A corridor-centric approach to planning electric vehicle charging infrastructure

The transition to electric vehicles (EV) faces two major barriers. On one hand, EV batteries are still expensive and limited by range, owing to the lack of technology breakthrough. On the other hand, the underdeveloped supporting infrastructure, particularly the lack of fast refueling facilities, makes EVs unsuitable for medium and long distance travel. The primary purpose of this study is to better understand these hurdles and to develop strategies to overcome them. To this end, a conceptual optimization model is proposed to analyze travel by EVs along a long corridor. The objective of the model is to select the battery size and charging capacity (in terms of both the charging power at each station and the number of stations needed along the corridor) to meet a given level of service in such a way that the total social cost is minimized. Two extensions of the base model are also considered. The first relaxes the assumption that the charging power at the stations is a continuous variable. The second variant considers battery swapping as an alternative to charging. Our analysis suggests that (1) the current paradigm of charging facility development that focuses on level 2 charging delivers poor level of service for long distance travel; (2) the level 3 charging method is necessary not only to achieve a reasonable level of service, but also to minimize the social cost; (3) investing on battery technology to reduce battery cost is likely to have larger impacts on reducing the charging cost; and (4) battery swapping promises high level of service, but it may not be socially optimal for a modest level of service, especially when the costs of constructing swapping and charging stations are close.     

The design of electric vehicle charging network

The promotion of Electric Vehicles (EVs) has become a key measure of the governments in their attempt to reduce greenhouse gas emissions. However, range anxiety is a big barrier for drivers to choose EVs over traditional vehicles. Installing more charging stations in appropriate locations can relieve EV drivers’ range anxiety. To determine the locations of public charging stations, we propose two optimization models for two different charging modes - fast and slow charging, which aim at minimizing the total cost while satisfying certain coverage goal. Instead of using discrete points, we use geometric objects to represent charging demands. Importantly, to resolve the partial coverage problem (PCP) for networks, we extend the polygon overlay method to split the demands on the road network. After applying the models to Greater Toronto and Hamilton Area (GTHA) and to Downtown Toronto, we show that the proposed models are practical and effective in determining the locations of charging stations. Moreover, they can eliminate PCP and provide much more accurate results than the complementary partial coverage method (CP).     

The vehicle emissions and performance monitoring system: analysis of tailpipe emissions and vehicle performance

This paper describes tailpipe emission results generated by the Vehicle Performance and Emissions Monitoring system (VPEMS). VPEMS integrates on‐board emissions and vehicle/driver performance measurements with positioning and communications technologies, to transmit a coherent spatio‐temporally referenced dataset to a central base station in near real time. These results focus on relationships between tailpipe emissions of CO, CO₂, NO_x and speed and acceleration. Emissions produced by different driving modes are also presented. Results are generally as one would expect, showing variation between vehicle speed, vehicle acceleration and emissions. Data is based upon a test run in central London on urban streets with speeds not exceeding about 65 km/h. The results presented demonstrate the capabilities of the system. Various issues remain with regard to validation of the data and expansion of the system capability to obtain additional vehicle performance data.     

Cutting vehicle emissions with regenerative braking

This paper presents an analysis of vehicle regenerative braking systems as a quick and relatively easy means of achieving higher overall fuel efficiency and lowering carbon emissions. The system involves the installation of an additional electric motor/generator in parallel to the vehicle’s internal combustion engine and is used in conjunction with a DCDC converter and ultracapacitor. The system is used to recapture the energy lost in vehicle braking, significantly reducing a vehicle’s overall energy consumption and lowering vehicle emissions. Experimentally-based evidence is collected and compared for two sample vehicles to deduce the potential fuel and emissions saving.     

A general corridor model for designing plug-in electric vehicle charging infrastructure to support intercity travel

This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.     

Real-time parking information management to reduce search time,vehicle displacement and emissions

The time spent searching for free parking spaces can produce considerable environmental pollution. Information on parking availability can be a powerful instrument for reducing these search costs. This paper develops a demand assignment model to evaluate the benefits of manipulating information with the objective of reducing the time and distances involved in finding a parking-place; including the walking distances involved. Using the full search procedure it was found that improvements of some 10% in efficiency could be achieved, but only at high computational costs. A genetic algorithm was programmed that increases the possibility of finding the optimum information conditions that can be translated into lower emissions of toxic greenhouse gases.     

Real world vehicle emissions: Their correlation with driving parameters

Vehicular population in developing countries is expected to proliferate in the coming decade, centred on Tier II and Tier III cities rather than large metropolis. WLTP is being introduced as a global instrument for emission regulation to reduce gap between standard test procedures and actual road conditions. This work aims at quantifying and discernment of the gap between WLTC and real-world conditions in an urban city in a developing country on the basis of driving cycle parameters and simulated emissions for gasoline fuelled light passenger cars. Real world driving patterns were recorded on different routes and varying traffic conditions using car-chasing technique integrated with GPS monitoring and speed sensors. Real-world driving patterns and ambient conditions were used to simulate emissions using International Vehicle Emissions model for average rate (g/km) and Comprehensive Modal Emissions Model for instantaneous emission (g/s) analysis. Cycle parameters were mathematically calculated to compare WLTC and road trips. The analyses revealed a large gap between WLTC and road conditions. CO emissions were predicted to be 155% higher than WLTC and HC and NOx emissions were estimated to be 63% and 64% higher respectively. These gaps were correlated to different driving cycle parameters. It was observed that road driving occurs at lower average speeds with higher frequency and magnitudes of accelerations. The positive kinetic energy required by road cycles, was 100% higher than WLTC and the Relative Positive Acceleration (RPA) demanded by road cycles, was found to be 60% higher in real-world driving patterns and thereby contribute to higher emissions.     

A structural analysis of vehicle design responses to Corporate Average Fuel Economy policy

The US Corporate Average Fuel Economy (CAFE) regulations are intended to influence automaker vehicle design and pricing choices. CAFE policy has been in effect for the past three decades, and new legislation has raised standards significantly. We present a structural analysis of automaker responses to generic CAFE policies. We depart from prior CAFE analyses by focusing on vehicle design responses in long-run oligopolistic equilibrium, and we view vehicles as differentiated products, taking demand as a general function of price and product attributes. We find that under general cost, demand, and performance functions, single-product profit maximizing firm responses to CAFE standards follow a distinct pattern: firms ignore CAFE when the standard is low, treat CAFE as a vehicle design constraint for moderate standards, and violate CAFE when the standard is high. Further, the point and extent of first violation depends upon the penalty for violation, and the corresponding vehicle design is independent of further standard increases. Thus, increasing CAFE standards will eventually have no further impact on vehicle design if the penalty for violation is also not increased. We implement a case study by incorporating vehicle physics simulation, vehicle manufacturing and technology cost models, and a mixed logit demand model to examine equilibrium powertrain design and price decisions for a fixed vehicle body. Results indicate that equilibrium vehicle design is not bound by current CAFE standards, and vehicle design decisions are directly determined by market competition and consumer preferences. We find that with increased fuel economy standards, a higher violation penalty than the current stagnant penalty is needed to cause firms to increase their design fuel economy at equilibrium. However, the maximum attainable improvement can be modest even if the penalty is doubled. We also find that firms’ design responses are more sensitive to variation in fuel prices than to CAFE standards, within the examined ranges.     

A multi-period optimization model for the deployment of public electric vehicle charging stations on network

A multi-period multipath refueling location model is developed to expand public electric vehicle (EV) charging network to dynamically satisfy origin–destination (O–D) trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost-effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites.     

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave. This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics. It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models. Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions. The vehicular composition also affects emissions. These parameters can be quantified and incorporated into the emission models. There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions     

Canada’s voluntary agreement on vehicle greenhouse gas emissions: When the details matter

The 2005 voluntary agreement between the automobile industry and Canadian government to reduce greenhouse gas emissions from passenger vehicles is evaluated. We analyze the likely effect of the agreement on emissions, and on use of biofuels and advanced vehicle technologies. We conclude that the impact on emissions could be far less than suggested, possibly even zero, even if automobile companies fully comply. The pros and cons of the Canadian agreement are assessed and compared with other voluntary and mandatory greenhouse gas reduction programs. Some lessons learned include the importance of specific performance metrics to evaluate progress, use of precise baseline measurements and methods, and an appreciation of the asymmetry in information between most governments and the affected industries.     

Congestion and emissions mitigation: A comparison of capacity,demand, and vehicle based strategies

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO₂e, CO, and NO_x through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM_2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation.     

Reducing transit fleet emissions through vehicle retrofits,replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO₂ equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.     

Experimental study of the impact of structural geometry and wind direction on vehicle emissions in urban environment

Vehicle emissions inside an urban environment are investigated using a wind-tunnel under neutral atmospheric conditions. The urban environment was formed as street canyon model. The diffusion flow field in the boundary layer inside the street canyon was examined at different locations of varying geometry of the street and wind directions in the downwind distance of the leeward side of the street canyon model. The results show that the vertical velocity increases as the aspect ratio increases and with wind direction increases from θ = 90°. The pollutant concentration increases as the aspect ratio decreases. The pollutant concentration decreases as the wind direction increases from θ = 90°. The pollutant concentration distributions indicate that the variability of the structure, geometry and wind direction inside the street canyon are important parameters for estimating air quality in the urban street canyon.     

Applying activity-travel data for the assessment of vehicle exhaust emissions: Application of a GPS-enhanced data collection tool

This paper describes the development of a global positioning system, enhanced data collection tool for the assessment of vehicle exhaust emissions. This involves the collection of activity and travel data on a personal digital assistant with built-in global positioning system receiver. By converting the second-by-second global positioning system based travel data into emissions, estimates are made of the exhausts produced by individual vehicle trips. Differences in travel behaviour and vehicle emissions were examined by gender and trip purpose.     

Development of a microscopic activity-based framework for analyzing the potential impacts of transportation control measures on vehicle emissions

The 1990 Clean Air Act Amendments (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have defined a set of transportation control measures to counter the increase in the vehicle emissions and energy consumption due to increased travel. The value of these TCM strategies is unknown as there is limited data available to measure the travel effects of individual TCM strategies and the models are inadequate in forecasting changes in travel behavior resulting from these strategies. The work described in this paper begins to provide an operational methodology to overcome these difficulties so that the impacts of the policy mandates of both CAAA and ISTEA can be assessed. Although the framework, as currently developed, falls well short of actually forecasting changes in traveler behavior relative to policy options designed to encourage emissions reduction, the approach can be useful in estimating upper bounds of certain policy alternatives in reducing vehicle emissions. Subject to this important limitation, the potential of transportation policy options to alleviate vehicle emissions is examined in a comprehensive activity-based approach. Conclusions are drawn relative to the potential emissions savings that can be expected from efficient trip chaining behavior, ridesharing among household members, as well as from technological advances in vehicle emissions control devices represented by replacing all of the vehicles in the fleet by vehicles conforming to present-day emissions technology.     

Diesel-engined vehicle nitric oxide and soot emissions during the European light-duty driving cycle using a transient mapping approach

An engine mapping-based methodology is developed to gain a first approximation of a vehicle’s performance and emissions during a light-duty cycle. The procedure is based on a steady-state experimental investigation of the engine with an appropriate vehicle drivetrain model applied so that the cycle vehicle speed data can be transformed into engine speed and torque. Correction analysis is then applied based on transient experimentation to account for the transient discrepancies during real driving. The developed algorithm is applied for the case of a diesel-engined vehicle running on the European light-duty cycle. A comparative analysis is performed for each section of the cycle revealing its individual transient characteristics.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

Multi-impact optimization to reduce aviation noise and emissions

This paper describes a multi-indicator assessment and minimization problem focused on aviation “Community” noise. The model explores a combined noise and emission objective for airfreight movements at Luxembourg’s Findel Airport. Community noise is evaluated via four population impact indicators emissions are tabulated from in-flight segments in the proximity of the airport and from the contribution of taxiing. A set of scenarios based on trajectory, aircraft scheduling, fleet composition, operational procedures are implemented in software. An integer programming methodology is used to search for minimal environmental impact. An on/off switch allows for an evaluation of each indicator in the objective. Different impacts on the population are explored in a case study involving the Cargolux Airlines International S.A.     

Green supply chain network design to reduce carbon emissions

We consider a supply chain network design problem that takes CO₂ emissions into account. Emission costs are considered alongside fixed and variable location and production costs. The relationship between CO₂ emissions and vehicle weight is modeled using a concave function leading to a concave minimization problem. As the direct solution of the resulting model is not possible, Lagrangian relaxation is used to decompose the problem into a capacitated facility location problem with single sourcing and a concave knapsack problem that can be solved easily. A Lagrangian heuristic based on the solution of the subproblem is proposed. When evaluated on a number of problems with varying capacity and cost characteristics, the proposed algorithm achieves solutions within 1% of the optimal. The test results indicate that considering emission costs can change the optimal configuration of the supply chain, confirming that emission costs should be considered when designing supply chains in jurisdictions with carbon costs.