Electric Vehicles: A Data Science Perspective Review
Abstract
:1. Introduction
2. Review Methodology
3. Role and Sources of Data in the Electric Vehicle Domain
- Alternative fuels data center [29] which contains data about EV sales and charging stations for each state of USA;
- Alternative fuel vehicle data [30] which also contains information about alternative fuel vehicles for USA;
- EV volumes [31] that contains informations about world sales of EVs; and
- data.gov [32] which is a search platform for various datasets.
- Nokia HERE API that is used for routing and calculating distances between geographical coordinates with many advanced parameters, similarly to Google Maps API and Open Street Map API;
- Oplaadpalen API that provides information about charging stations around the world same as Open charge map; and
- Vehicle API by edmunds that provides the data about vehicles (e.g., manufacturer or engine type).
4. EV Market: Data for Modelling Economic Factors
4.1. EV Acceptance
4.2. EV Future Sales
5. EV Infrastructure: Data for Modelling Technical Factors
5.1. Batteries
5.2. Charging Stations
5.2.1. Deployment
5.2.2. User Charging Behavior
5.3. Vehicle-To-Grid
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- International Energy Agency. Global EV Outlook 2018; International Energy Agency: Paris, France, 2018. [Google Scholar]
- Babcock, L.R., Jr. A combined pollution index for measurement of total air pollution. J. Air Pollut. Control Assoc. 1970, 20, 653–659. [Google Scholar] [CrossRef]
- Saber, A.Y.; Venayagamoorthy, G.K. Plug-in vehicles and renewable energy sources for cost and emission reductions. IEEE Trans. Ind. Electron. 2011, 58, 1229–1238. [Google Scholar] [CrossRef]
- Granovskii, M.; Dincer, I.; Rosen, M.A. Economic and environmental comparison of conventional, hybrid, electric and hydrogen fuel cell vehicles. J. Power Sources 2006, 159, 1186–1193. [Google Scholar] [CrossRef]
- Nykvist, B.; Nilsson, M. Rapidly falling costs of battery packs for electric vehicles. Nat. Clim. Chang. 2015, 5, 329–332. [Google Scholar] [CrossRef]
- International Energy Agency. Global EV Outlook 2016; International Energy Agency: Paris, France, 2016. [Google Scholar]
- Revolve. Electric Vehicles in the Developing World. 2016. Available online: https://insideevs.com/news/341919/new-study-shows-which-countries-lead-trail-in-electric-car-adoption/ (accessed on 23 December 2018).
- Neubauer, J.; Wood, E. The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility. J. Power Sources 2014, 257, 12–20. [Google Scholar] [CrossRef]
- Rauh, N.; Franke, T.; Krems, J.F. Understanding the impact of electric vehicle driving experience on range anxiety. Hum. Factors 2015, 57, 177–187. [Google Scholar] [CrossRef]
- Hender, B. Recent developments in battery electric vehicles. Proc. Inst. Electr. Eng. 1965, 112, 2297–2308. [Google Scholar] [CrossRef]
- Jeremy, K.; McKellen, P.; Williams, K.; Rees, M.; Pearson, G.; Bird, B.; Harlen, R.; Brown, J.; Winn, R.; Hender, B. Discussion on Recent Developments in Battery Electric Vehicles. Proc. Inst. Electr. Eng. Lond. 1966, 113, 1809. [Google Scholar]
- Golob, T.F.; Kitamura, R.; Bradley, M.; Bunch, D.S. Predicting the market penetration of electric and clean-fuel vehicles. Sci. Total Environ. 1993, 134, 371–381. [Google Scholar] [CrossRef]
- Tanaka, M.; Ida, T.; Murakami, K.; Friedman, L. Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan. Transp. Res. Part A Policy Pract. 2014, 70, 194–209. [Google Scholar] [CrossRef]
- Pevec, D.; Kayser, M.; Babic, J.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Podobnik, V. A Computational Framework for Managing Electric Vehicle Charging Infrastructure. In Proceedings of the 9th international Exergy, Energy and Environment Symposium, Split, Hrvatska, 14–17 May 2017. [Google Scholar]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. Extending parking lots with electricity trading agent functionalities. In Proceedings of the Workshop on Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis (AMEC/TADA 2015)@ AAMAS, Istanbul, Turkey, 4 May 2015. [Google Scholar]
- Buamod, I.; Abdelmoghith, E.; Mouftah, H.T. A review of OSI-based charging standards and eMobility open protocols. In Proceedings of the 2015 6th International Conference on the Network of the Future (NOF), Montreal, QC, Canada, 30 September–2 October 2015; pp. 1–7. [Google Scholar]
- Van Amstel, M.; Ghatikar, R.; Wargers, A. Importance of Open Charge Point Protocol for the Electric Vehicle Industry. Available online: https://www.openchargealliance.org/uploads/files/OCA-EN_whitepaper_OCPP_vs_proprietary_protocols_v1.0.pdf (accessed on 23 December 2018).
- Ruiz, V.; Pfrang, A.; Kriston, A.; Omar, N.; Van den Bossche, P.; Boon-Brett, L. A review of international abuse testing standards and regulations for lithium ion batteries in electric and hybrid electric vehicles. Renew. Sustain. Energy Rev. 2017, 81, 1427–1452. [Google Scholar] [CrossRef]
- Yilmaz, M.; Krein, P.T. Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Trans. Power Electron. 2012, 28, 2151–2169. [Google Scholar] [CrossRef]
- Van Der Aalst, W. Data science in action. In Process Mining; Springer: New York, NY, USA, 2016; pp. 3–23. [Google Scholar]
- Scopus Preview. Available online: https://www.scopus.com/search/form.uri?display=basic (accessed on 23 December 2018).
- IEEE Xplore Digital Library. Available online: https://ieeexplore.ieee.org/Xplore/home.jsp (accessed on 23 December 2018).
- The Exponential Growth of Data. 2017. Available online: https://insidebigdata.com/2017/02/16/the-exponential-growth-of-data/ (accessed on 23 December 2018).
- Bichler, M.; Heinzl, A.; van der Aalst, W.M. Business Analytics and Data Science: Once Again? Bus. Inf. Syst. Eng. 2017, 59, 77–79. [Google Scholar] [CrossRef]
- Your Home for Data Science. Available online: https://www.kaggle.com/ (accessed on 23 December 2018).
- Russell Hensley, S.K.; Pinner, D. Electrifying cars: How three industries will evolve. Mckinsey Q. 2009, 3, 87–96. [Google Scholar]
- Watson, R.T.; Boudreau, M.C.; Chen, A.J. Information systems and environmentally sustainable development: Energy informatics and new directions for the IS community. MIS Q. 2010, 34, 23–38. [Google Scholar] [CrossRef]
- Watson, R.T.; Howells, J.; Boudreau, M.C. Energy Informatics: Initial thoughts on data and process management. In Green Business Process Management; Springer: New York, NY, USA, 2012; pp. 147–159. [Google Scholar]
- Alternative Fueling Station Counts by State. Available online: https://afdc.energy.gov/stations/states (accessed on 23 December 2018).
- U.S. Energy Information Administration-EIA-Independent Statistics and Analysis. Available online: https://www.eia.gov/ (accessed on 23 December 2018).
- Global Plug-in Sales for 2017 Q1 April. Available online: http://e-hike.net/tr/content/global-plug-vehicle-sales-q1-2017 (accessed on 23 December 2018).
- Data.gov. Available online: https://www.data.gov/ (accessed on 23 December 2018).
- NHTS Home Page. Available online: https://nhts.ornl.gov/ (accessed on 23 December 2018).
- ElaadNL. Smart Charging: Leven van de Wind en Rijden op de Zon! Available online: https://www.infiniot.nl/projecten/elaad_leven_van_de_wind_en_rijden_op_de_zon (accessed on 23 December 2018).
- Glerum, A.; Stankovikj, L.; Thémans, M.; Bierlaire, M. Forecasting the demand for electric vehicles: Accounting for attitudes and perceptions. Transp. Sci. 2013, 48, 483–499. [Google Scholar] [CrossRef]
- Pevec, D.; Kayser, M.; Babic, J.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Podobnik, V. A Data-driven Framework for Extending Electric Vehicle Charging Infrastructure. In Proceedings of the Winter Conference on Business Analytics, Snowbird, UT, USA, 2–4 March 2017. [Google Scholar]
- Svendsen, M.; Winther-Jensen, M.; Pedersen, A.B.; Andersen, P.B.; Sørensen, T.M. Electric vehicle data acquisition system. In Proceedings of the 2014 IEEE International Electric Vehicle Conference (IEVC), Florence, Italy, 17–19 December 2014; pp. 1–7. [Google Scholar]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. Evaluating Policies for Parking Lots Handling Electric Vehicles. IEEE Access 2018, 6, 944–961. [Google Scholar] [CrossRef]
- Ketter, W.; Collins, J.; Reddy, P. Power TAC: A competitive economic simulation of the smart grid. Energy Econ. 2013, 39, 262–270. [Google Scholar] [CrossRef]
- Ketter, W.; Peters, M.; Collins, J.; Gupta, A. A Multiagent Competitive Gaming Platform to Address Societal Challenges. MIS Q. 2016, 40, 447–460. [Google Scholar] [CrossRef]
- Hox, J.; Boeije, H. Data collection, primary versus secondary. Encycl. Soc. Meas. 2005, 1, 593. [Google Scholar]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. Modeling Smart Parking Lots through a Discrete-Event Simulation. In Proceedings of the 2016 Winter Conference on Business Intelligence (WCBI), Sandy, UT, USA, 3–5 Mar 2016. [Google Scholar]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. Estimating Profitability of EV-enabled Parking Lots: A Simulation-based Approach. In Proceedings of the Workshop on Information Technology and Systems (WITS), Dublin, Ireland, 15–16 December 2016. [Google Scholar]
- Higgins, A.; Paevere, P.; Gardner, J.; Quezada, G. Combining choice modelling and multi-criteria analysis for technology diffusion: An application to the uptake of electric vehicles. Technol. Forecast. Soc. Chang. 2012, 79, 1399–1412. [Google Scholar] [CrossRef]
- Eggers, F.; Eggers, F. Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model. Technol. Forecast. Soc. Chang. 2011, 78, 51–62. [Google Scholar] [CrossRef]
- Jensen, A.F.; Cherchi, E.; Mabit, S.L.; Ortúzar, J.D. Predicting the potential market for electric vehicles. Transp. Sci. 2016, 51, 427–440. [Google Scholar] [CrossRef]
- Lebeau, K.; Van Mierlo, J.; Lebeau, P.; Mairesse, O.; Macharis, C. The market potential for plug-in hybrid and battery electric vehicles in Flanders: A choice-based conjoint analysis. Transp. Res. Part D Transp. Environ. 2012, 17, 592–597. [Google Scholar] [CrossRef]
- Develder, C.; Sadeghianpourhamami, N.; Strobbe, M.; Refa, N. Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets. In Proceedings of the 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia, 6–9 November 2016; pp. 600–605. [Google Scholar]
- Bikcora, C.; Refa, N.; Verheijen, L.; Weiland, S. Prediction of availability and charging rate at charging stations for electric vehicles. In Proceedings of the 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China, 16–20 October 2016; pp. 1–6. [Google Scholar]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. On the Profitability of the Smart Parking Lots. In Proceedings of the 2015 Winter Conference on Business Intelligence (WCBI), Snowbird, UT, USA, 12–14 March 2015. [Google Scholar]
- Lu, F.; Hua, G. A location-sizing model for electric vehicle charging station deployment based on queuing theory. In Proceedings of the 2015 International Conference on Logistics, Informatics and Service Sciences (LISS), Barcelona, Spain, 27–29 July 2015; pp. 1–5. [Google Scholar]
- Ip, A.; Fong, S.; Liu, E. Optimization for allocating BEV recharging stations in urban areas by using hierarchical clustering. In Proceedings of the 2010 6th International Conference on Advanced Information Management and Service (IMS), Seoul, Korea, 30 November–2 December 2010; pp. 460–465. [Google Scholar]
- Yan, L.; Shen, H.; Li, S.; Huang, Y. Electrical Vehicle Charging Station Deployment based on Real World Vehicle Trace. In Proceedings of the International Conference on Internet of Vehicles, Nadi, Fiji, 7–10 December 2016. [Google Scholar]
- Bingham, C.; Walsh, C.; Carroll, S. Impact of driving characteristics on electric vehicle energy consumption and range. IET Intell. Transp. Syst. 2012, 6, 29–35. [Google Scholar] [CrossRef]
- Carty, S.S. Obama Pushes Electric Cars, Battery Power This Week. 2010. Available online: http://content.usatoday.com/communities/driveon/post/2010/07/obama-pushes-electric-cars-battery-power-this-week-/1 (accessed on 23 December 2018).
- Gohlke, D.; Zhou, Y. Assessment of Light-Duty Plug-In Electric Vehicles in the United States, 2010–2018; Technical Report; Argonne National Lab. (ANL): Argonne, IL, USA, 2019. [Google Scholar]
- Lutsey, N. The Rise of Electric Vehicles: The Second Million. 2017. Available online: https://theicct.org/blogs/staff/second-million-electric-vehicles (accessed on 23 December 2018).
- Randall, T. Driving Tesla’s Model 3 Changes Everything. 2017. Available online: https://www.bloomberg.com/news/articles/2017-07-31/driving-tesla-s-model-3-changes-everything (accessed on 23 December 2018).
- Winton, N. Here’s The Competition For Tesla’s Model 3. 2016. Available online: https://www.forbes.com/sites/neilwinton/2016/03/31/teslas-model-3-will-join-small-group-of-pioneering-battery-powered-cars/ (accessed on 23 December 2018).
- Condliffe, J. Volvo is killing off internal combustion-kinda. Available online: https://www.technologyreview.com/s/608228/volvo-is-killing-off-internal-combustion-kind-of/ (accessed on 23 December 2018).
- Chrisafis, A.; Vaughan, A. France to Ban Sales of Petrol and Diesel Cars by 2040. 2017. Available online: https://www.theguardian.com/business/2017/jul/06/france-ban-petrol-diesel-cars-2040-emmanuel-macron-volvo (accessed on 23 December 2018).
- Condliffe, J. Britain is Getting Rid of the Internal-Combustion Engine. 2017. Available online: https://www.technologyreview.com/s/608347/britain-is-getting-rid-of-the-internal-combustion-engine/ (accessed on 23 December 2018).
- Schmitt, B. German Transport Minister: ICE Ban By 2030 “Utter Nonsense”. 2016. Available online: https://www.forbes.com/sites/bertelschmitt/2016/10/11/german-transport-minister-ice-ban-by-2030-utter-nonsense/ (accessed on 23 December 2018).
- Which Country Will Become the First to Ban Internal Combustion Cars? 2016. Available online: https://www.greentechmedia.com/articles/read/what-country-will-become-the-first-to-ban-internal-combustion-cars (accessed on 23 December 2018).
- Ko, W.; Hahn, T.K. Analysis of consumer preferences for electric vehicles. IEEE Trans. Smart Grid 2013, 4, 437–442. [Google Scholar] [CrossRef]
- Wee, S.; Coffman, M.; La Croix, S. Do electric vehicle incentives matter? Evidence from the 50 US states. Res. Policy 2018, 47, 1601–1610. [Google Scholar] [CrossRef]
- Zhang, Y.; Qian, Z.S.; Sprei, F.; Li, B. The impact of car specifications, prices and incentives for battery electric vehicles in Norway: Choices of heterogeneous consumers. Transp. Res. Part C Emerg. Technol. 2016, 69, 386–401. [Google Scholar] [CrossRef]
- Hidrue, M.K.; Parsons, G.R.; Kempton, W.; Gardner, M.P. Willingness to pay for electric vehicles and their attributes. Resour. Energy Econ. 2011, 33, 686–705. [Google Scholar] [CrossRef] [Green Version]
- Hoen, A.; Koetse, M.J. A choice experiment on alternative fuel vehicle preferences of private car owners in the Netherlands. Transp. Res. Part A Policy Pract. 2014, 61, 199–215. [Google Scholar] [CrossRef]
- Smith, B.; Olaru, D.; Jabeen, F.; Greaves, S. Electric vehicles adoption: Environmental enthusiast bias in discrete choice models. Transp. Res. Part D Transp. Environ. 2017, 51, 290–303. [Google Scholar] [CrossRef]
- Wang, N.; Pan, H.; Zheng, W. Assessment of the incentives on electric vehicle promotion in China. Transp. Res. Part A Policy Pract. 2017, 101, 177–189. [Google Scholar] [CrossRef]
- Anderson, J.E.; Lehne, M.; Hardinghaus, M. What electric vehicle users want: Real-world preferences for public charging infrastructure. Int. J. Sustain. Transp. 2018, 12, 341–352. [Google Scholar] [CrossRef]
- Dorcec, L.; Pevec, D.; Vdovic, H.; Babic, J.; Podobnik, V. How do people value electric vehicle charging service? A gamified survey approach. J. Clean. Prod. 2019, 210, 887–897. [Google Scholar] [CrossRef]
- Wang, S.; Zhang, N.; Li, Z.; Shahidehpour, M. Modeling and impact analysis of large scale V2G electric vehicles on the power grid. In Proceedings of the Innovative Smart Grid Technologies-Asia (ISGT Asia), Tianjin, China, 21–24 May 2012; pp. 1–6. [Google Scholar]
- Helbing, D. Agent-based modeling. In Social Self-Organization; Springer: New York, NY, USA, 2012; pp. 25–70. [Google Scholar]
- Janssen, M.A. Agent-Based Modelling. Available online: http://isecoeco.org/pdf/agent_based%20_modeling.pdf (accessed on 17 October 2019).
- Yang, W.; Zhou, H.; Liu, J.; Dai, S.; Ma, Z.; Liu, Y. Market evolution modeling for electric vehicles based on system dynamics and multi-agents. In Proceedings of the 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), Vienna, Austria, 8–11 September 2015; pp. 133–138. [Google Scholar]
- Sullivan, J.L.; Salmeen, I.; Simon, C. PHEV Marketplace Penetration: An Agent Based Simulation; Technical Report for Transportation Research Institute: Ann Arbor, MI, USA, July 2009. [Google Scholar]
- Shafiei, E.; Thorkelsson, H.; Ásgeirsson, E.I.; Davidsdottir, B.; Raberto, M.; Stefansson, H. An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland. Technol. Forecast. Soc. Chang. 2012, 79, 1638–1653. [Google Scholar] [CrossRef]
- Segal, R. Forecasting the market for electric vehicles in California using conjoint analysis. Energy J. 1995, 16, 89–111. [Google Scholar] [CrossRef]
- Beggs, S.; Cardell, S.; Hausman, J. Assessing the potential demand for electric cars. J. Econom. 1981, 17, 1–19. [Google Scholar] [CrossRef]
- Pentland, A.; Liu, A. Modeling and prediction of human behavior. Neural Comput. 1999, 11, 229–242. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhong, M.; Geng, N.; Jiang, Y. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China. PLoS ONE 2017, 12, e0176729. [Google Scholar] [CrossRef]
- Becker, T.A.; Sidhu, I.; Tenderich, B. Electric Vehicles in the United States: A New Model with Forecasts to 2030; Center for Entrepreneurship and Technology, University of California, Berkeley: Berkeley, CA, USA, 2009; Volume 24. [Google Scholar]
- Du Preez, J.; Witt, S.F. Univariate versus multivariate time series forecasting: An application to international tourism demand. Int. J. Forecast. 2003, 19, 435–451. [Google Scholar] [CrossRef]
- Chayama, M.; Hirata, Y. When univariate model-free time series prediction is better than multivariate. Phys. Lett. A 2016, 380, 2359–2365. [Google Scholar] [CrossRef] [Green Version]
- Li, F.; Dou, C.; Xu, S. Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting. Electronics 2019, 8, 816. [Google Scholar] [CrossRef]
- Neubauer, J.S.; Pesaran, A.; Williams, B.; Ferry, M.; Eyer, J. A Techno-Economic Analysis of PEV Battery Second Use: Repurposed-Battery Selling Price and Commercial and Industrial End-User Value; Technical Report for SAE International: Troy, MI, USA, April 2012. [Google Scholar]
- Neubauer, J.; Pesaran, A.A. NREL’s PHEV/EV Li-Ion Battery Secondary-Use Project; National Renewable Energy Laboratory: Lakewood, CO, USA, 2010. [Google Scholar]
- Ahmadian, A.; Sedghi, M.; Elkamel, A.; Fowler, M.; Golkar, M.A. Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework. Renew. Sustain. Energy Rev. 2018, 81, 2609–2624. [Google Scholar] [CrossRef]
- Battery Cell Production Begins at the Gigafactory. 2017. Available online: https://www.tesla.com/blog/battery-cell-production-begins-gigafactory (accessed on 23 December 2018).
- Boyd, S. Batteries and Electrification R and D Overview; Department of Energy: Washington, DC, USA, 2018. [Google Scholar]
- Dharmakeerthi, C.; Mithulananthan, N.; Saha, T. Planning of electric vehicle charging infrastructure. In Proceedings of the Power and Energy Society General Meeting (PES), Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5. [Google Scholar]
- Nilsson, M.; Habibovic, A. Identifying EV drivers needs for information communication technology to ease the EV charging process. In Proceedings of the Adjunct 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) 2013, Eindhoven, The Netherlands, 28–30 October 2013; pp. 13–16. [Google Scholar]
- Administrator. Navigation. Available online: http://www.evtown.org/about-ev-town/ev-charging/charging-levels.html (accessed on 23 December 2018).
- Miller, J.M.; Onar, O.C.; White, C.; Campbell, S.; Coomer, C.; Seiber, L.; Sepe, R.; Steyerl, A. Demonstrating dynamic wireless charging of an electric vehicle: The benefit of electrochemical capacitor smoothing. IEEE Power Electron. Mag. 2014, 1, 12–24. [Google Scholar] [CrossRef]
- Andrenacci, N.; Ragona, R.; Valenti, G. A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas. Appl. Energy 2016, 182, 39–46. [Google Scholar] [CrossRef]
- Momtazpour, M.; Butler, P.; Hossain, M.S.; Bozchalui, M.C.; Ramakrishnan, N.; Sharma, R. Coordinated clustering algorithms to support charging infrastructure design for electric vehicles. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, Beijing, China, 12 August 2012; pp. 126–133. [Google Scholar]
- Vazifeh, M.M.; Zhang, H.; Santi, P.; Ratti, C. Optimizing the deployment of electric vehicle charging stations using pervasive mobility data. arXiv 2015, arXiv:1511.00615. [Google Scholar] [CrossRef]
- Chen, T.D.; Kockelman, K.M.; Khan, M. The electric vehicle charging station location problem: A parking-based assignment method for Seattle. In Proceedings of the Transportation Research Board 92nd Annual Meeting, Washington, DC, USA, 13–17 January 2013; Volume 340. [Google Scholar]
- Hess, A.; Malandrino, F.; Reinhardt, M.B.; Casetti, C.; Hummel, K.A.; Barceló-Ordinas, J.M. Optimal deployment of charging stations for electric vehicular networks. In Proceedings of the First Workshop on Urban Networking, Nice, France, 10 December 2012; pp. 1–6. [Google Scholar]
- Mehar, S.; Senouci, S.M. An optimization location scheme for electric charging stations. In Proceedings of the 2013 International Conference on Smart Communications in Network Technologies (SaCoNeT), Paris, France, 17–19 June 2013; Volume 1, pp. 1–5. [Google Scholar]
- Sadeghi-Barzani, P.; Rajabi-Ghahnavieh, A.; Kazemi-Karegar, H. Optimal fast charging station placing and sizing. Appl. Energy 2014, 125, 289–299. [Google Scholar] [CrossRef]
- He, F.; Wu, D.; Yin, Y.; Guan, Y. Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Transp. Res. Part B Methodol. 2013, 47, 87–101. [Google Scholar] [CrossRef]
- Frade, I.; Ribeiro, A.; Gonçalves, G.; Antunes, A. Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transp. Res. Rec. 2011, 2252, 91–98. [Google Scholar] [CrossRef]
- Xi, X.; Sioshansi, R.; Marano, V. Simulation–optimization model for location of a public electric vehicle charging infrastructure. Transp. Res. Part D Transp. Environ. 2013, 22, 60–69. [Google Scholar] [CrossRef]
- Xie, F.; Liu, C.; Li, S.; Lin, Z.; Huang, Y. Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles. Transp. Res. Part E Logist. Transp. Rev. 2018, 109, 261–276. [Google Scholar] [CrossRef]
- Sweda, T.; Klabjan, D. An agent-based decision support system for electric vehicle charging infrastructure deployment. In Proceedings of the Vehicle Power and Propulsion Conference (VPPC), Chicago, IL, USA, 6–9 September 2011; pp. 1–5. [Google Scholar]
- Sweda, T.M.; Klabjan, D. Agent-Based Information System for Electric Vehicle Charging Infrastructure Deployment. J. Infrastruct. Syst. 2014, 21, 04014043. [Google Scholar] [CrossRef]
- Botsford, C.; Szczepanek, A. Fast charging vs. slow charging: Pros and cons for the new age of electric vehicles. In Proceedings of the International Battery Hybrid Fuel Cell Electric Vehicle Symposium, Stavanger, Norway, 13–16 May 2009. [Google Scholar]
- Capar, I.; Kuby, M.; Leon, V.J.; Tsai, Y.J. An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations. Eur. J. Oper. Res. 2013, 227, 142–151. [Google Scholar] [CrossRef]
- Pevec, D.; Babic, J.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Ketter, W.; Podobnik, V. Electric Vehicle Range Anxiety: An Obstacle for the Personal Transportation (R) evolution? In Proceedings of the 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 18–21 June 2019; pp. 1–8. [Google Scholar]
- Sathaye, N.; Kelley, S. An approach for the optimal planning of electric vehicle infrastructure for highway corridors. Transp. Res. Part E Logist. Transp. Rev. 2013, 59, 15–33. [Google Scholar] [CrossRef]
- Jia, L.; Hu, Z.; Song, Y.; Zhan, K.; Ding, H. Planning of electric vehicle charging stations in highway network. Autom. Electr. Power Syst. 2015, 39, 82–89. [Google Scholar]
- Wang, G.; Xu, Z.; Wen, F.; Wong, K.P. Traffic-constrained multiobjective planning of electric-vehicle charging stations. IEEE Trans. Power Deliv. 2013, 28, 2363–2372. [Google Scholar] [CrossRef]
- Abdalrahman, A.; Zhuang, W. A survey on PEV charging infrastructure: Impact assessment and planning. Energies 2017, 10, 1650. [Google Scholar] [CrossRef]
- Masoum, A.S.; Deilami, S.; Moses, P.; Masoum, M.; Abu-Siada, A. Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimisation considering voltage regulation. IET Gener. Transm. Distrib. 2011, 5, 877–888. [Google Scholar] [CrossRef]
- Qian, K.; Zhou, C.; Allan, M.; Yuan, Y. Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans. Power Syst. 2011, 26, 802–810. [Google Scholar] [CrossRef]
- Koroleva, K.; Kahlen, M.; Ketter, W.; Rook, L.; Lanz, F. Tamagocar: Using a simulation app to explore price elasticity of demand for electricity of electric vehicle users. In Proceedings of the Thirty Fifth International Conference on Information Systems, Auckland, New Zealand, 14–17 December 2014; pp. 1–8. [Google Scholar]
- Taylor, J.; Maitra, A.; Alexander, M.; Brooks, D.; Duvall, M. Evaluation of the impact of plug-in electric vehicle loading on distribution system operations. In Proceedings of the Power & Energy Society General Meeting (PES’09), Calgary, AB, Canada, 26–30 July 2009; pp. 1–6. [Google Scholar]
- Vyas, A.; Santini, D. Use of national surveys for estimating’full’PHEV potential for oil use reduction. In Proceedings of the PLUG-IN 2008 Conference, San Jose, CA, USA, 21–24 July 2008. [Google Scholar]
- Kelly, J.C.; MacDonald, J.S.; Keoleian, G.A. Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics. Appl. Energy 2012, 94, 395–405. [Google Scholar] [CrossRef]
- Shao, S.; Pipattanasomporn, M.; Rahman, S. Grid integration of electric vehicles and demand response with customer choice. IEEE Trans. Smart Grid 2012, 3, 543–550. [Google Scholar] [CrossRef]
- Franke, T.; Krems, J.F. Understanding charging behaviour of electric vehicle users. Transp. Res. Part F Traffic Psychol. Behav. 2013, 21, 75–89. [Google Scholar] [CrossRef]
- Babic, J.; Carvalho, A.; Ketter, W.; Podobnik, V. Modelling electric vehicle owners’ willingness to pay for a charging service. In Proceedings of the Erasmus Energy Forum, Rotterdam, The Netherlands, 18–19 May 2016; pp. 1–8. [Google Scholar]
- Dorcec, L.; Pevec, D.; Vdovic, H.; Babic, J.; Podobnik, V. Exploring Willingness to Pay for Electric Vehicle Charging with Gamified Survey. In Proceedings of the 3rd International Conference on Smart and Sustainable Technologies (SpliTech 2018), Split, Croatia, 26–29 June 2018. [Google Scholar]
- Clement-Nyns, K.; Haesen, E.; Driesen, J. The impact of vehicle-to-grid on the distribution grid. Electr. Power Syst. Res. 2011, 81, 185–192. [Google Scholar] [CrossRef]
- He, Y.; Venkatesh, B.; Guan, L. Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans. Smart Grid 2012, 3, 1095–1105. [Google Scholar] [CrossRef]
- Soares, J.; Sousa, T.; Morais, H.; Vale, Z.; Canizes, B.; Silva, A. Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid. Appl. Soft Comput. 2013, 13, 4264–4280. [Google Scholar] [CrossRef] [Green Version]
- Kennedy, J. Particle swarm optimization. In Encyclopedia of Machine Learning; Springer: New York, NY, USA, 2011; pp. 760–766. [Google Scholar]
- Kahlen, M.; Ketter, W. Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity Markets. In Proceedings of the AAAI, Austin, TX, USA, 25–30 January 2015; pp. 665–671. [Google Scholar]
- Vytelingum, P.; Voice, T.D.; Ramchurn, S.D.; Rogers, A.; Jennings, N.R. Agent-based micro-storage management for the smart grid. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1. International Foundation for Autonomous Agents and Multiagent Systems, Toronto, ON, Canada, 10–14 May 2010; pp. 39–46. [Google Scholar]
- Kamboj, S.; Decker, K.; Trnka, K.; Pearre, N.; Kern, C.; Kempton, W. Exploring the Formation of Electric Vehicle Coalitions for Vehicle-to-Grid Power Regulation. Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.452.3152&rep=rep1&type=pdf (accessed on 17 October 2019).
- Valogianni, K.; Ketter, W.; Collins, J.; Zhdanov, D. Effective Management of Electric Vehicle Storage Using Smart Charging. In Proceedings of the AAAI, Québec City, QC, Canada, 27–31 July 2014; pp. 472–478. [Google Scholar]
- Ramchurn, S.D.; Vytelingum, P.; Rogers, A.; Jennings, N. Agent-based control for decentralised demand side management in the smart grid. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 1. International Foundation for Autonomous Agents and Multiagent Systems, Taipei, Taiwan, 2–6 May 2011; pp. 5–12. [Google Scholar]
- Mwasilu, F.; Justo, J.J.; Kim, E.K.; Do, T.D.; Jung, J.W. Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev. 2014, 34, 501–516. [Google Scholar] [CrossRef]
- Fleet of 150 Renault ZOE for Smart Solar Charging Project. 2016. Available online: https://media.group.renault.com/global/en-gb/groupe-renault/media/pressreleases/76330/une-flotte-de-150-renault-zoe-pour-un-projet-neerlandais-de-recharge-solaire-intelligente (accessed on 17 October 2019).
- Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart grid—The new and improved power grid: A survey. IEEE Commun. Surv. Tutor. 2012, 14, 944–980. [Google Scholar] [CrossRef]
- Pevec, D.; Babic, J.; Kayser, M.A.; Carvalho, A.; Ghiassi-Farrokhfal, Y.; Podobnik, V. A Data-Driven Statistical Approach for Extending Electric Vehicle Charging Infrastructure. Int. J. Energy Res. 2018, 2018, 3102–3120. [Google Scholar] [CrossRef]
Data Acquiring Method | Example | Public | Tariff Model | Research Example | |||
---|---|---|---|---|---|---|---|
Free | Paid | Trial | Other | ||||
Data Repositories | Alternative Fuels Data Centre | ✓ | ✓ | [42,43] | |||
Alternative Fuel Vehicle Data | ✓ | ✓ | |||||
City of Melbourne open data platform | ✓ | ✓ | |||||
data.gov | ✓ | ✓ | |||||
EV-Volumes | ✓ | ✓ | ✓ | ||||
API | Nokia HERE | ✓ | ✓ | ✓ | [14,36] | ||
Google Maps | ✓ | ✓ | ✓ | ||||
Oplaadpalen | ✓ | ✓ | |||||
Open Street Maps | ✓ | ✓ | |||||
puni.hr | ✓ | ||||||
Vehicle API | ✓ | ✓ | ✓ | ||||
plugshare.com | ✓ | ✓ | ✓ | ||||
openchargemap.org | ✓ | ✓ | ✓ | ||||
Survey | National Household Travel Survey | ✓ | ✓ | [44,45,46,47] | |||
Qualitrics | ✓ | ✓ | ✓ | ||||
Companies | ElaadNL | ✓ | [14,35,48,49] | ||||
Renault | ✓ | ||||||
Smart ED Platform | ✓ | ||||||
Simulations | Agent-based | ✓ | [15,39,50,51,52,53] | ||||
Mathematical | ✓ | ||||||
Manual | Aggregation from multiple existing sources | ✓ | [14,36,54] | ||||
New data collecting system | ✓ |
State | Year | All Factors | Most Important Factors | Paper |
---|---|---|---|---|
Korea | 2009 | battery price, holding tax, subsidies type, subsidies level, battery swappability, and availability of recharging infrastructure | swappable battery and availability of recharging infrastructure | [65] |
Norway | 2011, 2012, 2013 | price, technology, incentives (i.e., bus lane access, toll waiver, and charging station density) | technology | [67] |
US | 2018, 2012, 2009 | price, driving range, time to charge, acceleration, pollution, fuel cost, alternative fuel availability, and home plug-in construction fee | price reduction | [13,66,68,73] |
Netherlands | 2011 | car type, price, monthly cost, driving range, recharge/refuelling type, additional detour time to reach a fuel or charging station, number of available models, and policy measure | low range, long refueling times | [69] |
Japan | 2012 | price, fuel cost, driving range, emission reduction, alternative fuel availability, and home plug-in construction fee | availability of charging stations | [13] |
Australia | 2017 | environmental concerns, technology, range, charging infrastructure | environmental concerns, technology | [70] |
Croatia | 2019 | range, battery capacity, pricing of the charging service | pricing of the charging service | [73] |
China | 2017 | environmental concerns, technology, education, charging infrastructure density, driving restriction | charging infrastructure density, licence fee | [74] |
Germany | 2018 | environmental concerns, number of charging stations, charging station speed | availability of charging stations | [72] |
State | Forecasting Factors | Forecast Year | Observation | Research Method | Research |
---|---|---|---|---|---|
China | Dependency of decrease of the number of traditional cars on increase of EVs | 2030 | Developed regions 80–90% EV market penetration, underdeveloped regions 30% market penetration | agent-based modelling | [77] |
Data from sales (i.e., from 2011 to 2015) | 2020 | 1 million EVs sold | univariate and multivariate time series modelling | [83] | |
United States | Subsidies | 2020 | EV sales 4–5%, EV fleet share 2% | agent-based modelling | [78] |
Gasoline prices, traditional car cost | 2030 | EV sales 64%, EV fleet share 24% | Bass diffusion | [84] | |
Iceland | Marketing and word of mouth | 2040 | 70–100% EV adoption, based on the prices of gasoline and EVs | agent-based modelling | [79] |
Switzerland | Choice between predefined cars | none | Changes in market influenced by EV cost, subsidies, and monthly cost of ownership | conjoint analysis | [35] |
Belgium | Car utilities | 2020 | 13% EV market share | conjoint analysis | [47] |
2030 | 45% EV market share | ||||
Norway, Netherlands, and Denmark | Survey before and after driving a EV | 2020 | 40% EV market share | conjoint analysis | [46] |
Australia | Prices of electricity, income, and subsidies | 2030 | 45% EV market share | conjoint analysis and Bass diffusion | [44] |
EV Data | Method | Algorithm | Research |
---|---|---|---|
Yes | Machine Learning | XGBoost, Clustering | [36] |
Clustering | [97,98] | ||
Optimization | Greedy, Genetic | [99] | |
Mathematical programming | [52,53,100] | ||
No | Optimization | Genetic | [101,102,103] |
Mathematical programming | [104,105,106,107] | ||
Simulation | Queuing theory | [51] | |
Agent-based modelling | [108,109] |
Observed Dependency on Charging Behavior | Observed Behavior | Peak Hours | Research |
---|---|---|---|
Grid load | uncontrolled domestic charging, uncontrolled off-peak domestic charging, smart domestic charging, and uncontrolled public charging | none | [118] |
Traveling distance | The longer the traveling time the earlier the departure is | 5 p.m. | [120] |
Load on the energy grid caused by EVs is never 0, households with greater income cause greater load, older population generates peak earlier than younger population | 8 p.m. | [122] | |
Cars are parked more than 90% of times | 6 p.m. | [123] | |
Charging times | The charging pattern differs between weekdays and weekends, classification of charging session as: charging near home, charging near work, and park to charge | none | [48] |
Two peaks in charging utilization, in the morning charge near work, and in the evening charge near home. Drop in charging utilization during the summer | 8 a.m. and 5 p.m. | [14] | |
Driving pattern and battery state-of-charge | Extended driving distance can be achieved with reducing the amount of accelerating and de-accelerating | none | [54] |
Traveling distance and driving duration | On average users charge 3.1 times a week when the remaining battery capacity is under 30% or under 15% | none | [124] |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Pevec, D.; Babic, J.; Podobnik, V. Electric Vehicles: A Data Science Perspective Review. Electronics 2019, 8, 1190. https://doi.org/10.3390/electronics8101190
Pevec D, Babic J, Podobnik V. Electric Vehicles: A Data Science Perspective Review. Electronics. 2019; 8(10):1190. https://doi.org/10.3390/electronics8101190
Chicago/Turabian StylePevec, Dario, Jurica Babic, and Vedran Podobnik. 2019. "Electric Vehicles: A Data Science Perspective Review" Electronics 8, no. 10: 1190. https://doi.org/10.3390/electronics8101190
APA StylePevec, D., Babic, J., & Podobnik, V. (2019). Electric Vehicles: A Data Science Perspective Review. Electronics, 8(10), 1190. https://doi.org/10.3390/electronics8101190