Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems

The deployment of battery-powered electric bus systems within the public transportation sector plays an important role in increasing energy efficiency and abating emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line. The bus energy consumption for each route segment is determined based on individual route, bus type, traffic, and other information. Different scenarios are examined in order to assess the influence of charging power, climate, and changing operating conditions. The findings reveal significant differences in terms of required infrastructure. Moreover, the results highlight a trade-off between battery capacity and charging infrastructure under different operational and infrastructure conditions. This paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.     

Modeling electric taxis' charging behavior using real-world data

Charging behavior is critical to the development and deployment of electric vehicle (EV) systems, given its impacts in EV adoption, the energy and environmental performance of EVs, potential load change to the electric grid, etc. However, the general characteristics of practical charging behavior have not been well studied. Existing studies are mostly based on travel data from conventional internal combustion engine vehicles, modeled with assumed and simplified charging scenarios. The use of public charging infrastructure is often neglected. Few studies evaluate real-world charging behaviors of EVs currently in operation using public charging stations. To address this gap, this study analyzes the data of 39,372 charging events from 129 unique electric taxis in Shenzhen, China to study the distributions of daily charging frequency, charging start time, and charging duration. The insights we learned from this study are: 1) the daily frequency for a vehicle to visit charging stations is unlikely to exceed five times; 2) the distribution of charging start time have multiple peaks and can be fitted with Gaussian Mixture Models; 3) charging duration is influenced by charging start time; and 4) charging dynamics can be modeled using the distributions of daily charging frequency, charging start time, and charging duration. Results from this study can inform charging behavior modeling for EVs and charging infrastructure development.     

Optimization models for placement of an energy-aware electric vehicle charging infrastructure

This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Are batteries ready for plug-in hybrid buyers?

The notion persists that battery technology and cost remain as barriers to commercialization of electric-drive passenger vehicles. Within the context of starting a market for plug-in hybrid electric vehicles (PHEVs), we explore two aspects of the purported problem: (1) PHEV performance goals and (2) the abilities of present and near-term battery chemistries to meet the resulting technological requirements. We summarize evidence stating that battery technologies do not meet the requirements that flow from three sets of influential PHEV goals due to inherent trade-offs among power, energy, longevity, cost, and safety. However, we also show that part of this battery problem is that those influential goals are overly ambitious compared to goals derived from consumers’ PHEV designs. We elicited PHEV designs from potential early buyers among U.S. new car buyers; most of those who are interested in a PHEV are interested in less technologically advanced PHEVs than assumed by experts. Using respondents’ PHEV designs, we derive peak power density and energy density requirements and show that current battery chemistries can meet them. By assuming too aggressive PHEV goals, existing policy initiatives, battery research, and vehicle development programs mischaracterize the batteries needed to start commercializing PHEVs. To answer the question whether batteries are ready for PHEVs, we must first answer the question, “whose PHEVs?”     

How uncertain is the future of electric vehicle market: Results from Monte Carlo simulations using a nested logit model

Plug-in electric vehicles (PEVs) are widely regarded as an important component of the technology portfolio designed to accomplish policy goals in sustainability and energy security. However, the market acceptance of PEVs in the future remains largely uncertain from today's perspective. By integrating a consumer choice model based on nested multinomial logit and Monte Carlo simulation, this study analyzes the uncertainty of PEV market penetration using Monte Carlo simulation. Results suggest that the future market for PEVs is highly uncertain and there is a substantial risk of low penetration in the early and midterm market. Top factors contributing to market share variability are price sensitivities, energy cost, range limitation, and charging availability. The results also illustrate the potential effect of public policies in promoting PEVs through investment in battery technology and infrastructure deployment. Continued improvement of battery technologies and deployment of charging infrastructure alone do not necessarily reduce the spread of market share distributions, but may shift distributions toward right, i.e., increase the probability of having great market success.     

Why is electric vehicle uptake low in Atlantic Canada? A comparison to leading adoption provinces

This paper investigates potential electric vehicle (EV) adoption among households in Atlantic Canada, a region lagging in terms of EV uptake. The data come from a 2015 Survey for Preferences and Attitudes of Canadians towards Electric Vehicles or SPACE, with an intent to investigate electric mobility prospects in Canada through a series of socioeconomic, attitudinal, and stated preference (SP) questions. A latent class (LC) random utility model is used to segment Atlantic respondents based on their sociodemographic and environmental attitudes, and to estimate their willingness-to-pay for different vehicular features. A separate model is estimated for leading adoption provinces (Ontario and British Columbia), and compared to the Atlantic model. Results indicate that cash incentives and the quality of battery warranty are important features shaping the choice of vehicle powertrain in the Atlantic sample. Contrary to the model obtained from the leading provinces, electric range, maintenance cost, free parking, and access to high occupancy vehicle lanes are not significant attributes in the Atlantic model. With respect to segmentation, the adoption of EVs in the Atlantic model increases with youth, education, and progressive attitudes towards the environment, while income is not a determining factor. Our results imply some support for EVs among Atlantic consumers, though at less advanced levels than the leading adoption provinces in Canada. However, the high purchase price of EVs, lack of financial incentives, and limited public charging infrastructure are seen as key reasons for low EV deployment in Atlantic Canada. The demographic profiles of the plug-in oriented group, found in this study, suggest targeted decisions regarding policy and marketing.     

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

Assessing greenhouse gas emissions from electric vehicle operation in Australia using temporal vehicle charging and electricity emission characteristics

ABSTRACT

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required. A key issue in this regard is the extent to which GHG emissions are a function of where and when EV charging will be enabled (or disabled) by the provision of recharging infrastructure and implementation of charging management strategies by the electricity industry. This article presents an investigation of the GHG emissions arising from electricity and gasoline consumption by plug-in hybrid EVs under a range of standard EV-power system integration scenarios. An assessment framework is presented, and GHG emissions from EV use are assessed for the New South Wales (NSW) and South Australian (SA) pools of the Australian National Electricity Market (NEM) using retrospective electricity system generation data for 2011. Results highlight that there is a range of possible outcomes depending on the integration scenario and emissions accounting approach used. This range illustrates value of a temporally explicit assessment approach in capturing the temporal alignment of electricity sector emission intensity and EV charging. Results also show the importance of a clean electricity generation mix in order for EVs to provide a GHG abatement benefit beyond what would be achieved by a hybrid (but non-plug-in) vehicle. The extent to which overnight charging in NSW is observed to produce higher emissions relative to unmanaged charging also illustrates a possible trade-off between GHG emissions and benefits for electricity industry from EV charging at times of low demand.     

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

We introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver’s stopping tolerance arises as another critical factor affecting the transportation costs.     

Exploring the choice of battery electric vehicles in city logistics: A conjoint-based choice analysis

Adoption of electric vehicles by transport companies remains limited although major European cities should reach CO₂-free city logistics by 2030. This paper explores therefore the vehicle choice behaviour of transport companies through a conjoint-based choice analysis.The results showed that the benefits of battery electric vehicles are less valued than their disadvantages. However, a majority of respondents agrees that authorities should encourage the use of battery electric vehicles. Based on the preferences of transporters, we conclude that the most important measures are to develop a larger charging infrastructure and implement financial incentives through subsidies or tax exemption.     

Vehicle routing optimization with soft time windows in a fuzzy random environment

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.     

Supply chain leading models of building charging stations: Leaders,subsidy policies,and cost sharing

Consumer willingness to pay for electric vehicles (EVs) is severely limited by their driving range. The expansion of a charging network could alleviate this dilemma. This paper focuses on determining whether the manufacturer or the dealer is more suitable to extend charging network. In scenario 1 (wholesale price is exogenous), M-Investing (the manufacturer invests on charging stations) better facilitates EV adoption at the early stage of EV market. By contrast, D-Investing (the dealer invests on charging stations) better facilitates the EV adoption when the market becomes mature. However, neither of the two investors have an incentive to offer building investment. In scenario 2 (wholesale price is a decision made by the manufacturer), M-Investing is consistently better than D-Investing in terms of facilitating EV adoption. The manufacturer is voluntary even with high building costs. In addition, we address two subsidy policies: producer subsidy and consumer subsidy, to determine which is more effective in facilitating EV adoption in M-Investing and D-Investing, respectively. Moreover, we extend our model by allowing cost sharing between the manufacturer and the dealer. We observe some cases in which the profit and the EV adoption level are improved.     

A review of spatial localization methodologies for the electric vehicle charging infrastructure

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem’s spatial dimension, and derives perspectives for further research.     

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

基于遗传模拟退火算法的战时物流配送路径优化研究

综合考虑战时物流配送车辆路径问题（VRP）的多目标评价，提出多属性道路网络下战时物流配送的VRP算法，并建立完全分层优化模型。将进化算法与传统优化技术相结合，构造了模型的两层求解算法，第一层采用遗传算法和模拟退火算法混合的GASA算法，第二层采用枚举法。并以成品燃油配送为例进行了实验，结果表明算法较标准遗传算法更有效。     

Where are the electric vehicles? A spatial model for vehicle-choice count data

Electric vehicles (EVs) are predicted to increase in market share as auto manufacturers introduce more fuel efficient vehicles to meet stricter fuel economy mandates and fossil fuel costs remain unpredictable. Reflecting spatial autocorrelation while controlling for a variety of demographic and locational (e.g., built environment) attributes, the zone-level spatial count model in this paper offers valuable information for power providers and charging station location decisions. By anticipating over 745,000 personal-vehicle registrations across a sample of 1000 census block groups in the Philadelphia region, a trivariate Poisson-lognormal conditional autoregressive (CAR) model anticipates Prius hybrid EV, other EV, and conventional vehicle ownership levels. Initial results signal higher EV ownership rates in more central zones with higher household incomes, along with significant residual spatial autocorrelation, suggesting that spatially-correlated latent variables and/or peer (neighbor) effects on purchase decisions are present. Such data sets will become more comprehensive and informative as EV market shares rise. This work’s multivariate Poisson-lognormal CAR modeling approach offers a rigorous, behaviorally-defensible framework for spatial patterns in choice behavior.     

Dually sustainable urban mobility option: Shared-taxi operations with electric vehicles

Electric vehicles (EVs) are energy efficient and often presented as a zero-emission transport mode to achieve long-term decarbonization visions in the transport sector. The implementation of a sustainable transportation environment through EV utilization, however, requires the addressing of certain cost and environmental concerns such as limited driving range and battery-charging issues before its full potential can be realized. Nevertheless, a specific type of use of EVs, namely in taxi services, may elicit positive public opinion, as it promises a commitment toward sustainability in urban life. In light of this, this study proposes an integrated approach that combines EV operation with a conceptual design for shared-ride taxi services. As some productivity loss may be naturally expected due to the time spent in charging, it is important to look at whether such performance loss from the passenger and system standpoints can be offset with ingenuity in operational design. In this study, an EV taxi charge-replenishing scheme that can be coupled with a real-time taxi-dispatch algorithm is designed. The proposed EV charging schemes for taxi services are studied via simulations and the effects of the limited driving range and battery-charging details are examined from a system performance viewpoint. The simulation study also reveals illustrative results on the impact of the EV taxi fleet's operation on the charging system. Next, a real-time shared-taxi operation scheme that allows ride sharing with other passengers is proposed to maximize the operational efficiency. The simulation results suggest that the shared-taxi concept can be a viable option to improve on the limitations caused by EV operation. In addition, the importance of projected charging demands and queue delays at different charging locations are also addressed. Some limitations and a future research agenda are also discussed.     

Selecting sustainable electric bus powertrains using multipreference evolutionary algorithms

Constant improvement of vehicle technologies towards more efficient powertrains and reduced pollutant emissions, frequently leads to the increase of the vehicle or fuel costs, compromising its viability. Multi-objective optimization methods are commonly used to solve such problems, finding optimal trade-off solutions relatively conflicting objectives. Nevertheless, vehicle driving performance, is often disregarded from the optimization process or considered only as a fixed constraint. This may raise some issues, which are discussed in this paper: (a) vehicle dynamics are not improved, (b) trade-off optimal solutions are not distinguishable, (c) interesting solutions near constraints limits won´t be considered if constraints are not marginally relaxed.

This paper proposes a method to optimize three electric-drive vehicle options for an urban bus, a battery electric (BEV), a fuel cell hybrid (FC-HEV) and a plug-in hybrid (FC-PHEV), aiming minimum carbon footprint, maximum financial indicator and simultaneously improved driving performance (speed, acceleration, and electric range). The carbon footprint is assessed by a life cycle (LC) approach, considering the impact of the fuel production and use, and vehicle embodied materials; while the financial assessment considers the vehicle and fuel costs. The spherical pruning multi-objective differential evolution algorithm (spMODE-II) is used in the optimization, considering different preference regions within the problem constraints and objectives. The vehicle solutions optimality and suitability are compared with other multi-objective algorithm, NSGA-II.

The FC-HEV achieved the lowest LC emissions (547 g/km), and the FC-PHEV the maximum financial gain (0.19 $/km), while the BEV achieved the best trade-off of solutions.