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Yushimito, W.F.; Moreno, S.; Miranda, D. Fleet Conversion Feasibility of Battery Electric Taxis. Encyclopedia. Available online: https://encyclopedia.pub/entry/46549 (accessed on 17 June 2024).
Yushimito WF, Moreno S, Miranda D. Fleet Conversion Feasibility of Battery Electric Taxis. Encyclopedia. Available at: https://encyclopedia.pub/entry/46549. Accessed June 17, 2024.
Yushimito, Wilfredo F., Sebastian Moreno, Daniela Miranda. "Fleet Conversion Feasibility of Battery Electric Taxis" Encyclopedia, https://encyclopedia.pub/entry/46549 (accessed June 17, 2024).
Yushimito, W.F., Moreno, S., & Miranda, D. (2023, July 07). Fleet Conversion Feasibility of Battery Electric Taxis. In Encyclopedia. https://encyclopedia.pub/entry/46549
Yushimito, Wilfredo F., et al. "Fleet Conversion Feasibility of Battery Electric Taxis." Encyclopedia. Web. 07 July, 2023.
Fleet Conversion Feasibility of Battery Electric Taxis
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Given the semi-private nature of the mode, the conversion of taxi vehicles to electric requires a feasibility analysis, as it can impact their operations and revenues.

battery electric vehicles taxis feasibility charging stations

1. Introduction

The continuous growth of population and industries has resulted in an increase in global CO2 levels. One of the major industries contributing to this increase is the transportation industry. In Chile, the transportation sector is the second-largest sector responsible for CO2 emissions [1], which represented more than a third of the total emissions in 2019 [2]. This increase is mainly due to the rapid growth of the country’s automotive industry. According to [3], between 2010 and 2021, the number of vehicles continuously increased at an average rate of 6.1% per year.
This increase is observed in both private transportation and public transportation sectors. Between 2018 and 2020, there was an approximate average of 210,000 vehicles, including taxis, buses, and minibuses, with taxis accounting for approximately 50% of the total public transportation vehicles [4]. For this reason, in 2017, the Chilean government, with the aim of curtailing greenhouse gas emissions and CO2 emissions, adopted an electromobility strategy that seeks to attain 100% electric vehicles for urban public transportation by 2050 [5].
The goal is challenging as there are multiple factors influencing the adoption of electric vehicles in public transportation, especially taxis. According to a survey conducted in Shenzhen and Guangzhou, involving over 725 taxi drivers, Reference [6] found that the main adoption factors in taxi drivers are similar to those expressed by private car owners. For private car owners, References [7][8][9][10] showed that while drivers are becoming more confident with electric vehicles, the most relevant characteristics in their purchasing decisions are the purchase prices, fuel economy, and the battery’s driving range (maximum distance that an electric vehicle can travel with one charge).
For taxi drivers, there are other important factors that influence their decisions to replace their conventional gasoline vehicles (CGV) with battery electric vehicles (BEVs), which are even more difficult than for private car owners. These aspects include the locations of recharging stations [11][12][13], the number of recharging points compared to available gas stations [11], the possibility of parking, fast-charge stations [11], and the cost of the vehicle [7][8][9][14]. In addition, another key factor for taxi drivers is performance expectancy [6]. Reference [15] defined performance expectancy as “the degree to which an individual believes that using new technology can improve his/her job performance”. For taxis, this generates the question of whether a fleet of BEV taxis would have the capacity to meet the demands, considering the current battery ranges and recharging times. For the former, this is important, as CGVs do not have problems completing day-trip distances of 200–400 km (typical values for a taxi in a day), but for a BEV, this distance poses a challenge due to the limitations of their current driving ranges, as shown in [6]. The latter is relevant, considering that charging times for an electric vehicle with a 30 kW battery can vary between 8 min and 14 h, depending on the type of recharge [5].

2. Feasibility of BEVs for Private Use

For private-use vehicles, Reference [16] computed the daily vehicle miles traveled (DVMT) to infer the suitability of replacing gasoline vehicles with electric vehicles in Atlanta, USA. To perform this evaluation, Reference [16] assumed that drivers would not change their driving patterns, which implies that they would charge once a day, typically overnight while at home. With that, they computed the market by identifying the proportion of the 484 vehicles analyzed that could meet their daily range of travel needs. They found that a small proportion of vehicles exceeded a range of 100 miles (161 km) per day, thus pointing out that the market for electric vehicles, at least for private use, is feasible. Similarly, for private-use vehicles, Reference [17] used GPS data to assess the feasibility of electric vehicles in households in Seattle, USA. They found that a typical household in this city drives 23 miles (≈37 km) per day, meaning that electric vehicles with 100-mile battery ranges satisfy the trip-distance needs. Reference [18] also used five weeks of GPS data to analyze home-to-home tour distances in Australia, aiming to assess the feasibility of making these trips using electric vehicles. Their results align with previous studies showing that a large percentage (90%) of day-to-day driving can be accommodated because they involve short-range distance trips; while trips over 170 km would require a recharge. Notice that all of these studies are focused on privately owned vehicles used for home-based trips, and it is assumed that the recharging process occurs at night or while they are parked.
A more recent study conducted by [19] collected data (GPS travel survey data) from November 2004 to April 2006 in the Seattle metropolitan area. The study aimed to estimate the feasibility of electric vehicles based on the reliability of the battery range estimation. A driver is presented with two options for his daily trip: a CGV and a BEV. The feasibility is then calculated as the number of days that the driver replaces a CGV with a BEV, considering that for the BEV, a battery range is estimated. Their numerical results also show that to increment the feasibility of the usage of the BEV, an increase in the mean and a reduction in the standard deviation of the BEV range distribution is required.
Moreover, related to the driving range, Reference [20] conducted a daily driving usage study from a dataset comprising information on more than 1000 vehicles in Europe. An analysis on the distances and trip durations of the journeys, as well as the energy consumed and idle time analysis, were performed. Their analysis showed that, in terms of driving range, a hybrid electric vehicle can cover the daily needs of the sample as the majority of the urban trips comprise less than 50 km trips. However, they found that longer trips cannot recover energy due to short idle times and that autonomy (driving range) would need to increase, up to 400 km.

3. Feasibility of BEVs for Taxis

As can be observed from the recollection of studies in the previous section, the battery range (or battery autonomy) is an important aspect in the decision for the adoption of BEVs for private use. However, current surveys on taxi drivers show that the battery range is just one of many important factors. For instance, Reference [6] conducted a survey in Shenzhen and Guangzhou (both in China), comprising 725 respondents, to identify the factors driving the acceptance of electric vehicles by taxi drivers in these cities.
They found that the factors influencing the adoption are performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, vehicle price value, habit, and satisfaction with incentive policies. These findings are similar to those found for private car users by [8][10][14][21]. Reference [21] studied the effects of the battery range as well as battery depletion using household questionnaire data gathered in Nagoya, Japan. From the data, they developed a probabilistic distribution (Weibull and log-normal) of daily travel distances. They found that anxiety is reduced when drivers have charging stations available in places where they tend to park for long periods (large-scale retail facilities, workplaces, expressway rest areas); this reduces the range desire for electric vehicles. They also modeled the response to the probability of battery depletion and found that the respondents had low expectations that the battery would be depleted based on their travel distance expectations. Thus, according to the authors, battery depletion will not play a significant role in the purchasing decisions of BEVs.
Reference [10] conducted a stated preference survey consisting of 996 respondents in Italy and 938 respondents in Slovenia; the authors found that the vehicle price, driving range, and environmental awareness are key in consumers’ decisions to adopt BEVs in both countries. Reference [9] conducted a survey in 30 provinces in China, involving 1021 respondents, to determine the impact of purchasing subsidies on the decision to adopt electric vehicles. Their results indicate that cost concerns of BEVs in China can be compensated by subsidies. However, respondents were more concerned with the operational aspects of the BEVs, such as cruising power or the availability of charging facilities.
Reference [8] conducted a stated preference survey in Italy, confirming that vehicle attributes, such as purchase price, fuel economy, and driving range, play very relevant roles. These results show new factors in comparison to a previous study conducted in Italy, where fast-charging networks, driving ranges, and financial incentives were identified as the primary factors influencing the purchasing decisions of electric vehicles [14].
Another recent behavioral study was conducted in Hong Kong by [22]; the authors collected 250 surveys from taxi drivers. They found similar factors affecting the adoption of BEVs, such as daily driving distance, driving range per full charge, and charging time. They also pointed out the importance of a network of charging stations and power infrastructure.
Reference [23] conducted a feasibility analysis on the conversion of current vehicles used as“taxi colectivos” (shared taxis with fixed routes and no timetables) in Santiago de Chile. The study explored the installation of batteries that could be charged using the current electric grid or through a solar recharging station at the beginning of the taxi routes. They found that the most beneficial effect was obtained when the batteries were charged with a solar grid; however, this solution is still unreliable due to solar intermittence. The study assumed that a “taxi colectivo” trip in Santiago could be completed with a fully charged battery, which might be an acceptable assumption only if the route “taxi colectivo” is short. In another study, Reference [24] addressed the feasibility of replacing taxis with BEVs in Seoul, South Korea, using a cost–benefit analysis. Their study included the cost assessment of vehicles and infrastructure while the benefits were computed by means of a traffic assignment. Reference [25] assessed the feasibility of replacing taxis by using real-world driving data and battery simulations in Canada. A life battery simulation appears reasonable in this context, considering that cold weather affects the duration of the battery. They considered two scenarios, one with one shift that charged overnight, and the other with two shifts, with two charges at the beginning of each shift. In both cases, the losses due to lost trips were small, showing that the losses for taxis would be minimal if replaced by BEVs.

References

  1. IEA. Energy Policies Beyond IEA Countries: Chile 2018 Review; IEA: Paris, France, 2018.
  2. CNE. Anuario Estadístico de Energía. 2020. Available online: https://www.cne.cl/wp-content/uploads/2021/12/AnuarioCNE2020.pdf (accessed on 5 January 2022).
  3. CEIC. Chile Motor Vehicles Sales Growth, 2010–2021. 2022. Available online: https://www.ceicdata.com/en/indicator/chile/motor-vehicles-sales-growth (accessed on 5 January 2022).
  4. INE. Base de Datos de Permisos de Circulación. 2021. Available online: https://www.ine.gob.cl/docs/default-source/parque-de-vehiculos/bbdd/2021/base-de-datos-permisos-de-circulación.accdb?sfvrsn=dba78113_5&download=true (accessed on 5 January 2022).
  5. Ministerio de Energía. Estrategia Nacional de Electro-Movilidad. 2020. Available online: https://energia.gob.cl/sites/default/files/estrategia-nacional-electromovilidad_ministerio-de-energia.pdf (accessed on 5 January 2022).
  6. Zhou, M.; Long, P.; Kong, N.; Zhao, L.; Jia, F.; Campy, K.S. Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China. Transp. Res. Part A Policy Pract. 2021, 144, 134–152.
  7. Simsekoglu, Ö. Socio-demographic characteristics, psychological factors and knowledge related to electric car use: A comparison between electric and conventional car drivers. Transp. Policy 2018, 72, 180–186.
  8. Danielis, R.; Rotaris, L.; Giansoldati, M.; Scorrano, M. Drivers’ preferences for electric cars in Italy. Evidence from a country with limited but growing electric car uptake. Transp. Res. Part A Policy Pract. 2020, 137, 79–94.
  9. Dong, X.; Zhang, B.; Wang, B.; Wang, Z. Urban households’ purchase intentions for pure electric vehicles under subsidy contexts in China: Do cost factors matter? Transp. Res. Part A Policy Pract. 2020, 135, 183–197.
  10. Rotaris, L.; Giansoldati, M.; Scorrano, M. The slow uptake of electric cars in Italy and Slovenia. Evidence from a stated-preference survey and the role of knowledge and environmental awareness. Transp. Res. Part A Policy Pract. 2021, 144, 1–18.
  11. Globisch, J.; Plötz, P.; Dütschke, E.; Wietschel, M. Consumer preferences for public charging infrastructure for electric vehicles. Transp. Policy 2019, 81, 54–63.
  12. Canepa, K.; Hardman, S.; Tal, G. An early look at plug-in electric vehicle adoption in disadvantaged communities in California. Transp. Policy 2019, 78, 19–30.
  13. Sun, Z.; Gao, W.; Li, B.; Wang, L. Locating charging stations for electric vehicles. Transp. Policy 2020, 98, 48–54.
  14. Giansoldati, M.; Danielis, R.; Rotaris, L.; Scorrano, M. The role of driving range in consumers’ purchasing decision for electric cars in Italy. Energy 2018, 165, 267–274.
  15. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478.
  16. Pearre, N.S.; Kempton, W.; Guensler, R.L.; Elango, V.V. Electric vehicles: How much range is required for a day’s driving? Transp. Res. Part C Emerg. Technol. 2011, 19, 1171–1184.
  17. Khan, M.; Kockelman, K.M. Predicting the market potential of plug-in electric vehicles using multiday GPS data. Energy Policy 2012, 46, 225–233.
  18. Greaves, S.; Backman, H.; Ellison, A.B. An empirical assessment of the feasibility of battery electric vehicles for day-to-day driving. Transp. Res. Part A Policy Pract. 2014, 66, 226–237.
  19. Dong, J.; Wu, X.; Liu, C.; Lin, Z.; Hu, L. The impact of reliable range estimation on battery electric vehicle feasibility. Int. J. Sustain. Transp. 2020, 14, 833–842.
  20. Dalla Chiara, B.; Deflorio, F.; Eid, M. Analysis of real driving data to explore travelling needs in relation to hybrid–electric vehicle solutions. Transp. Policy 2019, 80, 97–116.
  21. Miwa, T.; Sato, H.; Morikawa, T. Range and Battery Depletion Concerns with Electric Vehicles. J. Adv. Transp. 2017, 2017, 7491234.
  22. Wu, Y.A.; Lau, Y.Y.; Wong, L.M.; Wu, J. A Preliminary Feasibility Study of Electric Taxi Promotion in Hong Kongâ Behavior Modelling of Driving Patterns and Preferences. Appl. Sci. 2023, 13, 1491.
  23. Girard, A.; Roberts, C.; Simon, F.; Ordoñez, J. Solar electricity production and taxi electrical vehicle conversion in Chile. J. Clean. Prod. 2019, 210, 1261–1269.
  24. Kang, S.C.; Lee, H. Economic appraisal of implementing electric vehicle taxis in Seoul. Res. Transp. Econ. 2019, 73, 45–52.
  25. Darcovich, K.; Ribberink, H.; Michelet, C.; Lombardi, K.; Ghorab, M. The Feasibility of Electric Vehicles as Taxis in a Canadian Context. In Proceedings of the 2019 Electric Vehicles International Conference (EV), Bucharest, Romania, 3–4 October 2019.
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