IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p6053-d1534814.html
   My bibliography  Save this article

A Model-Driven Approach for Estimating the Energy Performance of an Electric Vehicle Used as a Taxi in an Intermediate Andean City

Author

Listed:
  • Jairo Castillo-Calderón

    (PhD Program in Mechanical Engineering, University of Zaragoza, C. de Pedro Cerbuna, 12, 50009 Zaragoza, Spain
    Scientific Experiences in Mobility, Vehicles and Transport (eX-MoVeT) Research Group, Faculty of Energy, Industries and Non-Renewable Natural Resources, Universidad Nacional de Loja, Av. Pio Jaramillo Alvarado, Loja 110103, Ecuador)

  • Daniel Cordero-Moreno

    (Faculty of Science and Technology, Universidad del Azuay, Av. 24 de Mayo 7-77, Cuenca 010204, Ecuador)

  • Emilio Larrodé Pellicer

    (Department of Mechanical Engineering, University of Zaragoza, C. de Pedro Cerbuna, 12, 50009 Zaragoza, Spain)

Abstract

Regarding the decision to opt for vehicles with electric propulsion systems to achieve a sustainable future, much research has focused on the electrification of passenger cars, since this class of vehicles is the largest contributor of greenhouse gas emissions in the transportation sector. The purpose of this paper is to assess the energy performance of an electric vehicle used as a taxi in Loja, Ecuador, an intermediate Andean city, using a model-driven approach. Data acquisition was performed through the OBDII port of the KIA SOUL EV for 24 days and the variable mass of the vehicle was recorded as a function of the number of passengers; the effects of road gradient were also considered. The energy performance of the vehicle was simulated by developing an analytical model in MATLAB/Simulink. An average measured battery performance of 8.49 ± 1.4 km/kWh per day was obtained, where the actual energy regenerated was 31.2 ± 1.5%. To validate the proposed model, the results of the daily energy performance estimated with the simulation were compared with those measured in real driving conditions. The results demonstrated a Pearson correlation coefficient of 0.93, indicating a strong positive linear dependence between the variables. In addition, a coefficient of determination of 0.86 and a mean absolute percentage error of 3.35% were obtained, suggesting that the model has a satisfactory predictive capacity for energy performance.

Suggested Citation

  • Jairo Castillo-Calderón & Daniel Cordero-Moreno & Emilio Larrodé Pellicer, 2024. "A Model-Driven Approach for Estimating the Energy Performance of an Electric Vehicle Used as a Taxi in an Intermediate Andean City," Energies, MDPI, vol. 17(23), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6053-:d:1534814
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/6053/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/6053/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ye, Fei & Kang, Wanlin & Li, Lixu & Wang, Zhiqiang, 2021. "Why do consumers choose to buy electric vehicles? A paired data analysis of purchase intention configurations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 14-27.
    2. Castro Verdezoto, Pedro L. & Vidoza, Jorge A. & Gallo, Waldyr L.R., 2019. "Analysis and projection of energy consumption in Ecuador: Energy efficiency policies in the transportation sector," Energy Policy, Elsevier, vol. 134(C).
    3. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    4. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    5. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    6. Icaza, Daniel & Borge-Diez, David & Galindo, Santiago Pulla, 2022. "Analysis and proposal of energy planning and renewable energy plans in South America: Case study of Ecuador," Renewable Energy, Elsevier, vol. 182(C), pages 314-342.
    7. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    2. Sun, Xilei & Fu, Jianqin, 2024. "Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle," Energy, Elsevier, vol. 300(C).
    3. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    4. Sun, Xilei & Zhou, Feng & Fu, Jianqin & Liu, Jingping, 2024. "Experiment and simulation study on energy flow characteristics of a battery electric vehicle throughout the entire driving range in low-temperature conditions," Energy, Elsevier, vol. 292(C).
    5. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    6. Perugu, Harikishan & Collier, Sonya & Tan, Yi & Yoon, Seungju & Herner, Jorn, 2023. "Characterization of battery electric transit bus energy consumption by temporal and speed variation," Energy, Elsevier, vol. 263(PC).
    7. Klemen Deželak & Klemen Sredenšek & Sebastijan Seme, 2023. "Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method," Energies, MDPI, vol. 16(14), pages 1-15, July.
    8. Maksymilian Mądziel, 2024. "Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses," Energies, MDPI, vol. 17(5), pages 1-22, February.
    9. Hatem Abdelaty & Moataz Mohamed, 2021. "A Prediction Model for Battery Electric Bus Energy Consumption in Transit," Energies, MDPI, vol. 14(10), pages 1-26, May.
    10. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    11. Xiao, Yiyong & Zhang, Yue & Kaku, Ikou & Kang, Rui & Pan, Xing, 2021. "Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    12. Ibrahim, Amier & Jiang, Fangming, 2021. "The electric vehicle energy management: An overview of the energy system and related modeling and simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    13. David Watling & Patrícia Baptista & Gonçalo Duarte & Jianbing Gao & Haibo Chen, 2022. "Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles," Energies, MDPI, vol. 15(9), pages 1-28, May.
    14. Giacomo Lanza & Mauro Passacantando & Maria Grazia Scutellà, 2024. "Matheuristic approaches to the green sequencing and routing problem," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 994-1045, September.
    15. Ma, Xiaolei & Miao, Ran & Wu, Xinkai & Liu, Xianglong, 2021. "Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing," Energy, Elsevier, vol. 216(C).
    16. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Zhang, Lei & Dorrell, David G. & Sun, Fengchun, 2022. "Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation," Applied Energy, Elsevier, vol. 327(C).
    17. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    18. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    19. Küng, Lukas & Bütler, Thomas & Georges, Gil & Boulouchos, Konstantinos, 2019. "How much energy does a car need on the road?," Applied Energy, Elsevier, vol. 256(C).
    20. Hariharan, C. & Gunadevan, D. & Arun Prakash, S. & Latha, K. & Antony Aroul Raj, V. & Velraj, R., 2022. "Simulation of battery energy consumption in an electric car with traction and HVAC model for a given source and destination for reducing the range anxiety of the driver," Energy, Elsevier, vol. 249(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6053-:d:1534814. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.