Optimal Plug-In Hybrid 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 $590/kW h at a 5% discount rate or below $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.

Optimal design and allocation of electrified vehicles and dedicated charging infrastructure for minimum life cycle greenhouse gas emissions and cost

Electrified vehicles, including plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), have the potential to reduce greenhouse gas (GHG) emissions from personal transportation by shifting energy demand from gasoline to electricity. GHG reduction potential depends on vehicle design, adoption, driving and charging patterns, charging infrastructure, and electricity generation mix. We construct an optimization model to study these factors by determining optimal design of conventional vehicles (CVs), hybrid electric vehicles (HEVs), PHEVs, and BEVs and optimal allocation of vehicle designs and charging infrastructure in the fleet for minimum lifecycle GHG emissions over a range of scenarios. We focus on vehicles with similar size and acceleration to a Toyota Prius under urban EPA driving conditions. We find that under today’s U.S. average grid mix, the vehicle fleet allocated for minimum GHG emissions includes HEVs and PHEVs with ~30 miles (48 km) of electric range. Allocating only CVs, HEVs, PHEVs, or BEVs will produce 86%, 1%, 0%, or 13+% more life cycle GHG emissions, respectively. Unlike BEVs, PHEVs do consume some gasoline; however, PHEVs can power a large portion of vehicle miles on electrical energy while accommodating infrequent long trips without need for a large battery pack, with its corresponding production and weight implications. Availability of workplace charging for 90% of vehicles optimistically reduces optimized GHG emissions by 0.5%. Under decarbonized grid scenarios, larger battery packs are more competitive and reduce life cycle GHG emissions significantly. Future work will relax modeling assumptions and address life cycle cost and cost-effectiveness of GHG reductions.

Plug-in hybrid electric vehicles (PHEVs) have potential to reduce greenhouse gas (GHG) emissions in the U.S. light-duty vehicle fleet. GHG emissions from PHEVs and other vehicles depend on both vehicle design and driver behavior. We pose a twice-differentiable, factorable mixed-integer nonlinear programming model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation for minimizing lifecycle greenhouse gas (GHG) emissions. The resulting nonconvex optimization problem is solved using a convexification-based branch-and-reduce algorithm, which achieves global solutions. In contrast, a randomized multistart approach with local search algorithms finds global solutions in 59% of trials for the two-vehicle case and 18% of trials for the three-vehicle case. Results indicate that minimum GHG emissions is achieved with a mix of PHEVs sized for around 35 miles of electric travel. Larger battery packs allow longer travel on electric power, but additional battery production and weight result in higher GHG emissions, unless significant grid decarbonization is achieved. PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over ordinary hybrid electric vehicles. Optimal allocation of different vehicles to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.

Aluminum production is a major energy consumer and important source of greenhouse gas (GHG) emissions globally. Estimation of the energy consumption and GHG emissions caused by aluminum production in China has attracted widespread attention because China produces more than half of the global aluminum. This paper conducted life cycle (LC) energy consumption and GHG emissions analysis of primary and recycled aluminum in China for the year 2020, considering the provincial differences on both the scale of self-generated electricity consumed in primary aluminum production and the generation source of grid electricity. Potentials for energy saving and GHG emissions reductions were also investigated. The results indicate that there are 157,207 MJ of primary fossil energy (PE) consumption and 15,947 kg CO2-eq of GHG emissions per ton of primary aluminum ingot production in China, with the LC GHG emissions as high as 1.5–3.5 times that of developed economies. The LC PE consumption and GHG emissions of recycled aluminum are very low, only 7.5% and 5.3% that of primary aluminum, respectively. Provincial-level results indicate that the LC PE and GHG emissions intensities of primary aluminum in the main production areas are generally higher while those of recycled aluminum are lower in the main production areas. LC PE consumption and GHG emissions can be significantly reduced by decreasing electricity consumption, self-generated electricity management, low-carbon grid electricity development, and industrial relocation. Based on this study, policy suggestions for China’s aluminum industry are proposed. Recycled aluminum industry development, restriction of self-generated electricity, low-carbon electricity utilization, and industrial relocation should be promoted as they are highly helpful for reducing the LC PE consumption and GHG emissions of the aluminum industry. In addition, it is recommended that the central government considers the differences among provinces when designing and implementing policies.

This paper examines the impact on the life cycle greenhouse gas (GHG) emissions reduction when fossil-fueled ICE gasoline, diesel and natural gas vehicles, hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are banned in a step-by-step manner from 2035. We examine the impact of vehicle bans on life cycle GHG emissions and on the marginal cost (MC) of emissions reduction using four different scenarios defined by hydrogen production method, renewable energy share, and infrastructure development for refueling stations. The vehicle penetration and the fuel demand are determined by a consumer choice model characterized by a multinomial logit algorithm. Our analysis found that vehicle bans significantly promote battery electric vehicles (BEVs) for mini-sized vehicles and hydrogen fuel cell vehicles (FCVs) for light and heavy-duty vehicles. A vehicle ban that excludes BEVs and FCVs from 2035 under an enhanced infrastructure plan can reduce the life cycle GHG emissions as much as 438 million tonnes by 2060 compared to the 2017 level. The MC of the life cycle GHG mitigation decreases continuously and reaches as low as $482 per tonne CO2eq in 2060. However, if PHEVs are excluded from the ban, the life cycle GHG emissions are reduced more by 88 Mt-CO2eq in 2060 at a lower MC of $122 per tonne CO2eq. This is due to decreases in GHG emissions from VP where the replacement of PHEVs for BEVs and FCVs reduces the production of batteries and fuel cells. The main structure of the model.

Development and application of an electric vehicles life-cycle energy consumption and greenhouse gas emissions analysis model

Plug-in hybrid electric vehicle (PHEVs) technology has the potential to address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption. However, the net implications of PHEVs depend critically on the distances they are driven between charges: Urban drivers with short commutes who can charge frequently may benefit economically from PHEVs while also reducing fuel consumption and GHG emissions, but drivers who cannot charge frequently are unlikely to make up the cost of large PHEV battery packs with future fuel cost savings. We construct an optimization model to determine the optimal PHEV design and optimal allocation of PHEVs, hybrid-electric vehicles (HEVs) and conventional vehicles (CVs) to drivers in order to minimize net cost, fuel consumption, and GHG emissions. We use data from the 2001 National Household Transportation Survey to estimate the distribution of distance driven per day across vehicles. We find that (1) minimum fuel consumption is achieved by assigning large capacity PHEVs to all drivers; (2) minimum cost is achieved by assigning small capacity PHEVs to all drivers; and (3) minimum greenhouse gas emissions is achieved by assigning medium-capacity PHEVs to drivers who can charge frequently and large-capacity PHEVs to drivers who charge less frequently.

A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies

Impacts of alternative fuel combustion in cement manufacturing: Life cycle greenhouse gas, biogenic carbon, and criteria air contaminant emissions

Introducing demand to supply ratio as a new metric for understanding life cycle greenhouse gas (GHG) emissions from rainwater harvesting systems

It is essential to increase food production to meet the projected population increase while reducing environmental loads. Biological nitrification inhibition (BNI)-enabled wheat genetic stocks are under development through chromosome engineering by transferring chromosomal regions carrying the BNI trait from a wild relative (Leymus racemosus (Lam.) Tzvelev) into elite wheat varieties; field evaluation of these newly developed BNI-wheat varieties has started. Ten years from now, BNI-enabled elite wheat varieties are expected to be deployed in wheat production systems. This study aims to evaluate the impacts of introducing these novel genetic solutions on life cycle greenhouse gas (LC-GHG) emissions, nitrogen (N) fertilizer application rates and N-use efficiency (NUE). Scenarios were developed based on evidence of nitrification inhibition and nitrous oxide (N2O) emission reduction by BNI crops and by synthetic nitrification inhibitors (SNIs), as both BNI-wheat and SNIs slow the nitrification process. Scenarios including BNI-wheat will inhibit nitrification by 30% by 2030 and 40% by 2050. It was assumed that N fertilizer application rates can potentially be reduced, as N losses through N2O emissions, leaching and runoff are expected to be lower. The results show that the impacts from BNI-wheat with 40% nitrification inhibition by 2050 are assessed to be positive: a 15.0% reduction in N fertilization, a 15.9% reduction in LC-GHG emissions, and a 16.7% improvement in NUE at the farm level. An increase in ammonia volatilization had little influence on the reduction in LC-GHG emissions. The GHG emissions associated with N fertilizer production and soil N2O emissions can be reduced between 7.3 and 9.5% across the wheat-harvested area worldwide by BNI-wheat with 30% and 40% nitrification inhibition, respectively. However, the present study recommends further technological developments (e.g. further developments in BNI-wheat and the development of more powerful SNIs) to reduce environmental impacts while improving wheat production to meet the increasing worldwide demand.

Plastic production and consumption contribute to climate change and the depletion of non-renewable fossil resources, necessitating a shift towards a circular economy. This study explored the potential of pyrolysis as a novel approach to managing plastic waste and achieving plastic circularity in the Netherlands. Specifically, we focused on the pyrolysis of DKR-350, a low-quality mixed-plastic sorting residue. Using the life cycle assessment framework, we analysed DKR-350 pyrolysis, based on empirical data from pilot-scale trials, from two perspectives depending on the system’s primary function: waste management or naphtha production. We also considered the impacts of pyrolysis feedstock pre-treatment, including washing. Our findings demonstrated that pyrolysis of DKR-350, with lifecycle greenhouse gas (GHG) emissions of 876 kg CO2 eq. per 1000 kg pyrolysed unwashed DKR-350, can offer significant environmental benefits compared to incineration, resulting in a 28%–31% reduction in lifecycle GHG emissions. Sensitivity analysis showed the potential for achieving a 39%–65% reduction in GHG emissions by 2030, with lifecycle GHG emissions representing a mere 470 kg CO2 eq. per 1000 kg pyrolysed unwashed DKR-350 for the best sensitivity case. Lastly, we analysed the carbon recovery efficiency – a potential circularity indicator based on substance flow – resulting in 38%–55% of recovered carbon in pyrolysis oil, the system’s main product from a lifecycle perspective.

Vehicle lightweighting vs. electrification: Life cycle energy and GHG emissions results for diverse powertrain vehicles

We pose a reformulated model for optimal design and allocation of conventional (CV), hybrid electric (HEV), and plug-in hybrid electric (PHEV) vehicles to obtain global solutions that minimize life cycle greenhouse gas (GHG) emissions of the fleet. The reformulation is a twice-differentiable, factorable, nonconvex mixed-integer nonlinear programming (MINLP) model that can be solved globally using a convexification-based branch-and-reduce algorithm. We compare results to a randomized multistart local-search approach for the original formulation and find that local-search algorithms locate global solutions in 59% of trials for the two-segment case and 18% of trials for the three-segment case. The results indicate that minimum GHG emissions are achieved with a mix of PHEVs sized for 25–45 miles of electric travel. Larger battery packs allow longer travel on electrical energy, but production and weight of underutilized batteries result in higher GHG emissions. Under the current average U.S. grid mix, PHEVs offer a nearly 50% reduction in life cycle GHG emissions relative to equivalent conventional vehicles and about 5% improvement over HEVs when driven on the standard urban driving cycle. Optimal allocation of different PHEVs to different drivers turns out to be of second order importance for minimizing net life cycle GHGs.