What is the market potential of plug-in electric vehicles as commercial passenger cars? A case study from Germany

Commercial passenger cars are a possible early market segment for plug-in electric vehicles (PEVs). Compared to privately owned vehicles, the commercial vehicle segment is characterized by higher mileage and a higher share of vehicle sales in Germany. To this point, there are only few studies which analyze the commercial passenger car sector and arrive at contradictory results due to insufficient driving profile data with an observation period of only one day. Here, we calculate the market potential of PEVs for the German commercial passenger car sector by determining the technical and economical potential for PEVs in 2020 from multi-day driving profiles. We find that commercial vehicles are better suited for PEVs than private ones since they show higher average annual mileage and drive more regularly. About 87% of the analyzed three-week vehicle profiles can technically be fulfilled by battery electric vehicles (BEVs) with an electric driving range of about 110 km while plug-in hybrid electric vehicles (PHEVs) with an electric range of 40 km could obtain an electric driving share of 60% on average. In moderate energy price scenarios, PEVs can reach a market share of 2–4% in the German commercial passenger car sales by 2020 and especially the large commercial branches (Trade, Manufacturing, Administrative services and Other services) are important. However, our analysis shows a high sensitivity of results to energy and battery prices as well as electric consumptions.     

What are the value and implications of two-car households for the electric car?

The major barriers to a more widespread introduction of battery electric vehicles (BEVs) beyond early adopters are the limited range, charging limitations, and costly batteries. An important question is therefore where these effects can be most effectively mitigated. An optimization model is developed to estimate the potential for BEVs to replace one of the conventional cars in two-car households and to viably contribute to the households’ driving demand. It uses data from 1 to 3 months of simultaneous GPS logging of the movement patterns for both cars in 64 commuting Swedish two-car households in the Gothenburg region.The results show that, for home charging only, a flexible vehicle use strategy can considerably increase BEV driving and nearly eliminate the unfulfilled driving in the household due to the range and charging limitations with a small battery. The present value of this flexibility is estimated to be on average $6000–$7000 but varies considerably between households. With possible near-future prices for BEVs based on mass production cost estimates, this flexibility makes the total cost of ownership (TCO) for a BEV advantageous in almost all the investigated households compared to a conventional vehicle or a hybrid electric vehicle. Because of the ubiquity of multi-car households in developed economies, these families could be ideal candidates for the initial efforts to enhance BEV adoptions beyond the early adopters. The results of this research can inform the design and marketing of cheaper BEVs with small but enough range and contribute to increased knowledge and awareness of the suitability of BEVs in such households.     

Wireless charging in California: Range,recharge, and vehicle electrification

This research evaluated the potential for wireless dynamic charging (charging while moving) to address range and recharge issues of modern electric vehicles by considering travel to regional destinations in California. A 200-mile electric vehicle with a real range of 160 miles plus 40 miles reserve was assumed to be used by consumers in concert with static and dynamic charging as a strict substitute for gasoline vehicle travel. Different combinations of wireless charging power (20–120 kW) and vehicle range (100–300 miles) were evaluated. One of the results highlighted in the research indicated that travel between popular destinations could be accomplished with a 200-mile EV and a 40 kW dynamic wireless charging system at a cost of about $2.5 billion. System cost for a 200-mile EV could be reduced to less than $1 billion if wireless vehicle charging power levels were increased to 100 kW or greater. For vehicles consuming 138 kWh of dynamic energy per year on a 40 kW dynamic system, the capital cost of $2.5 billion plus yearly energy costs could be recouped over a 20-year period at an average cost to each vehicle owner of $512 per year at a volume of 300,000 vehicles or $168 per year at a volume of 1,000,000 vehicles. Cost comparisons of dynamic charging, increased battery capacity, and gasoline refueling were presented. Dynamic charging, coupled with strategic wayside static charging, was shown to be more cost effective to the consumer over a 10-year period than gasoline refueling at $2.50 or $4.00 per gallon. Notably, even at very low battery prices of $100 per kWh, the research showed that dynamic charging can be a more cost effective approach to extending range than increasing battery capacity.     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data

The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline and electricity. Moreover, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist’s daily travel distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3–18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. On the other hand, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.     

Implementation and test of a hybrid storage system on an electric urban bus

Customer acceptance of Battery Electric Vehicles (BEVs) depends strongly on the performance of the Energy Storage System (ESS). Energy density, power density and lifetime of ESSs are three key parameters to be optimized in a BEV. For this purpose the use of a hybrid energy source on board of electric vehicles has been proposed and analyzed in literature. However, most of the previous studies have been limited to simulation or to test bench experiments of the ESS. This paper focuses on the implementation and use of the association of high energy NiCd battery and high power supercapacitors on board of a 3.5 t urban bus. An uncomplicated and efficient energy management strategy has been implemented and tested. The behavior of the vehicle has been investigated by experiment on a roller test bench for two different driving cycles, highlighting the effects of the hybridization: reduction of losses within the battery with consequent expected lifetime extension, improved dynamic of the vehicle and a possible driving range extension.     

Are multi-car households better suited for battery electric vehicles? – Driving patterns and economics in Sweden and Germany

Battery electric vehicles (BEVs) could reduce CO₂ emissions from the transport sector but their limited electric driving range diminishes their utility to users. The effect of the limited driving range can be reduced in multi-car households where users could choose between a BEV and a conventional car for long-distance travel. However, to what extent the driving patterns of different cars in a multi-car household’s suit the characteristics of a BEV needs further analysis. In this paper we analyse the probability of daily driving above a fixed threshold for conventional cars in current Swedish and German car driving data. We find second cars in multi-car households to require less adaptation and to be better suited for BEV adoption compared to first cars in multi-car households as well as to cars in single-car households. Specifically, the share of second cars that could fulfil all their driving is 20 percentage points higher compared to first cars and cars from single-car households. This result is stable against variation of driving range and of the tolerated number of days requiring adaptation. Furthermore, the range needed to cover all driving needs for about 70% of the vehicles is only 220 km for second cars compared to 390 km for the average car. We can further confirm that second cars have higher market viability from a total cost of ownership perspective. Here, the second cars achieve a 10 percentage points higher market share compared to first cars, and to cars in single-car households for Swedish economic conditions, while for Germany the corresponding figure is 2 percentage points. Our results are important for understanding the market viability of current and near-future BEVs.     

Effects of battery chemistry and performance on the life cycle greenhouse gas intensity of electric mobility

Lithium traction batteries are a key enabling technology for plug-in electric vehicles (PEVs). Traction battery manufacture contributes to vehicle production emissions, and battery performance can have significant effects on life cycle greenhouse gas (GHG) emissions for PEVs. To assess emissions from PEVs, a life cycle perspective that accounts for vehicle production and operation is needed. However, the contribution of batteries to life cycle emissions hinge on a number of factors that are largely absent from previous analyses, notably the interaction of battery chemistry alternatives and the number of electric vehicle kilometers of travel (e-VKT) delivered by a battery. We compare life cycle GHG emissions from lithium-based traction batteries for vehicles using a probabilistic approach based on 24 hypothetical vehicles modeled on the current US market. We simulate life-cycle emissions for five commercial lithium chemistries. Examining these chemistries leads to estimates of emissions from battery production of 194–494 kg CO₂ equivalent (CO₂e) per kWh of battery capacity. Combined battery production and fuel cycle emissions intensity for plug-in hybrid electric vehicles is 226–386 g CO₂e/e-VKT, and for all-electric vehicles 148–254 g CO₂e/e-VKT. This compares to emissions for vehicle operation alone of 140–244 g CO₂e/e-VKT for grid-charged electric vehicles. Emissions estimates are highly dependent on the emissions intensity of the operating grid, but other upstream factors including material production emissions, and operating conditions including battery cycle life and climate, also affect life cycle GHG performance. Overall, we find battery production is 5–15% of vehicle operation GHG emissions on an e-VKT basis.     

How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?

The entry of various plug-in electric vehicles (PEVs) into the passenger vehicle sector provides novel opportunities to learn about the potential for future PEV markets. However, early PEV buyers (or “Pioneers”) can substantially differ from present conventional vehicle owners that have interest in purchasing PEVs in the future (or the “Potential Early Mainstream buyers”). To compare the characteristics, preferences, and motivations of Pioneers and Potential Early Mainstream buyers, we draw data from the Canadian Plug-in Electric Vehicle Study, a three-part mixed-mode survey with samples of PEV owners (n = 94) and conventional new vehicle buyers (n = 1754). We identify several significant differences in household characteristics, including income, education, and recharge access. In terms of preferences, Pioneers express extremely high valuation of PEVs and prefer pure battery electric vehicle (BEV) designs over plug-in hybrid electric (PHEV) designs. In contrast, Potential Early Mainstream respondents prefer PHEVs. Both Pioneer and Potential Early Mainstream respondents are similarly cautious about controlled charging programs, but Pioneers place five times as much value on using electricity generated from renewable sources than the Potential Early Mainstream. Pioneers also tend to have different motivations, including significantly higher levels of environmental concern, and higher engagement in environment- and technology-oriented lifestyles. Policymakers, automakers, and electric utilities that anticipate a transition to electric mobility ought to consider how potential future PEV buyers may differ in their vehicle preferences, usage and motivations relative to current PEV owners.     

China’s electric car frustrations

By 2020, the vehicle population in China will likely exceed 280 million—exacerbating national energy security, urban air pollution, and traffic congestion. In response, many local and regional governments in China are pursuing an expanding array of measures to restrain growth in personal vehicle ownership and, along with the central government, reducing emissions and energy use of vehicles. One prominent strategy is the promotion of new energy vehicles, especially plug-in electric vehicles (PEVs). Large subsidies were offered—up to $27,600 (171,000 RMB) per vehicle in some regions, including almost $9200 (57,000 RMB) from the central government—which suggests that China is making a major commitment to PEVs. But sales have been meager. In 2013, only 17,600 PEVs, mostly buses and utility trucks, were sold, less than 0.1% of total civilian vehicle sales. Several factors explain the failure of PEV sales to take off: (1) protectionism by local governments; (2) uncertainty over which electric-drive vehicle technologies to promote and what consumers are willing to pay, (3) lagging investments in charging infrastructure, and (4) conservative investment behavior by automakers and battery manufacturers. The central government issued directives to local governments in late 2013 to reduce barriers to out-of-town companies, resulting in modest sales increases in early 2014, but a more coherent, broader, and effective set of policies, incentives, and strategies are needed to overcome consumer and industry resistance and the lack of charging infrastructure.     

Comparative analysis of the energy consumption and CO2 emissions of 40 electric,plug-in hybrid electric,hybrid electric and internal combustion engine vehicles

This paper analyses the results of the Royal Automobile Clubhallo’s 2011 RAC Future Car Challenge, an annual motoring challenge in which participants seek to consume the least energy possible while driving a 92 km route from Brighton to London in the UK. The results reveal that the vehicle’s power train type has the largest impact on energy consumption and emissions. The traction ratio, defined as the fraction of time spent on the accelerator in relation to the driving time, and the amount of regenerative braking have a significant effect on the individual energy consumption of vehicles. In contrast, the average speed does not have a great effect on a vehicles’ energy consumption in the range 25–70 km/h.     

Fast-charging station choice behavior among battery electric vehicle users

This study explores how battery electric vehicle users choose where to fast-charge their vehicles from a set of charging stations, as well as the distance by which they are generally willing to detour for fast-charging. The focus is on fast-charging events during trips that include just one fast-charge between origin and destination in Kanagawa Prefecture, Japan. Mixed logit models with and without a threshold effect for detour distance are applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Findings from the mixed logit model with threshold show that private users are generally willing to detour up to about 1750 m on working days and 750 m on non-working days, while the distance is 500 m for commercial users on both working and non-working days. Users in general prefer to charge at stations requiring a shorter detour and use chargers located at gas stations, and are significantly affected by the remaining charge. Commercial users prefer to charge at stations encountered earlier along their paths, while only private users traveling on working days show such preference and they turn to prefer the stations encountered later when choosing a station in peak hours. Only private users traveling on working days show a strong preference for free charging. Commercial users tend to pay for charging at a station within 500 m detour distance. The fast charging station choice behavior is heterogeneous among users. These findings provide a basis for early planning of a public fast charging infrastructure.     

A new offline optimization approach for designing a fuel cell hybrid bus

In this study a hydrogen powered fuel cell hybrid bus is optimized in terms of the powertrain components and in terms of the energy management strategy. Firstly the vehicle is optimized aiming to minimize the cost of its powertrain components, in an official driving cycle. The optimization variables in powertrain component design are different models and sizes of fuel cells, of electric motors and controllers, and batteries. After the component design, an energy management strategy (EMS) optimization is performed in the official driving cycle and in two real measured driving cycles, aiming to minimize the fuel consumption. The EMS optimization is based on the control of the battery’s state-of-charge. The real driving cycles are representative of bus driving in urban routes within Lisbon and Oporto Portuguese cities. A real-coded genetic algorithm is developed to perform the optimization, and linked with the vehicle simulation software ADVISOR. The trade-off between cost increase and fuel consumption reduction is discussed in the lifetime of the designed bus and compared to a conventional diesel bus. Although the cost of the optimized hybrid powertrain (62,230 €) achieves 9 times the cost of a conventional diesel bus, the improved efficiency of such powertrain achieved 36% and 34% of lower energy consumption for the real driving cycles, OportoDC and LisbonDC, which can originate savings of around 0.43 €/km and 0.37 €/km respectively. The optimization methodology presented in this work, aside being an offline method, demonstrated great improvements in performance and energy consumption in real driving cycles, and can be a great advantage in the design of a hybrid vehicle.     

Ultrafine particle infiltration into passenger vehicles. Part II: Model analysis

Experimental studies showed that infiltration and passive ventilation are important air exchange mechanisms inside vehicles but previous mathematical models did not consider either one. In this study, we incorporated infiltration and passive ventilation to advance the existing mathematical models and evaluated how different transport mechanisms affect passenger exposures at increasing speeds. Infiltration was formulated using Bernoulli’s equation and passive ventilation was derived empirically. The new model describes ultrafine particle (UFP) and carbon dioxide (CO₂) transport for a wide range of driving speed under any ventilation conditions. Unlike statistical models, this mathematical model can also provide vehicle-specific and transport mechanism-specific information. The model predictions were in a good agreement with data collected from 10 different vehicle models with an average discrepancy of less than 16% for UFPs and less than 3% for CO₂. Under outdoor air (OA) mode, when the fan is off, the model simulation showed that the infiltration and passive ventilation can substantially increase the UFP I/O (in-cabin/on-road concentrations) ratio from 0.15 at 0 km/h to 0.57 at 130 km/h. At medium fan setting, mechanical ventilation dominates and UFP I/O stays at 0.58 regardless of driving speed. Under recirculation (RC) mode, infiltration increases and the RC-mode filtration only removed 44% and 69% of the infiltrated particles at the lowest and medium fan settings, respectively. Model simulations under OA mode show that infiltration starts to occur above 115 km/h with the lowest fan setting; whereas, medium and higher fan settings prevent infiltration up to 145 km/h.     

How driver behaviour and parking alignment affects inductive charging systems for electric vehicles

Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transfer of electrical energy is highly reliant on the accurate alignment of the coils involved. Therefore understanding the issue of parking misalignment and driver behaviour is an important human factors question, and the focus of this paper. Two studies were conducted, one a retrospective analysis of 100 pre-parked vehicles, the second a dynamic study where 10 participants parked an EV aiming to align with a charging pad with no bay markings as guidance. Results from both studies suggest that drivers are more accurate at parking laterally than in the longitudinal direction, with a mean lateral distance from the centre of the bay being 12.12 and 9.57 cm (retrospective and dynamic studies respectively) compared to longitudinally 23.73 and 73.48 cm. With current inductive charging systems having typical tolerances of approximately ±10 cm from their centre point, this study has shown that only 5% of vehicles in both studies would be aligned sufficiently accurately to allow efficient transfer of electrical energy through induction.     

Statistical modeling of Electric Vehicle electricity consumption in the Victorian EV Trial,Australia

The market share of Electric Vehicles (EVs), an attractive alternative to conventional vehicles, is expected to exceed 30% of all vehicles by 2033 in Australia. Although the expected EV uptake may place greater burdens on electricity networks, the potential impacts contributed by different EV user categories and vehicle models to peak loads at different times during the day are not well understood. This paper addresses the issue through statistical analysis of the charge events in the Victorian EV Trial in Australia as well as modeling the charging behaviors according to participant categories and vehicle models. The analysis was performed on 4933 charge events that were recorded by both private and public Electric Vehicle Supply Equipment. In total, these events consumed over 33 MW h of energy over 12,170 h by the 178 trial participants, out of which about 70% were household participants while the others were fleet participants. Based on a range of EV uptake scenarios and modeled charging behaviors from the trial, the power demand in the summer of 2032/33 was estimated for all of Victoria. The results of the simulations show that the broad scale uptake of EVs produces a relatively small increase in overall power demand (estimated to be between 5.72% and 9.79% in 2032/33).     

Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses

The variance in fuel consumption caused by driving style (DS) difference exceeds 10% and reaches a maximum of 20% under different road conditions, even for experienced bus drivers. To study the influence of DS on fuel consumption, a method for summarizing DS characteristic parameters on the basis of vehicle-engine combined model is proposed. With this method, the author proposes 26 DS characteristic parameters related to fuel consumption in the accelerating, normal running, and decelerating processes of vehicles. The influence of DS characteristic parameters on fuel consumption under different road conditions and vehicle masses is quantitatively analyzed on the basis of real driving data over 100,000 km. Analysis results show that the influence of DS characteristic parameters on fuel consumption changes with road condition and vehicle mass, with road condition serving a more important function. However, the DS characteristics in the accelerating process of vehicles are decisive for fuel consumption under different conditions. This study also calculates the minimum sample size necessary for analyzing the effect of DS characteristics on fuel consumption. The statistical analysis based on the real driving data over 2500 km can determine the influence of DS on fuel consumption under a given power-train configuration and road condition. The analysis results can be employed to evaluate the fuel consumption of drivers, as well as to guide the design of Driver Advisory System for Eco-driving directly.     

Assessing CO2 emissions of electric vehicles in Germany in 2030

Electric vehicles are often said to reduce carbon dioxide (CO₂) emissions. However, the results of current comparisons with conventional vehicles are not always in favor of electric vehicles. We outline that this is not only due to the different assumptions in the time of charging and the country-specific electricity generation mix, but also due to the applied assessment method. We, therefore, discuss four assessment methods (average annual electricity mix, average time-dependent electricity mix, marginal electricity mix, and balancing zero emissions) and analyze the corresponding CO₂ emissions for Germany in 2030 using an optimizing energy system model (PERSEUS-NET-TS). Furthermore, we distinguish between an uncontrolled (i.e. direct) charging and an optimized controlled charging strategy. For Germany, the different assessment methods lead to substantial discrepancies in CO₂ emissions for 2030 ranging from no emissions to about 0.55 kg/kWh_el (110 g/km). These emissions partly exceed the emissions from internal combustion engine vehicles. Furthermore, depending on the underlying power plant portfolio and the controlling objective, controlled charging might help to reduce CO₂ emissions and relieve the electricity grid. We therefore recommend to support controlled charging, to develop consistent methodologies to address key factors affecting CO₂ emissions by electric vehicles, and to implement efficient policy instruments which guarantee emission free mobility with electric vehicles agreed upon by researchers and policy makers.     

Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads

The purpose of our study is to develop a “corrected average emission model,” i.e., an improved average speed model that accurately calculates CO₂ emissions on the road. When emissions from the central roads of a city are calculated, the existing average speed model only reflects the driving behavior of a vehicle that accelerates and decelerates due to signals and traffic. Therefore, we verified the accuracy of the average speed model, analyzed the causes of errors based on the instantaneous model utilizing second-by-second data from driving in a city center, and then developed a corrected model that can improve the accuracy. We collected GPS data from probe vehicles, and calculated and analyzed the average emissions and instantaneous emissions per link unit. Our results showed that the average speed model underestimated CO₂ emissions with an increase in acceleration and idle time for a speed range of 20 km/h and below, which is the speed range for traffic congestion. Based on these results, we analyzed the relationship between average emissions and instantaneous emissions according to the average speed per link unit, and we developed a model that performed better with an improved accuracy of calculated CO₂ emissions for 20 km/h and below.     

Influence of driving patterns on vehicle emissions: A case study for Latin American cities

On-board real-time emission experiments were conducted on 78 light-duty vehicles in Bogota. Direct emissions of carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NO_x) and hydrocarbons (HC) were measured. The relationship between such emissions and vehicle specific power (VSP) was established. The experimental matrix included both gasoline-powered and retrofit dual fuel (gasoline–natural gas) vehicles. The results confirm that VSP is an appropriate metric to obtain correlations between driving patterns and air pollutant emissions. Ninety-five percent of the time vehicles in Bogota operate in a VSP between −15.2 and 17.7 kW ton⁻¹, and 50% of the time they operate between −2.9 and 1.2 kW ton⁻¹, representing low engine-load and near-idling conditions, respectively. When engines are subjected to higher loads, pollutant emissions increase significantly. This demonstrates the relevance of reviewing smog check programs and command-and-control measures in Latin America, which are widely based on static (i.e., idling) emissions testing. The effect of different driving patterns on the city’s emissions inventory was determined using VSP and numerical simulations. For example, improving vehicle flow and reducing sudden and frequent accelerations could curb annual emissions in Bogota by up to 12% for CO₂, 13% for CO and HC, and 24% for NO_x. This also represents possible fuel consumption savings of between 35 and 85 million gallons per year and total potential economic benefits of up to 1400 million dollars per year.