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Charging Infrastructure Optimisation for Electric Taxis

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Charging Infrastructure Optimisation for Electric Taxis Technische Universität München Department of Electrical and Computer Engineering Institute for Renewable and Sustainable Energy Systems Charging infrastructure optimisation for electric taxis Reinhard Sellmair Vollständiger Abdruck der von der Fakultät für Elektrotechnik und Informationstechnik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktor-Ingenieurs (Dr.-Ing.) genehmigte Dissertation. Vorsitzender: Prof. Dr.-Ing. Ulf Schlichtmann Prüfende der Dissertation: 1. Prof. Dr. rer. nat. Thomas Hamacher 2. Prof. Dr.-Ing. Fritz Busch Die Dissertation wurde am 29.11.2017 bei der Technischen Universität München eingereicht und durch die Fakultät der Elektrotechnik und Informationstechnik am 24.07.2018 angenommen. Acknowledgement I entirely carried out this thesis as Research Associate of TUMCREATE in Singapore. This work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) pro- gramme. I want to express my gratitude for giving me the chance of being part of this project and supporting me in my research. I want to thank Prof. Dr. Thomas Hamacher for giving me the opportunity to write this thesis under his supervision. I sincerely appreciated his support, feedback and the freedom he gave me throughout the years to elaborate on this topic. Furthermore, I want to thank Prof. Dr. Fritz Busch for our discussions and taking over the role of co-supervisor. Especially I want to thank Tony Heng, Shaun Lee, Angela Khew, and Bernard Tan from SMRT Taxis. It would not have been possible to carry out this thesis without their support and the data they gave me access to. Our numerous discussions gave me invaluable insights into the perspective of a taxi operator. Furthermore, I’m thanking Robert Kochhan for his excellent support throughout our collaboration with SMRT. Without his expertise in economics I would not have been able to elaborate on this topic in the same depth. Among the many colleagues and friends at TUMCREATE from whom I got the chance to learn from I want to highlight Tom Schelo, David Ciechanowicz, Marc Gallet, and Sascha Moecker. Sharing their knowledge with me broadened my expertise and as a consequence improved the quality of this thesis. Most of all I want to thank my wife Yün Yün and my parents for their everlasting encouragement and support throughout my journey. i Abstract Replacing conventional taxis with electric vehicles would be an efficient measure to reduce CO2 emissions in the transportation sector. The high daily mileage of taxis offers the chance to compensate the higher purchasing price of electric vehicles by lower energy costs. However, due to their limited range and long charging times, covering high mileage still poses a challenge on today’s electric vehicles. To tackle this challenge and at the same time, to offer a convenient solution to the drivers, a customised charging infrastructure is vital to make electric taxis an attractive alternative. This thesis proposes a method to optimise a charging infrastructure for electric taxis and applies this method in the context of Singapore. Therefore, a detailed driving profile analysis of almost 3,000 taxis was conducted. Furthermore, vehicle energy models were developed to simulate the energy consumption and charging process of electric vehicles. The aim of the infrastructure optimisation is to maximise the economic profit of both, the taxi drivers and a charging infrastructure provider while taking today’s taxi drivers’ activity patterns and break location choices into account. Therefore, a bi-level simulation-optimisation approach was chosen. For this purpose, an agent-based taxi driving profile simulation was developed which is capable of reproducing today’s taxis’ driving patterns while respecting electric vehicle restrictions regarding range and charging time. This simulation model was validated with respect to the recorded driving profiles. To quantify the economic potential of electric taxis, Total Cost of Ownership (TCO) models were applied to assess electric vehicles and charging stations. In order to take economic considerations into account for the charging infrastructure optimisation, the objective function’s parameters were derived from the TCO models. The objective of the optimisation is to minimise the opportunity cost from detours and waiting times as well as charging infrastructure costs. These costs are minimised by selecting locations where charging stations shall be placed and assigning the ideal number of charging stations to each location. This problem is described as the multiple server location problem. The optimisation results showed that today’s electric vehicles can reach almost the same mileage as conventional taxis. Thereby, electric taxi drivers would not have to adjust their activity patterns by changing the time of their breaks or even work longer. Lower energy costs of electric vehicles compensate for the lower achievable revenue so that the economic potential of electric and conventional taxis is almost identical. Upcoming electric vehicles with longer range and shorter charging times are expected to exceed the economic potential of today’s conventional taxis. ii Kurzfassung Das Ersetzen von konventionellen Taxis durch Elektroautos wäre eine effiziente Maßnah- me um CO2 Emissionen im Verkehrssektor einzusparen. Die hohe tägliche Laufleistung von Taxis bietet die Möglichkeit höhere Kaufpreise von Elektroautos durch geringere Energiekosten zu kompensieren. Aufgrund der begrenzten Reichweite und langen Lade- zeiten stellt das Erreichen einer hohen täglichen Laufleistung nach wie vor eine Heraus- forderung für heutige Elektroautos dar. Um diese Herausforderung zu bewältigen und gleichzeitig Taxifahrern eine komfortable Lösung anbieten zu können ist eine speziell für diesen Anwendungsfall ausgelegte Ladeinfrastruktur essentiell. Nur unter dieser Bedin- gung können Elektrotaxis eine attraktive Alternative zu konventionellen Taxis darstellen. Diese Arbeit schlägt eine Methodik vor um eine Ladeinfrastruktur für Elektrota- xis zu optimieren und wendet diese Methodik für Taxis in Singapur an. Dafür wurde eine detaillierte Analyse der Fahrprofile von nahezu 3.000 Taxis durchgeführt. Außer- dem wurden Energiemodelle entwickelt um den Energieverbrauch und den Ladevorgang von Elektroautos zu simulieren. Das Ziel der Infrastrukturoptimierung ist es den wirt- schaftlichen Gewinn von sowohl Taxifahrern als auch einem Ladeinfrastrukturbetreiber zu maximieren. Gleichzeitig werden hierbei die Bewegungsprofile und die Auswahl an Or- ten um Pause zu machen von Taxifahrern berücksichtigt. Hierfür wurde ein zweistufiger Simulation-Optimierungsansatz gewählt. Für diesen Zweck wurde eine agentenbasierte Fahrprofilsimulation für Taxis entwi- ckelt die sowohl Fahrprofile heutiger Taxis reproduziert und gleichzeitig Einschränkun- gen von Elektroautos bezüglich Reichweite und Ladezeiten berücksichtigt. Dieses Modell wurde anhand der aufgezeichneten Fahrprofile validiert. Des Weiteren, wurden Gesamtwirtschaftlichkeitsmodelle angewandt um das wirt- schaftliche Potenzial von Elektrofahrzeugen und Ladestationen zu quantifizieren. Um wirtschaftliche Faktoren in der Ladeinfrastrukturoptimierung zu berücksichtigen wurden Parameter von den Wirtschaftlichkeitsmodellen abgeleitet und in die Zielfunktion der Optimierung integriert. Das Ziel der Optimierung ist es Opportunitätskosten von Umwegen um Ladestationen zu erreichen, Wartezeiten auf eine verfügbare Ladestation und Ladeinfrastrukturkosten zu minimieren. Hierfür werden sowohl optimale Orte für das Aufstellen von Ladestatio- nen ausgewählt als auch die optimale Anzahl an Ladestationen an jedem dieser Orte identifiziert. Dieses Optimierungsproblem ist beschrieben als ein multiple server location problem. Die Optimierungsergebnisse zeigten, dass Fahrer von heutigen Elektroautos annä- hernd die gleiche Laufleistung und Einnahmen von Fahrern mit konventionellen Taxis erreichen könnten. Dabei müssten Elektrotaxifahrer weder länger arbeiten noch ihre ge- wöhnlichen Pausenzeiten anpassen. iii iv Die geringeren Energiekosten von Elektroautos gleichen die etwas geringeren Ein- nahmen aus, sodass das wirtschaftliche Potenzial von konventionellen und Elektrotaxis nahezu identisch ist. Es ist zu erwarten, dass in naher Zukunft Elektrofahrzeuge mit höheren Reichweiten und geringeren Ladezeiten das wirtschaftliche Potenzial konventio- neller Taxis übertreffen werden. Contents 1 Introduction 1 1.1 Motivation . 1 1.2 Aim . 2 1.3 Approach and structure . 2 1.4 Contribution . 5 2 Context and data background 6 2.1 Taxis in Singapore . 6 2.2 Electric vehicles . 7 2.3 Charging infrastructure . 9 2.4 Driving profile data . 11 2.4.1 Logger data . 11 2.4.2 Status data . 12 2.4.3 Revenue data . 13 2.5 Street network . 13 3 Driving profile analysis 17 3.1 Pre-processing and merging of data sets . 17 3.2 Standstill periods . 19 3.2.1 Detection . 19 3.2.2 Classification . 19 3.2.3 Location clustering . 23 3.2.4 Shift schedules . 24 3.3 Trips . 25 3.3.1 Status and classification . 25 3.3.2 Mileage and revenue estimation . 27 3.3.3 Map-matching . 29 3.4 Traffic statistics . 30 3.4.1 Choice of street types . 31 3.4.2 Speed profiles . 31 3.4.3 Acceleration . 34 3.4.4 Driving shares . 35 4 Vehicle energy model 37 4.1 Energy consumption model . 37 4.1.1 Model and parameter . 38 4.1.2 Speed-based approach . 41 v CONTENTS vi 4.1.3 Route-based approach . 43 4.2 Charging model . 45 4.2.1 Charging curve . 46 4.2.2 Model and parameter . 46 4.2.3
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