Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet

Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles

Charging Recommender for Electric Taxis

Tato bakalářská práce se zabývá optimalizací strategie řidiče elektrického taxi ve smyslu plánování nabíjení, přibližování se klientům či čekání na klienty na potenciálně výhodných místech. Cílí především na doporučování místa a času nabíjení, ale také jednotlivých rozhodnutí řidiče se snahou maximalizovat jeho zisk. Nejprve jsem se zaměřil na obecný úvod do tématu elektrických vozidel, společně s průzkumem dostupných zdrojů zabývajících se vytvářením strategií pro pohyb nejen elektrických, ale i standardních (se spalovacími motory) taxi. Následně jsem navrhl dvě reprezentace prostředí (mřížková, K-means shlukování) a celý problém definoval pomocí frameworku MDP. Rovněž jsem představil algoritmus založený na dynamickém programování, který generuje navrhovanou sekvenci kroků, jichž by se měl řidič taxi držet. Důležitou částí řešení je rovněž odhad potřebných parametrů kupříkladu pravděpodobnosti vyzvednutí a vysazení zákazníka na konkrétních místech. Toto jsem provedl z rozsáhlého data setu historických cest taxíků. Závěrem jsem navržený algoritmus naimplementoval a experimentálně ukázal jeho výsledky v různých prostředích v porovnání se základním modelem chování řidiče elektrického taxi. Provedené experimenty ukázaly, že má metoda překonává zvolený základní model, jak v aspektu celkového přijmu řidiče, ve vzdálenosti nutné urazit k místu vyzvednutí pasažéra, ale také v efektivitě výběru nabíjecí stanice.This thesis deals with the problem of optimization of an electric taxi driver's strategy in terms of charging, passenger approaching or waiting in potentially favorable locations. It focuses primarily on recommending charging actions and taxi driver's decisions concerning the maximization of the driver's potential profit. Firstly I focused on a general introduction to the topic of electric vehicles together with a research of state of the art in the taxi movement strategy recommending field. I then proposed two concrete environment representations (Grid World, K-Means clustering) and defined the recommending problem as a Markov Decision Problem (MDP). I also presented an algorithm based on dynamic programming generating a policy for an electric taxi driver. An essential part of the presented solution method is an estimation of parameters such as pick-up and destination probability of passenger trips connected with particular locations on a planning map. It was done based on sizeable historical taxi trip data sets. Subsequently, I implemented the proposed algorithm based on a simple principle of dynamic programming. Finally, I experimentally showed the performance of my solution working in different environments compared with a base model of an electric taxi driver behavior. Experiments showed that my algorithm outperforms the base model in several fields, such as a total taxi driver's profit, distance to the next passenger, or charging station choice efficiency

Data-driven analyses of future electric personal mobility

Personal mobility is moving towards the era of electrification. Adopting electric vehicles (EV) is widely regarded as an effective solution to energy crisis and air pollution. Many automakers have announced their roadmap to electrification in the next 1-2 decades. At the same time, limited electric range and insufficient charging infrastructure are still obstacles to EV large-scale adoption. However, with the emerging technologies of ride-hailing, connected vehicles, and autonomous vehicles, these obstacles are being solved effectively, and the EV market penetration is expected to increase significantly. Among the many kinds of electric mobility, electric taxis and personal battery electric vehicles (BEV) especially are gaining increasing popularity and acceptance among customers. This dissertation studies the future challenges of electric taxis and personal BEVs. First, this dissertation examines the BEV feasibility from the spatial-temporal travel patterns of taxis. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs during a year. It is found that taxis with certain characteristics are more suitable for switching to BEVs, such as fewer daily shifts, shorter daily driving distance, and higher likelihood to dwell at the borough of Manhattan. Second, we model and simulate the operations of electric autonomous vehicle (EAV) taxis. EAV taxis are dispatched by the optimization-based model and the neural network-based model. The neural network dispatch model is able to learn the optimal dispatch strategies and runs much faster. The EAV taxis dispatched by the neural network-based model can improve operational efficiency in term of less empty travel distance and smaller fleet size. Third, this dissertation proposes a cumulative prospect theory (CPT) based modeling framework to describe charging behavior of BEV drivers. A BEV mass-market scenario is constructed using 2017 National Household Travel Survey data. By applying the CPT-based charging behavior model, we examine the battery state-of-charge when drivers decide to charge their vehicles, charging timing and locations, and charging power demand profiles under the mass-market scenario. In addition, sensitivity analyses with respect to drivers’ risk attitude and public charger network coverage are conducted

Energy management for electric vehicles in smart cities: a deep learning approach

International audienceWe propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajec-tory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy managemen

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

[EN] The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. 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Electrifying urban ridesourcing fleets at no added cost through efficient use of charging infrastructure

Ridesourcing fleets present an opportunity for rapid uptake of battery electric vehicles (BEVs) but adoption has largely been limited to small pilot projects. Lack of charging infrastructure presents a major barrier to scaling up, but little public information exists on the infrastructure needed to support ridesourcing electrification. With data on ridesourcing trips for New York City and San Francisco, and using agent-based simulations of BEV fleets, we show that given a sparse network of three to four 50 kW chargers per square mile, BEVs can provide the same level of service as internal combustion engine vehicles (ICEVs) at lower cost. This suggests that the cost of charging infrastructure is not a significant barrier to ridesourcing electrification. With coordinated use of charging infrastructure across vehicles, we also find that fleet performance becomes robust to variation in battery range and placement of chargers. Our analysis suggests that mandates on ridesourcing such as the California Clean Miles Standard could achieve electrification without significantly increasing the cost of ridesourcing services