SHORT PAPER GET2020
SHARED AUTONOMOUS AND ELECTRIC VEHICLES: ENVIRONMENTAL IMPACTS ASSESSMENT THROUGHOUT A LIFE CYCLE CONCEPT
M. VILAÇA a, G. CORREIA b and M.C. COELHO a
a Department of Mechanical Engineering/ Center for Mechanical Engineering and Automation,
University of Aveiro
Email: {mvilaca; margarida.coelho}@ua.pt
b Department of Transport & Planning, Faculty of Civil Engineering and Geosciences
Delft University of Technology Email: g.correia@tudelft.nl
Keywords: Shared Autonomous Electric Vehicles (SAEVs); Life Cycle Assessment (LCA) Subject area: Innovative technologies for smart cities; Future Mobility
1. INTRODUCTION
Autonomous electric vehicle technology has the long-term potential to truly disrupt the current mobility paradigm and combined with shared mobility it is assumed to play a significant role in transport systems in the foreseeable future. Although the convergence of automation, electrification and shared mobility seems natural, little is known about the environmental impact of this combination, especially considering a life cycle concept.
The use of electricity in the transport sector is promising for having the reduction potential on climate change and air quality impacts, powertrain efficiency and maintenance requirements (Ellingsen, Singh, & Strømman, 2016; Hawkins, Singh, Majeau-Bettez, & Strømman, 2013; IEA, 2019; Kukreja, 2018). However, from a total life cycle perspective, the main determinant of the environmental impact of electric vehicles (EVs) are largely dependent on different factors, such as electrical power structure and battery manufacturing (Hawkins et al., 2013; Tagliaferri et al., 2016). Research on connected and autonomous vehicles (CAVs) is expanding with the increased interest towards high levels of vehicle automation and their impact on mobility (Correia, Milakis, van Arem, & Hoogendoorn, 2016; Fagnant & Kockelman, 2014). Connectivity and automation can generate different vehicle consumption patterns which raises uncertainties regarding the potential of CAVS energy savings. It is recognized that CAVS yield significant number of potential benefits in safety, reduction of congestion, travel time, energy efficiency and parking space (Todd Litman, 2019). However, their net effect on energy consumption is unclear due to the potential increase in travel demand and attraction of new user groups (Fleming & Singer, 2019; Ross & Guhathakurta, 2017; Wadud, MacKenzie, & Leiby, 2016). Additionally, shared mobility is directly related with less congestion, less energy used per kilometre driven and lower overall emission since the fleet of cars can reduce (Baptista, Melo, & Rolim, 2014; Becker, Ciari, & Axhausen, 2018; Nijland & van Meerkerk, 2017). A future of shared autonomous and electric vehicles (SAEVs) it is expected to represent a natural synergy. Hence, apart from the integrated and holistic approach, the environmental impact of the combination of these technologies must also be considered through a full life cycle perspective.
A life cycle assessment (LCA) is a technique used to identify and quantify the direct and indirect environmental and health-related impacts throughout all stages of product life, usually referred to “cradle-to-grave” or “cradle-to-cradle” analysis (ISO 14040:2006, 2006; Moro & Helmers, 2017). It allows a true assessment of environmental aspects in different stages, generically, production, use and end-of-life phases. Furthermore, the basic elements of an LCA includes the evaluation of the potential environmental impacts related to raw materials used and the energy used.
The purpose of this short paper is to present the project “Driving2Driverless: Urban and regional transport management under a scenario of shared electric fully automated mobility”, namely the component dedicated to the evaluation of environmental and health-related impacts of an SAEV through a Life Cycle Assessment (LCA) methodology. Special attention is given to the impact of the sensing and computing subsystems that allows a level 4 of automation.
2. METHODOLOGY
Driving2Driveless project intends to study the challenges of shared electric and automated mobility in different systems components where the sustainability of the transport system is the main evaluator. The particular goal of this research is to identify the main environmental impacts of SAEVs taking into account sensing and computing subsystems that allow a level 4 of automation through a LCA approach.
An attributional LCA is developed to assess the environmental performance of SAEVs considering a cradle-to-grave perspective. The functional unit considered was 300,000 km, considering the estimation of a shared passenger vehicle driven over its lifetime (Farhan & Chen, 2018). The system boundary covers the life cycle stages of a production phase (including manufacturing and assembly), use phase, and disposal scenario (Fig. 1).
Figure 1 - System boundary and vehicle components of a SAEV life cycle
Data of vehicle components and battery material were obtained from Ecoinvent database (version 3.6) (Wernet et al., 2016). A compact-size passenger EV (approximately 1200 kg) equipped with lithium-ion battery is considered.
A level 4 of automation is defined as a vehicle that self-driving and monitor the driving environment within defined use cases where the human can be involved. Sensing and computing subsystems composition of a level 4 CAV were obtain based on Gawron et al.(2018).
Life Cycle Impact Assessment (LCIA) were carried out using SimaPro LCA software package (version 9.1) (PréConsultants, 2019). Quantification of environmental indicators and potential impacts are based on ReCiPe midpoint method (considering a hierarchical (H) perspective) (Huijbregts et al., 2016). According with this method, environmental indicators were obtained in terms of impact categories. In addition to global warming potential (GWP), ozone depletion, acidification, eutrophication, toxicity to humans, and particulate matter formation were considered relevant indicators for a more comprehensively analyses. A comparison analysis is developed between EV and SAEV, considering 0.199kWh/km and 3.97kWh/km the energy consumption of each typology, respectively (Burchart-korol et al., 2018; Zhang, Yang, Ke, Liu, & Yuan, 2019). The electricity power structure of Portugal is considered. 3. CONCLUSION
The results of Driving2Driveless project will allow the identification of good practices relative to the definition, dimensioning and design of electric and automated car sharing systems and answering the question as to whether these systems are able to make urban mobility more sustainable. The particular result of this piece of research intends to identify the environmental impact of SAEVs and the particular contributor phases during the life cycle of a vehicle. Moreover, the identification of the key factors of environmental impact considering a vehicle life cycle perspective is going to add value to stakeholders (industry, transport service companies, users or public transport operators) to meet this challenge.
ACKNOWLEDGEMENTS
The authors acknowledge to the following projects: UID/EMS/00481/2019-FCT - Fundação para a Ciência e a Tecnologia (FCT); CENTRO-01-0145-FEDER-022083 - Centro2020 Regional Operational Programme, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund; MobiWise (P2020 SAICTPAC/0011/2015, cofounded by COMPETE2020, Portugal2020 – Operational Program for Competitiveness and Internationalization, European Union’s ERDF and FCT); DICA-VE (POCI-01-0145-FEDER-029463) and Driving2Driveless (POCI-01-0145-FEDER-031923) projects, funded by FEDER through COMPETE2020, and by national funds (OE), through FCT/MCTES.
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