Energy Storage System Design for Green-Energy Cyber Physical Systems

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.

Plug-in hybrid electric vehicle (PHEV) technology has the potential to help address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption from 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 integrated optimization model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation of vehicles to drivers for minimum life cycle cost, GHG emissions, and petroleum consumption. We find that, while PHEVs with large battery capacity minimize petroleum consumption, a mix of PHEVs sized for 25–40 miles of electric travel produces the greatest reduction in lifecycle GHG emissions. At today’s average US energy prices, battery pack cost must fall below $460/kWh (below $300/kWh for a 10% discount rate) for PHEVs to be cost competitive with ordinary hybrid electric vehicles (HEVs). Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs even at $100/tonne. We find that the maximum battery swing should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption. Increased swing enables greater all-electric range (AER) to be achieved with smaller battery packs, improving cost competitiveness of PHEVs. Hence, existing policies that subsidize battery cost for PHEVs would likely be better tied to AER, rather than total battery capacity.

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.

Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles

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.

Researchers at Argonne National Laboratory expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model and incorporated the fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW results were separately calculated for the blended charge-depleting (CD) and charge-sustaining (CS) modes of PHEV operation and then combined by using a weighting factor that represented the CD vehicle-miles-traveled (VMT) share. As indicated by PSAT simulations of the CD operation, grid electricity accounted for a share of the vehicle's total energy use, ranging from 6% for a PHEV 10 to 24% for a PHEV 40, based on CD VMT shares of 23% and 63%, respectively. In addition to the PHEV's fuel economy and type of on-board fuel, the marginal electricity generation mix used to charge the vehicle impacted the WTW results, especially GHG emissions. Three North American Electric Reliability Corporation regions (4, 6, and 13) were selected for this analysis, because they encompassed large metropolitan areas (Illinois, New York, and California, respectively) and provided a significant variation of marginal generation mixes. The WTW results were also reported for the U.S. generation mix and renewable electricity to examine cases of average and clean mixes, respectively. For an all-electric range (AER) between 10 mi and 40 mi, PHEVs that employed petroleum fuels (gasoline and diesel), a blend of 85% ethanol and 15% gasoline (E85), and hydrogen were shown to offer a 40-60%, 70-90%, and more than 90% reduction in petroleum energy use and a 30-60%, 40-80%, and 10-100% reduction in GHG emissions, respectively, relative to an internal combustion engine vehicle that used gasoline. The spread of WTW GHG emissions among the different fuel production technologies and grid generation mixes was wider than the spread of petroleum energy use, mainly due to the diverse fuel production technologies and feedstock sources for the fuels considered in this analysis. The PHEVs offered reductions in petroleum energy use as compared with regular hybrid electric vehicles (HEVs). More petroleum energy savings were realized as the AER increased, except when the marginal grid mix was dominated by oil-fired power generation. Similarly, more GHG emissions reductions were realized at higher AERs, except when the marginal grid generation mix was dominated by oil or coal. Electricity from renewable sources realized the largest reductions in petroleum energy use and GHG emissions for all PHEVs as the AER increased. The PHEVs that employ biomass-based fuels (e.g., biomass-E85 and -hydrogen) may not realize GHG emissions benefits over regular HEVs if the marginal generation mix is dominated by fossil sources. Uncertainties are associated with the adopted PHEV fuel consumption and marginal generation mix simulation results, which impact the WTW results and require further research. More disaggregate marginal generation data within control areas (where the actual dispatching occurs) and an improved dispatch modeling are needed to accurately assess the impact of PHEV electrification. The market penetration of the PHEVs, their total electric load, and their role as complements rather than replacements of regular HEVs are also uncertain. The effects of the number of daily charges, the time of charging, and the charging capacity have not been evaluated in this study. A more robust analysis of the VMT share of the CD operation is also needed.

Nowadays, the greatest part of the efforts to reduce pollutant emissions is directed toward the hybridization of automotive drive trains. Plug-in Hybrid Electric Vehicle (PHEV) seems to be a good short term solution for replacing the conventional combustion engine propelled vehicles, in order to improve fuel economy and reduce pollution emissions. Such topic has a particular relevance while looking at vehicles that operate in urban environment, like light commercial vehicles used for goods delivering even in limited traffic areas. In order to obtain a wide range, full performance, high efficiency vehicle and, at the same time, reduce pollutant emissions, the most feasible solution, at present, is the PHEV, which combines batteries (that can be charged during the night or enough long stops directly from the electric power grid) that feed electrical drive together with a standard Internal Combustion Engine (ICE). In fact today Full Electric Vehicles can not assure the basic requirements of driving range, performance and load capability needed for a commercial vehicle operating in urban environments, mainly because of the low energy density of actually available batteries. Considering the average daily mission of a commercial vehicle delivering goods in urban environments, PHEV can cover even long distances from the hub to the city centre, exploiting the hybrid driving mode (which can increase the efficiency with respect to standard ICEVs) and then use its pure electric driving range (30-60 km) to deliver goods inside the city centre. Since the PHEV has two on-board engines (electric and endothermic) and two energy storage systems (the electrochemical batteries and the fuel tank), energy control strategies have to be developed and introduced in order to find out the most efficient one. The full energetic model of a Plug-In Hybrid Electric Commercial Vehicle, presented in previous papers [1] and already validated exploiting experimental tests performed on a prototype developed at the Mechanical Engineering Department of Politecnico di Milano, will be used in this paper. It will be used to develop energy flows control strategies able to allow the commercial vehicle to perform its daily mission in hybrid and pure electric driving modes.

Plug-in hybrid vehicles (PHEVs) present an interesting technological opportunity for using non-fossil primary energy in light duty passenger vehicles, with the associated potential for reducing air pollutant and greenhouse gas emissions, to the extent that the electric power grid is fed by non-fossil sources. This perspective, accompanying the article by Thompson et al (2011) in this issue, will touch on two other studies that are directly related: the Argonne study (Elgowainy et al 2010) and a PhD thesis from Utrecht (van Vliet 2010).

Widespread use of alternative hybrid powertrains currently appears inevitable and many opportunities for substantial progress remain. The necessity for environmentally friendly vehicles, in conjunction with increasing concerns regarding U.S. dependency on foreign oil and climate change, has led to significant investment in enhancing the propulsion portfolio with new technologies. Recently, plug-in hybrid electric vehicles (PHEVs) have attracted considerable attention due to their potential to reduce petroleum consumption and greenhouse gas (GHG) emissions in the transportation sector. PHEVs are especially appealing for short daily commutes with excessive stop-and-go driving. However, the high costs associated with their components, and in particular, with their energy storage systems have been significant barriers to extensive market penetration of PHEVs. In the research reported here, we investigated the implications of motor/generator and battery size on fuel economy and GHG emissions in a medium duty PHEV. An optimization framework is proposed and applied to two different parallel powertrain configurations, pretransmission and post transmission, to derive the Pareto frontier with respect to motor/generator and battery size. The optimization and modeling approach adopted here facilitates better understanding of the potential benefits from proper selection of motor/generator and battery size on fuel economy and GHG emissions. This understanding can help us identify the appropriate sizing of these components and thus reducing the PHEV cost. Addressing optimal sizing of PHEV components could aim at an extensive market penetration of PHEVs.

Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are evolving rapidly since the introduction of Toyota Prius into the market in 1997. As the world needs more fuel-efficient vehicles to mitigate climate change, the role of HEVs and PHEVs are becoming ever more important. While fuel economies of HEVs and PHEVs are superior to those of internal combustion engine (ICE) powered vehicles, they are partially powered by batteries and therefore they resemble characteristics of battery electric vehicles (BEVs) such as dependence of fuel economy on ambient temperatures. It is also important to understand how different extent of hybridization (a.k.a., hybridization ratio) affects fuel economy under various driving conditions. In addition, it is of interest to understand how HEVs and PHEVs compare with BEVs at a similar vehicle weight. This study investigated the relationship between vehicle mass and vehicle performance parameters, mainly fuel economy and driving range of PHEVs focused on 2018 and 2019 model years using the test data available from fuel economy website of the US Environmental Protection Agency (EPA). Previous studies relied on modeling to understand mass impact on fuel economy for HEV as there were not enough number of HEVs in the market to draw a trendline at the time. The study also investigated the effect of ambient temperature for HEVs and PHEVs and kinetic energy recovery of the regenerative braking using the vehicle testing data for model year 2013 and 2015 from Idaho National Lab (INL). The current study assesses current state-of-art for PHEVs. It also provides analysis of experimental results for validation of vehicle dynamic and other models for PHEVs and HEVs.

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.

Plug-in hybrid electric vehicle (PHEV) technology combining the merits of Battery electric vehicle (BEV) and Hybrid electric vehicle (HEV), has the potential to reduce greenhouse gas (GHG) emissions, and petroleum consumption in the transportation sector. However, the cost-benefit of PHEVs mainly determined by battery technology, optimal powertrain design, and vehicle kilometers daily traveled and charging habits. Targeting to cost-benefit, the optimal design method was presented, taking battery cycle life Vs DOD data, driving data, battery performance data into consideration. The method provided optimal vehicle designs to realize minimum life cycle cost, and maximum petroleum consumption under different scenarios. For A-segment equivalent PHEV (similar to a F3DM), under Shanghai urban driving conditions, it can be find that while PHEVs with present traction battery technology, 30 km AER was most life cycle cost-effective to obtain maximum petroleum displacement based on Shanghai driving data. Large capacity battery lead to petroleum displacement not so much as cost increased. At China electricity price off peak, Li-ion battery pack costs must fall below ¥2.0/Wh to be cost competitive with equivalent internal combustion engine vehicles (ICEs).

Plug-in hybrid electric vehicles (PHEVs), which use electricity from the grid to power a portion of travel, could play a role in reducing greenhouse gas (GHG) emissions from the transport sector. However, meaningful GHG emissions reductions with PHEVs are conditional on low-carbon electricity sources. We assess life cycle GHG emissions from PHEVs and find that they reduce GHG emissions by 32% compared to conventional vehicles, but have small reductions compared to traditional hybrids. Batteries are an important component of PHEVs, and GHGs associated with lithium-ion battery materials and production account for 2-5% of life cycle emissions from PHEVs. We consider cellulosic ethanol use and various carbon intensities of electricity. The reduced liquid fuel requirements of PHEVs could leverage limited cellulosic ethanol resources. Electricity generation infrastructure is long-lived, and technology decisions within the next decade about electricity supplies in the power sector will affectthe potential for large GHG emissions reductions with PHEVs for several decades.

By using the 2017 National Household Travel Survey (NHTS) data, this study explores the status quo of ownership and usage of conventional vehicles (CVs) and alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate the significance of the 2017 NHTS data. The results show that after two decades of development, AFVs only occupy about 5% of annual vehicle sales, and their share does not show big increases in recent years. Meanwhile, although HEVs still dominate the AFV market, the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership, income still seems to be a major factor influencing AFV adoption, with the median annual household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000 and $200,000, respectively. Besides, AFV households are more likely to live in urban areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of old drivers are much smaller than CVs, indicating this age group might still have concerns regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by BEVs. BEVs might still be able to replace CVs for meeting most travel demands after a single charge, considering most observed daily trip distances are fewer than 93.5 km for CVs. However, the observed max daily trip distances of AFVs are still much smaller than CVs, implying increasing the endurance to meet extremely long-distance travel demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the future.

Greenhouse gas emissions of conventional and alternative vehicles: Predictions based on energy policy analysis in South Korea