should be only charged at home vs. charged both at home and at the workplace, given charging infrastructure costs and production emissions, and we use driving pattern data to model required BEV ranges and PHEV electricity and gasoline usage.—Traut et al.
|Optimal vehicle allocations for minimizing life cycle GHG emissions (top) and cost (bottom) in selected scenarios. From Traut et al. Click to enlarge.|
Among their findings were:
In agreement with prior studies, without sufficient grid decarbonization, plug-in vehicles do not offer substantial GHG emissions reductions compared to HEVs. GHG reductions improve with low-carbon electricity. Thus, grid decarbonization is needed to make plug-in vehicles a relevant means of reducing GHG emissions beyond grid-independent HEVs.
Compared to CVs, HEVs offer cost and emissions reductions in almost all scenarios and are an optimal or near-optimal solution for minimizing cost across many scenarios.
Under the current US electricity generation mix, workplace charging availability provides no GHG emissions benefit in the optimized solution, but workplace charging does provide additional benefits of optimistically up to 21% in combination with low-carbon electricity.
Workplace charging availability changes the GHG-minimized vehicle allocation slightly, allocating smaller capacity PHEVs and BEVs.
Gas prices above $3.25/gal (plus 5.2% per year) cause PHEVs to appear in the minimum cost solution, but for plug-in vehicles to dominate over HEVs, either gas prices of $7/gal (plus 5.2% per year) or gas prices of $4.5/gal (plus 5.2% per year) in combination with low vehicle and battery costs (DOE 2030 program goal levels, including battery costs under $200/kWh) are needed. High carbon prices (over $100/tCO2e) do little to drive plug-in vehicles to appear in the cost-minimizing solution.
BEVs are restricted by range requirements from being a significant part of the minimum cost or GHG solutions. Even when range requirements are dramatically reduced, requiring BEV range adequate for only the average trip rather than the 95th percentile trip, a fleet of entirely BEVs is much more expensive and GHG-intensive than the other vehicle types, and BEVs are not GHG-minimizers for the full fleet even when charged with zero-emissions electricity. BEVs enter the GHG-optimal fleet only for short-range vehicles and only in cases with grid decarbonization.
This formulation represents a best-case scenario for minimizing cost or GHG emissions with these vehicle technologies; market outcomes would likely deviate, and we do not attempt to predict firm or consumer behavior.—Traut et al.
This research was supported by National Science Foundation grants from the Foundation’s Material Use, Science, Engineering and Society program, the CAREER program, and the Graduate Research Fellowship program. Support was also provided by Ford Motor Company, Toyota Motors of America, and the Steinbrenner Graduate Fellowship.
Traut, E., et al. (2012) Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost. Energy Policy doi: 10.1016/j.enpol.2012.08.061