Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed.     

Impacts of driving patterns on tank-to-wheel energy use of plug-in hybrid electric vehicles

We evaluate the implications of a range of driving patterns on the tank-to-wheel energy use of plug-in hybrid electric vehicles. The driving patterns, which reflect short distance, low speed, and congested city driving to long distance, high speed, and uncongested highway driving, are estimated using an approach that involves linked traffic assignment and vehicle motion models. We find substantial variation in tank-to-wheel energy use of plug-in hybrid electric vehicles across driving patterns. Tank-to-wheel petroleum energy use on a per kilometer basis is lowest for the city and highest for the highway driving, with the opposite holding for a conventional internal combustion engine vehicle.     

Life cycle emissions and cost model for urban light duty vehicles

The growth of vehicle sales and use internationally requires the consumption of significant quantities of energy and materials, and contributes to the deterioration of air-quality and climate conditions. Advanced propulsion systems and electric drive vehicles have substantially different characteristics and impacts. They require life cycle assessments and detailed comparisons with gasoline powered vehicles which, in turn, should lead to critical updates of traditional models and assumptions. For a comprehensive comparison of advanced and traditional light duty vehicles, a model is developed that integrates external costs, including emissions and time losses, with societal and consumer life cycle costs. Life cycle emissions and time losses are converted into costs for seven urban light duty vehicles. The results, which are based on vehicle technology characteristics and transportation impacts on environment, facilitate vehicle comparisons and support policy making in transportation. Substantially, more sustainable urban transportation can be achieved in the short-term by promoting policies that increase vehicle occupancy; in the intermediate-term by increasing the share of hybrid vehicles in the car market and in the long-term by the widespread use of electric vehicles. A sensitivity-analysis of life cost results revealed that vehicle costs change significantly for different geographical areas depending on vehicle taxation, pricing of gasoline, electric power and pollution. Current practices in carbon and air quality pricing favor oil and coal based technologies. However, increasing the cost of electricity from coal and other fossil fuels would increase the variable cost for electric vehicles, and tend to favor the variable cost of hybrid vehicles.     

An eco-driving system for electric vehicles with signal control under V2X environment

The benefit of eco-driving of electric vehicles (EVs) has been studied with the promising connected vehicle (i.e. V2X) technology in recent years. Whereas, it is still in doubt that how traffic signal control affects EV energy consumption. Therefore, it is necessary to explore the interactions between the traffic signal control and EV energy consumption. This research aims at studying the energy efficiency and traffic mobility of the EV system under V2X environment. An optimization model is proposed to meet both operation and energy efficiency for an EV transportation system with both connected EVs (CEVs) and non-CEVs. For CEVs, a stage-wise approximation model is implemented to provide an optimal speed control strategy. Non-CEVs obey a car-following rule suggested by the well-known Intelligent Driver Model (IDM) to achieve eco-driving. The eco-driving EV system is then integrated with signal control and a bi-objective and multi-stage optimization problem is formulated. For such a large-scale problem, a hybrid intelligent algorithm merging genetic algorithm (GA) and particle swarm optimization (PSO) is implemented. At last, a validation case is performed on an arterial with four intersections with different traffic demands. Results show that cycle-based signal control could improve both traffic mobility and energy saving of the EV system with eco-driving compared to a fixed signal timing plan. The total consumed energy decreases as the CEV penetration rate augments in general.     

GPS-based optimization of plug-in hybrid electric vehicles’ power demands in a cold weather city

Using the WPG03 duty cycle developed from global positioning data collected in Winnipeg, Canada, real world energy demands and costs are modeled. Three types of plug-in hybrid electric vehicles, four temperatures and two charging scenarios are compared to a vehicle with an internal combustion engine. Cold temperatures are shown to greatly affect vehicle operation energy costs, which is an important consideration for cold weather cities such as Winnipeg. The largest energy cost savings are obtained for smaller-battery plug-in hybrids that had the opportunity to charge during the day.     

Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.     

基于AMESim的插电式并联混合动力汽车能量管理策略仿真分析

为了缩短混合动力汽车开发时间,减少开发成本,本文以插电式并联混合动力汽车为研究对象,针对设计指标进行动力系统参数匹配以及使用AMESim软件搭建了整车模型,然后设计了基于门限值的能量管理策略并使用AMESim软件中的Signal,Control库进行搭建。之后对已搭建完成的车辆进行动力性经济性仿真分析,其中经济性分析是在NEDC工况下进行的,验证了本文所搭建策略和整车模型的正确性和可行性。     

Within-day recharge of plug-in hybrid electric vehicles: Energy impact of public charging infrastructure

This paper examines the role of public charging infrastructure in increasing the share of driving on electricity that plug-in hybrid electric vehicles might exhibit, thus reducing their gasoline consumption. Vehicle activity data obtained from a global positioning system tracked household travel survey in Austin, Texas, is used to estimate gasoline and electricity consumptions of plug-in hybrid electric vehicles. Drivers’ within-day recharging behavior, constrained by travel activities and public charger availability, is modeled. It is found that public charging offers greater fuel savings for hybrid electric vehicles s equipped with smaller batteries, by encouraging within-day recharge, and providing an extensive public charging service is expected to reduce plug-in hybrid electric vehicles gasoline consumption by more than 30% and energy cost by 10%, compared to the scenario of home charging only.     

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.     

Retrofitted plug-in hybrid vehicles: Results of NYIT drive share program

This paper presents results from a plug-in hybrid vehicle drive share program involving retrofitted hybrid electric vehicles. A potential for high fuel efficiency is indicated, however, the average fuel efficiency was only marginally better than conventional hybrid vehicles. This is due to the majority of vehicle miles traveled occurring on trips outside the “all electric” range and very short trips where fuel consumption is dominated by emissions control strategies. The work also considers the availability of the battery for vehicle to grid services and finds that there are a large number of trips in the afternoon period, typically when electrical demand is at a peak. Vehicle charging activity also tended towards daytime activity, contrary to the oft-assumed off-peak charging pattern.     

Electric vehicles for greenhouse gas reduction in China: A cost-effectiveness analysis

There have been ongoing debates over whether battery electric vehicles contribute to reducing greenhouse gas emissions in China’s context, and if yes, whether the greenhouse gas emissions reduction compensates the cost increment. This study informs such debate by examining the life-cycle cost and greenhouse gas emissions of conventional vehicles, hybrid electric vehicles and battery electric vehicles, and comparing their cost-effectiveness for reducing greenhouse gas emissions. The results indicate that under a wide range of vehicle and driving configurations (range capacity, vehicle use intensity, etc.), battery electric vehicles contribute to reducing greenhouse gas emissions compared with conventional vehicles, although their current cost-effectiveness is not comparable with hybrid electric vehicles. Driven by grid mix optimization, power generation efficiency improvement, and battery cost reduction, the cost-effectiveness of battery electric vehicles is expected to improve significantly over the coming decade and surpass hybrid electric vehicles. However, considerable uncertainty exists due to the potential impacts from factors such as gasoline price. Based on the analysis, it is recommended that the deployment of battery electric vehicles should be prioritized in intensively-used fleets such as taxis to realize high cost-effectiveness. Technology improvements both in terms of power generation and vehicle electrification are essential in improving the cost-effectiveness of battery electric vehicles.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.     

A comparison of technologies for carbon-neutral passenger transport

In this study, the use of energy carriers based on renewable energy sources in battery-powered electric vehicles (BPEVs), fuel-cell electric vehicles (FCEVs), hybrid electric vehicles (HEVs) and internal combustion engine vehicles (ICEVs) is compared regarding energy efficiency, emission and cost. There is the potential to double the primary energy compared with the current level by utilising vehicles with electric drivetrains. There is also major potential to increase the efficiency of conventional ICEVs. The energy and environmental cost of using a passenger car can be reduced by 50% solely by using improved ICEVs instead of ICEVs with current technical standard. All the studied vehicles with alternative powertrains (HEVs, FCEVs, and BPEVs) would have lower energy and environmental costs than the ICEV. The HEVs, FCEVs and BPEVs have, however, higher costs than the future methanol-fuelled ICEV, if the vehicle cost is added to the energy and environmental costs, even if significant cost reductions for key technologies such as fuel cells, batteries and fuel storages are assumed. The high-energy efficiency and low emissions of these vehicles cannot compensate for the high vehicle cost. The study indicates, however, that energy-efficiency improvements, combined with the use of renewable energy, would reduce the cost of CO₂ reduction by 40% compared with a strategy based on fuel substitution only.     

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.     

Simulating the household plug-in hybrid electric vehicle distribution and its electric distribution network impacts

This paper presents a multi agent-based simulation framework for modeling spatial distribution of plug-in hybrid electric vehicle ownership at local residential level, discovering “plug-in hybrid electric vehicle hot zones” where ownership may quickly increase in the near future, and estimating the impacts of the increasing plug-in hybrid electric vehicle ownership on the local electric distribution network with different charging strategies. We use Knox County, Tennessee as a case study to highlight the simulation results of the agent-based simulation framework.     

Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet

Municipal fleet vehicle purchase decisions provide a direct opportunity for cities to reduce emissions of greenhouse gases (GHG) and air pollutants. However, cities typically lack comprehensive data on total life cycle impacts of various conventional and alternative fueled vehicles (AFV) considered for fleet purchase. The City of Houston, Texas, has been a leader in incorporating hybrid electric (HEV), plug-in hybrid electric (PHEV), and battery electric (BEV) vehicles into its fleet, but has yet to adopt any natural gas-powered light-duty vehicles. The City is considering additional AFV purchases but lacks systematic analysis of emissions and costs. Using City of Houston data, we calculate total fuel cycle GHG and air pollutant emissions of additional conventional gasoline vehicles, HEVs, PHEVs, BEVs, and compressed natural gas (CNG) vehicles to the City's fleet. Analyses are conducted with the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model. Levelized cost per kilometer is calculated for each vehicle option, incorporating initial purchase price minus residual value, plus fuel and maintenance costs. Results show that HEVs can achieve 36% lower GHG emissions with a levelized cost nearly equal to a conventional sedan. BEVs and PHEVs provide further emissions reductions, but at levelized costs 32% and 50% higher than HEVs, respectively. CNG sedans and trucks provide 11% emissions reductions, but at 25% and 63% higher levelized costs, respectively. While the results presented here are specific to conditions and vehicle options currently faced by one city, the methods deployed here are broadly applicable to informing fleet purchase decisions.     

Development a new power management strategy for power split hybrid electric vehicles

Reduction of greenhouse gas emission and fuel consumption as one of the main goals of automotive industry leading to the development hybrid vehicles. The objective of this paper is to investigate the energy management system and control strategies effect on fuel consumption, air pollution and performance of hybrid vehicles in various driving cycles. In order to simulate the hybrid vehicle, the combined feedback–feedforward architecture of the power-split hybrid electric vehicle based on Toyota Prius configuration is modeled, together with necessary dynamic features of subsystem or components in ADVISOR. Multi input fuzzy logic controller developed for energy management controller to improve the fuel economy of a power-split hybrid electric vehicle with contrast to conventional Toyota Prius Hybrid rule-based controller. Then, effects of battery’s initial state of charge, driving cycles and road grade investigated on hybrid vehicle performance to evaluate fuel consumption and pollution emissions. The simulation results represent the effectiveness and applicability of the proposed control strategy. Also, results indicate that proposed controller is reduced fuel consumption in real and modal driving cycles about 21% and 6% respectively.     

The market potential for plug-in hybrid and battery electric vehicles in Flanders: A choice-based conjoint analysis

This paper considers the market potential for battery electric and plug-in hybrid electric vehicles in Flanders, Belgium. Making use of a large-scale survey conducted in 2011 and applying a choice-based conjoint experiment, it is predicted that by 2020, battery electric vehicles could have a market share of about 5% of new vehicles, and plug-in hybrid electric vehicles could have a share of around 7%. By 2030, these figures could increase to 15% and 29%. The speed of up-take of electric vehicles, however, is sensitive to purchase costs.     

Public charging infrastructure for plug-in electric vehicles: What is it worth?

Lack of charging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Public charging infrastructure has tangible and intangible value, such as reducing range anxiety or building confidence in the future of the PEV market. Quantifying the value of public charging infrastructure can inform analysis of investment decisions and can help predict the impact of charging infrastructure on future PEV sales. Estimates of willingness to pay (WTP) based on stated preference surveys are limited by consumers’ lack of familiarity with PEVs. As an alternative, we focus on quantifying the tangible value of public PEV chargers in terms of their ability to displace gasoline use for PHEVs and to enable additional electric (e−) vehicle miles for BEVs, thereby mitigating the limitations of shorter range and longer recharging time. Simulation studies provide data that can be used to quantify e-miles enabled by public chargers and the value of additional e-miles can be inferred from econometric estimates of WTP for increased vehicle range. Functions are synthesized that estimate the WTP for public charging infrastructure by plug-in hybrid and battery electric vehicles, conditional on vehicle range, annual vehicle travel, pre-existing charging infrastructure, energy prices, vehicle efficiency, and household income. A case study based on California’s public charging network in 2017 indicates that, to the purchaser of a new BEV with a 100-mile range and home recharging, existing public fast chargers are worth about $1500 for intraregional travel, and fast chargers along intercity routes are valued at over $6500.     

交通拥堵智能监测的车联网环境构建

车联网是互联网以及物联网通讯技术引入交通领域而发展出来的一类智能交通应用技术。本文在解析车联网基本架构体系的前提下,围绕交通拥堵智能监测的优化实现,基于V2V模型,构建一种智能监测的车联网典型应用环境,并参考车联网基本特征,探讨定义智能监测条件下的交通拥堵评价基准。