Electric Vehicle Taxis Research Articles

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Dynamic pricing for integrated energy-traffic systems from a cyber-physical-human perspective

This paper solves the vehicle assignment problem of an operating entity with dispatchable vehicles (e.g., an urban Integrated Energy-Traffic System (IETS) coordinator or an e-hailing company) in the context of energy-traffic coordinated modeling and analysis. Current research on electric vehicle (EV) scheduling and optimization has broadly assumed that human beings are rational decision-makers and obey the scheduling plan. In contrast, we proposed a user-centric dynamic pricing scheme that improves the system performance by incentivizing human participants to follow the expected behavioral patterns from a Cyber-Physical-Human System (CPHS) perspective. The interdisciplinary fields of power systems, traffic systems, and human decision-making were used to study the interactions in the CPHS. The proposed incentive scheme was applied to the urban IETS scenarios based on real-world Global Positioning System (GPS) data of EV taxis, and the results showed that it effectively optimized the integrated system.

Effects of Charging Infrastructure and Non-Electric Taxi Competition on Electric Taxi Adoption Incentives in New York City

With major investments in electric taxis emerging around the world, there is a need to better understand resource allocation trade-offs in subsidizing electric vehicle taxis (e-taxis) and investing in electric charging infrastructure. This is addressed using simulation experiments conducted in New York City: 2016 taxi pickups/drop-offs, a Manhattan road network (16,782 nodes, 23,337 links), and 212 charging stations specified by a 2013 Taxi & Limousine Commission study. The simulation is based on a platform used to evaluate taxi operations in California and Seoul. Eleven scenarios are analyzed: a baseline of 7,000 non-electric taxis, five scenarios ranging from 1,000 e-taxis to 5,000 e-taxis, and another five scenarios in which the e-taxis have infinite chargers as an upper bound. The study finds that the number of charging locations recommended in the earlier study may be insufficient at some locations even under the 3,000+ e-taxi scenarios. More importantly, despite an average revenue of $260 per taxi for the 7,000 non-electric taxis and about $247 per taxi for electric taxis over the finite charger scenarios, the revenue gap between e-taxis and non-electric taxis in a mixed fleet increases significantly as the e-taxi share increases. This is because the increasing queue delay imposed on e-taxis gives non-electric taxis an increasing competitive advantage, raising their average revenue from $260 per taxi (1,000 e-taxis) up to $286 per taxi (5,000 e-taxis, 150% revenue gap increase), all other operating costs being equal. This has implications for individual versus whole-fleet policies, as the individual-oriented policies may be less effective.

Economic appraisal of implementing electric vehicle taxis in Seoul

Global commitments for climate change mitigation and sustainable development have given the electric vehicle renewed attention as a critical building block of sustainable transport. However, the diffusion of electric vehicles has been limited so far. In an effort to facilitate scaled-up deployment of electric vehicles, this paper examines the economic feasibility of replacing the current fleet of fossil fuel taxis in Seoul with electric vehicle (EV) taxis by gradually phasing out the former based on their operational lifetime. In the cost-benefit analysis for such a transition, costs of purchasing vehicles and constructing and maintaining charging infrastructure are estimated based on the market prices and characteristics of taxi transport in Seoul. On the benefit side, the avoided environmental costs due to reduced air pollutants and greenhouse gas emissions are calculated. Traffic assignment is incorporated in the calculation process for a better estimation of the avoided costs through more accurate link flows and speeds on the road network. Savings in fuel and maintenance costs are also estimated. The resultant benefit-cost ratio signifies economic viability of the deployment of EV taxis. The results of the study are encouraging and could strengthen the argument for pushing the transition to electric vehicles forward.

A four-step method for electric-vehicle charging facility deployment in a dense city: An empirical study in Singapore

In the recent decade, electric vehicles (EVs), as a clean and cost-effective transport means, are paving the way to replace conventional gasoline vehicles. To facilitate wider adoption of EVs, corresponding charging infrastructures have to be established first. In this paper, we propose a four-step method to deploy normal and fast charging stations that can satisfy the charging demand of private EVs, 1-shift and 2-shift EV taxis in a mature city. The proposed four-step method provides an easy-to-implement procedure for charging demand estimation and distribution. First, we derive the charging frequency and type of charging facilities for each type of EVs based on their technical specifications and operational characteristics. Then, total demand for normal and fast charging facilities can be generated by using up-to-date transport statistics, and these demand is allocated to various charging sites (car parks or petrol stations) based on spatial distribution of EVs. Given the average daily engaged working hours of a charger, service capacity at each charging station is thus determined. A case study of Singapore is put forward in the end and a scenario analysis is conducted to demonstrate the impact of driving range.

Solar electricity production and taxi electrical vehicle conversion in Chile

Carbon emissions from the Chilean public transport sector have doubled between 2000 and 2013. Thus, the need to find alternative clean-air solutions is becoming increasingly critical. With this in mind, this paper addresses a solution to the aforementioned problem by studying the conversion of both taxis and “colectivos” (public taxis with defined routes) into electric vehicles (EV) to be used in the Chilean public transport sector. In Chile, 80% of taxis are Nissan V16s, meaning that all of these vehicles will be replaced and end up in landfills within the next 5–8 years. This study presents an option to give a second life to these vehicles, addressing environmental and financial issues. It compares emissions from the Nissan V16's conventional internal combustion engine (ICE) with an equivalent electric conversion prototype. Furthermore, it analyses the CO2 emissions from the EV that has been fully charged by the Chilean grid as well as one that has been charged by a solar photovoltaic system. A solar recharging station for EV taxis is designed, taking advantage of the high levels of solar radiation in Chile. The results show that EV conversion does not lead to real environmental benefits, in comparison to its ICE equivalent, when it is charged with the grid. However, the results also show a considerable decrease in cost and CO2 emissions per km travelled when using solar energy to charge the batteries. Future challenges include identifying solutions to cope with solar intermittence and minimizing CO2 emissions during periods of low radiation.

Conditions for electric vehicle taxi: A case study in the Greater Stockholm region

This study investigates the usability of electric vehicles (EVs) in a taxi company in Greater Stockholm, Sweden. By investigating cost and revenue data of both electric and conventional taxi vehicles, as well as by interviewing taxi drivers and carriers, an assessment has been made of the financial and operational implications of using EVs in a company’s taxi fleet. Both the drivers’ and the carriers’ perspectives have been examined. The main findings are that the investigated e-taxis have a similar or lower Total Cost of Ownership and slightly higher profitability than the investigated conventional taxis. For taxi drivers, using e-taxis implies more advanced planning and revenue service time being sacrificed for charging. However, certain customers’ preferences for EVs, as well as benefits such as corporate clients favoring e-taxis and a zero emission priority queuing system at Stockholm’s main international airport (partly) compensate for time devoted to charging. In order to facilitate increased use of e-taxis, more fast charging facilities should become available at strategic locations. Besides that, there are signs that carriers’ lack of information about the opportunities and consequences of shifting towards e-taxis hamper a wider deployment of e-taxis.

Optimal scheduling of a taxi fleet with mixed electric and gasoline vehicles to service advance reservations

This study addresses the problem of scheduling a fleet of taxis that are appointed to solely service customers with advance reservations. In contrast to previous studies that have dealt with the planning and operations of a taxi fleet with only electric vehicles (EVs), we consider that most taxi companies may have to operate with fleets comprised of both gasoline vehicles (GVs) and plug-in EVs during the transition from GV to (complete) EV taxi fleets. This paper presents an innovative multi-layer taxi-flow time-space network which effectively describes the movements of the taxis in the dimensions of space and time. An optimization model is then developed based on the time-space network to determine an optimal schedule for the taxi fleet. The objective is to minimize the total operating cost of the fleet, with a set of operating constraints for the EVs and GVs included in the model. Given that the model is formulated as an integer multi-commodity network flow problem, which is characterized as NP-hard, we propose two simple but effective decomposition-based heuristics to efficiently solve the problem with practical sizes. Test instances generated based on the data provided by a Taiwan taxi company are solved to evaluate the solution algorithms. The results show that the gaps between the objective values of the heuristic solutions and those of the optimal solutions are less than 3%, and the heuristics require much less time to obtain the good quality solutions. As a result, it is shown that the model, coupled with the algorithms, can be an effective planning tool to assist the company in routing and scheduling its fleet to service reservation customers.

A Cost-Oriented Optimal Model of Electric Vehicle Taxi Systems

This paper presents a cost optimal model of electric vehicle taxi systems based on the cost of electric vehicle taxi companies, charging or battery swap stations, passengers’ time, and emission costs. Considering the requirement of meeting passengers’ travel demands, an electric taxi demand model using transportation elasticity is formulated to optimize the number of electric taxis. The electric taxi demand model constitutes the measure of electric taxis, the cruise range, the amount of charging or battery swap stations, and other related factors. Simultaneously, to meet the charging requirements of electric taxis, a layout optimal model of EVSE (electric vehicle supply equipment) is designed using a Voronoi polygon method aimed at the cost of charging or battery swap stations and the range cost for changing. Finally, these aforementioned models are mixed to calculate the scale of electric taxis, the allocation of vehicle models, the optimizing level, and the site distribution of charging or battery swap stations. The key findings include the following: (1) the cost of the BEV(battery electric vehicle) taxi system is lower in the charging model than in the battery swap model, (2) the cost of the PHEV taxi system is lower than the BEV taxi system in the charging model, (3) in the Tongzhou District of Beijing, five charging or battery swap stations required being found to meet the charging demands of 5557 BEVs in the charging model or 5316 BEVs in the battery swap model, (4) according to the passengers’ travel demands and traffic conditions in Tongzhou, the BEV’s cruise range ought to be 250 km and BEV’s battery capacity should be 42.5 kW, the price of PHEV should be under 24,000 RMB and the electric-powered cruise range needs to be under 100 km, the daily operating time of EVs is around 16 h and the daily operating range is controlled under 380 km, and (5) a carbon tax is suggested to be imposed on ICEVs but the price should be under 20 RMB per ton.

EV charging behaviour analysis and modelling based on mobile crowdsensing data

With the growing application of electric vehicles (EVs), it is of great significance to have a deep understanding of EV users driving and charging patterns for charging forecasting. However, the rapid growth scale of EV taxis with charging patterns that are closely coupled with human behaviours of temporal-spatial charging choices was not compatible with most previous coordinated strategies. Unlike the majority of existing approaches, a large volume of second-level EV global positioning system (GPS) data was used to study the behaviour patterns of EV users. In practise, a mobile crowd-sensing system that records GPS data and transmits information to the server was deployed in a fleet of electric-taxi cabs in Shenzhen, China, making it possible to record the exact behaviour of each vehicle. Travelling and charging statuses of EVs were recorded and analysed into different characteristics of behaviour for each user. The load forecast methods proved to be more effective with more knowledge of both history and real-time data.

Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis

Electric vehicle (EV) taxis have been introduced into the public transportation systems to increase EV market penetration. Different from regular taxis that can refuel in minutes, EV taxis' recharging cycles can be as long as one hour. Due to the long cycle, the bad decision on the charging station, i.e., choosing one without empty charging piles, may lead to a long waiting time of more than an hour in the worst case. Therefore, choosing the right charging station is very important to reduce the overall waiting time. Considering that the waiting time can be a nonnegligible portion to the total work hours, the decision will naturally affect the revenue of individual EV taxis. The current practice of a taxi driver is to choose a station heuristically without a global knowledge. However, the heuristical choice can be a bad one that leads to more waiting time. Such cases can be easily observed in current collected taxi data in Shenzhen, China. Our analysis shows that there exists a large room for improvement in the extra waiting time as large as 30 min/driver. In this paper, we provide a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi's historical recharging events and real-time GPS trajectories, the current operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, we can recommend a charging station that leads to the minimal total time before its recharging starts. Extensive experiments verified that our predicted time is relatively accurate and can reduce the cost time of EV taxis by 50% in Shenzhen.

A Feasibility Test on Adopting Electric Vehicles to Serve as Taxis in Daejeon Metropolitan City of South Korea

For realizing sustainable development, EV (Electric Vehicle) is currently considered as one of the most promising alternative due to its cleanness and inexhaustibility. However, the development and dissemination of EV has stagnated because it faces major constraints such as battery performance and an excessively long charging time. Thus, this study examined the feasibility of using EVs as taxis by analyzing real data from a pilot project in Daejeon, a metropolitan city in South Korea for proposing the effective way to adopt EV. To reflect reality and improve accuracy, we adopted scenarios and assumptions based on in-depth interviews with groups of experts. The resulting initial benefit-to-cost (B/C) ratio for EV taxis is approximately 0.4, which is quite low compared to 0.7 for traditional taxis. However, after incorporating some further assumptions into the calculation, the B/C ratio shifts to approximately 0.7, which is more appropriate for EV adoption. For this improvement to be achieved, the dissemination of a charging infrastructure, improvement of the business model and policy support is strongly needed. Limitations to this work and potential areas for future study are also fully discussed.

Trajectory-interception based method for electric vehicle taxi charging station problem with real taxi data

ABSTRACTTaxi fleets are suitable for the first stage of deployment of electric vehicles (EV) because EV taxis can provide chances for more people to experience EV. However, the lack of charging facilities and limited operation range are barriers to the adoption of EV taxis. As EV taxis can charge when there is no passenger in the vehicle, the conventional origin-destination (OD)-based flow-interception method for the charging station problem is not appropriate. In this study, we present a new EV taxi charging facility planning model that uses the trajectory-interception method with real taxi trajectory data and electric vehicle operation data. Global positioning system (GPS) data from 1,000 vehicles (taxis) and taxi battery performance data from three EVs are used. We developed an optimization algorithm to find optimal charger distribution considering charger installation cost and waiting delay simultaneously based on the evolution algorithm. The results show that the maintenance cost of the chargers and the charging stations is more sensitive than the opportunity cost of taxis, including additional travel distance and time to minimize the total cost. The methodology proposed in this study provides a robust result for the charging station location problem and can be widely applied to other cities with similar situations.

A feasibility study for introducing electric vehicle taxis based on the life-cycle cost analysis

전기택시는 일반 시민들이 탑승을 하면서 쉽게 체험할 수 있는 장점이 있기 때문에 전기자동차 초기 보급 모델로 매 우 유용하다. 전기택시는 택시회사의 경영 개선에 도움이 되어야 택시회사에 도입될 수 있다. 그러므로 택시 사업자의 입장에서 비용이나 편익을 분석하여 도입에 대한 사업성이 검토되어야 한다. 그러나 LPG택시와 전기택시의 실제 운행 자료에 기반하여 두 대안을 비교할 수 있는 사업성 연구는 아직 없다. 본 연구에서는 대전광역시의 LPG택시 및 전기택 시의 실제 운행 자료를 토대로 생애주기비용을 산정하고 사업성을 분석하는 방법론과 분석 결과, 민감도 분석을 통한 정책적 시사점을 도출하였다. 평가 결과 현재의 배터리 기술, 차량 가격에서는 정부 보조금이 지급된 상태에서 충전시 간을 택시기사의 식사, 휴식 시간 등으로 활용해서 손실비용으로 계상하지 않을 경우에 LPG택시보다 총비용이 낮고 사 업성이 있다. 향후 연구로는 차량운행 후 수년이 지난 전기택시의 연비, 유지비 등 실적 자료에 기반 한 사업성 검증 연 구와 하이브리드 택시 등 다른 방식 친환경차량과의 사업성 비교 연구가 필요하다.

The optimal design and cost implications of electric vehicle taxi systems

In recent years, taxis in multiple cities and metropolitan areas around the world have shifted to utilizing alternative fuel options. Such change has significant potential to reduce environmental externalities and can contribute to alleviating energy policy concerns. However, little work has been conducted to assess the tradeoffs between selecting various fuels for taxis, or to design alternative fuel taxi systems. These tradeoffs exist as a result of the differing costs associated with fleet replacement, infrastructure deployment, operations and maintenance decisions, and costs to users. This paper aims to address this issue by providing an optimization framework for the design of electric taxi systems, and an assessment of optimal costs associated with various options. We focus on comparing the costs of taxi systems made up of gasoline vehicles, hybrid-electric vehicles, plug-in hybrid electric vehicles with AC Level 2 infrastructure, electric vehicles with battery switching infrastructure, and electric vehicles with DC Level 2 fast charging infrastructure. This approach is based on transit systems design methods and focuses on developing an approximate analytic model for electric taxi systems, which can be expanded upon in future research, to address large-scale taxi systems design problems. Scenario results are presented for various city types.