Research Article
BibTex RIS Cite

E-bike load demand estimation for transport using cartographic data

Year 2024, Volume: 4 Issue: 1, 20 - 29, 30.06.2024

Abstract

In remote areas of Sub-Saharan Africa, as well as in Rwanda, communities face the problem of finding an affordable and suitable way of transport for their daily life. Currently, the short-range transportation of people and goods rely on manual traction (e.g., bicycles, handmade wooden bicycle, etc.) and on small combustion engines. However, this region has a large renewable energy source (solar) which can help to mitigate this problem. Electric bicycles may be used to tackle the problem, although their use is still not largely widespread despite its potential. In addition, the local society does not have an easy way to accurately estimate the amount of energy consumed for a certain itinerary to be sure of how the electric vehicles perform. Thus, characteristic analysis of consumed energy is very important to analyze the performance of electric vehicle storage systems, model charging infrastructure, size vehicles, planning for itineraries, etc. This paper presents a methodology to estimate the power consumed by electric bikes for specific itineraries. In order to accomplish so, the WebPlotDigitizer tool and Google Maps were used to produce driving profile patterns. The results are compared against experimental data, showing the accuracy of the methodology presented. The simulated results show that the consumed power value differs by only 1.68% from the experimental recorded value; the difference can be explained by traffic congestion, the density of traffic, and the intersection that occurred during the experiment. The presented method of driving energy requirement estimation using WebPlotDigitizer is attractive, affordable, and easy to use.

Supporting Institution

University of Rwanda(UR_CST), African Center for Excellence Energy for Sustainable Development, Mechanical and Energy Engineering Department.

References

  • [1] G. R. C. Mouli, P. Van Duijsen, F. Grazian, A. Jamodkar, P. Bauer, and O. Isabella, “Sustainable e-bike charging station that enables AC, DC and wireless charging from solar energy,” Energies, vol. 13, no. 14, 2020, doi: 10.3390/en13143549.[2] K. Aliila Greyson, G. Bosinge Gerutu, C. Hamisi Mohamed, and P. Victor Chombo, “Exploring the adoption of e-bicycle for student mobility in rural and urban areas of Tanzania,” Sustain. Energy Technol. Assessments, vol. 45, no. April, p. 101206, 2021, doi: 10.1016/j.seta.2021.101206.
  • [3] R. Zhang and E. Yao, “Electric vehicles’ energy consumption estimation with real driving condition data,” Transp. Res. Part D Transp. Environ., vol. 41, pp. 177–187, 2015, doi: 10.1016/j.trd.2015.10.010.
  • [4] L. Liu and T. Suzuki, “Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities,” J. Transp. Heal., vol. 13, no. October 2018, pp. 150–163, 2019, doi: 10.1016/j.jth.2019.04.001.
  • [5] D. Drevon, S. R. Fursa, and A. L. Malcolm, “Intercoder Reliability and Validity of WebPlotDigitizer in Extracting Graphed Data,” Behav. Modif., vol. 41, no. 2, pp. 323–339, 2017, doi: 10.1177/0145445516673998.
  • [6] “Kigali to Musanze - Cycling Route - Bikemap.” https://www.bikemap.net/en/r/4425633/ (accessed Oct. 23, 2023).
  • [7] B. Qiujun Zhu and P. R. Komuniecki, “Driving Pattern Generation for Customized Energy Control Strategy in Hybrid Electric Vehicle Applications.” 2014.
  • [8] M. N. Yuniarto, S. E. Wiratno, Y. U. Nugraha, I. Sidharta, and A. Nasruddin, “Modeling, Simulation, and Validation of An Electric Scooter Energy Consumption Model: A Case Study of Indonesian Electric Scooter,” IEEE Access, vol. 10, pp. 48510–48522, 2022, doi: 10.1109/ACCESS.2022.3171860.
  • [9] I. Miri, A. Fotouhi, and N. Ewin, “Electric vehicle energy consumption modelling and estimation—A case study,” Int. J. Energy Res., vol. 45, no. 1, pp. 501–520, 2021, doi: 10.1002/er.5700.
  • [10] D. Jiménez, S. Hernández, J. Fraile-Ardanuy, J. Serrano, R. Fernández, and F. Alvarez, “Modelling the effect of driving events on electrical vehicle energy consumption using inertial sensors in smartphones,” Energies, vol. 11, no. 2, 2018, doi: 10.3390/en11020412.
  • [11] D. Penić, M. Štambuk, N. Raičević, and M. Vražić, “Estimation of required power and energy for bicycle electrification using global positioning system,” Renew. Energy Power Qual. J., vol. 17, no. 17, pp. 475–479, 2019, doi: 10.24084/repqj17.348.
  • [12] S. Hong, H. Hwang, D. Kim, S. Cui, and I. Joe, “Real driving cycle-based state of charge prediction for ev batteries using deep learning methods,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311285.
  • [13] S. T. Haumann, D. Bucher, and D. Jonietz, “Energy-based Routing and Cruising Range Estimation for Electric Bicycles,” Soc. Geo-Innovation short Pap. posters poster Abstr. 20th Agil. Conf. Geogr. Inf. Sci., no. 2006, 2017.
  • [14] G. Thejasree and R. Maniyeri, “E-bike system modeling and simulation,” Proc. - 2019 IEEE Int. Conf. Intell. Syst. Green Technol. ICISGT 2019, no. 1, pp. 9–14, 2019, doi: 10.1109/ICISGT44072.2019.00017.
  • [15] C. Abagnale, M. Cardone, P. Iodice, S. Strano, M. Terzo, and G. Vorraro, “A dynamic model for the performance and environmental analysis of an innovative e-bike,” Energy Procedia, vol. 81, pp. 618–627, 2015, doi: 10.1016/j.egypro.2015.12.046.
  • [16] S. Raghunath, “Hardware Design Considerations for an Electric BicycleUsing a BLDC Motor,” no. June, pp. 1–22, 2021.
  • [17] G. L. Plett, “1.Battery Boot Camp,” Batter. Manag. Syst. Vol. 1 Batter. Model., pp. 1–28, 2015.
  • [18] X. Zhao, Q. Yu, J. Ma, Y. Wu, M. Yu, and Y. Ye, “Development of a representative EV urban driving cycle based on a k-Means and SVM hybrid clustering algorithm,” J. Adv. Transp., vol. 2018, pp. 22–25, 2018, doi: 10.1155/2018/1890753.
  • [19] D. Perrotta, B. Ribeiro, R. J. F. Rossetti, and J. L. Afonso, “On the Potential of Regenerative Braking of Electric Buses as a Function of Their Itinerary,” Procedia - Soc. Behav. Sci., vol. 54, no. April 2015, pp. 1156–1167, 2012, doi: 10.1016/j.sbspro.2012.09.830.
  • [20] H. Yu, F. Tseng, and R. McGee, “Driving pattern identification for EV range estimation,” 2012 IEEE Int. Electr. Veh. Conf. IEVC 2012, 2012, doi: 10.1109/IEVC.2012.6183207.
  • [21] Q. Wu et al., “Driving Pattern Analysis for Electric Vehicle (EV) Grid Integration Study,” pp. 1–6, 2011, doi: 10.1109/isgteurope.2010.5751581.
  • [22] Z. H. Che Daud, Z. Asus, S. A. Abu Bakar, N. Abu Husain, P. Mohd Samin, and D. Chrenko, “Temperature prediction of lithium-ion battery used in realistic driving cycles,” 2017 IEEE Veh. Power Propuls. Conf. VPPC 2017 - Proc., vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/VPPC.2017.8330873.
  • [23] “E-bike data analysis – Blog for Medad Rufus Newman.” http://medadnewman.co.uk/2021/03/12/data-analysis-of-an-e-bike/ (accessed Oct. 23, 2023).
  • [24] W. Wang, “MODELING, ESTIMATION AND BENCHMARKING OF LITHIUM ION ELECTRIC BICYCLE BATTERY,” 2016, Accessed: Oct. 24, 2023. [Online]. Available: https://macsphere.mcmaster.ca/handle/11375/20293
  • [25] “How To Select The Right Motor For An Electric Bicycle – EBikeMarketplace.” https://ebikemarketplace.com/blogs/e-bike-parts-components-and-accessories/how-to-select-the-right-motor-for-an-electric-bicycle (accessed Oct. 23, 2023).
  • [26] S. Berntsen, L. Malnes, A. Langåker, and E. Bere, “Physical activity when riding an electric assisted bicycle,” Int. J. Behav. Nutr. Phys. Act., vol. 14, no. 1, pp. 1–8, 2017, doi: 10.1186/s12966-017-0513-z.
  • [27] A. Mohamed and A. Bigazzi, “Speed and road grade dynamics of urban trips on electric and conventional bicycles,” Transp. B, vol. 7, no. 1, pp. 1467–1480, 2019, doi: 10.1080/21680566.2019.1630691.
Year 2024, Volume: 4 Issue: 1, 20 - 29, 30.06.2024

Abstract

References

  • [1] G. R. C. Mouli, P. Van Duijsen, F. Grazian, A. Jamodkar, P. Bauer, and O. Isabella, “Sustainable e-bike charging station that enables AC, DC and wireless charging from solar energy,” Energies, vol. 13, no. 14, 2020, doi: 10.3390/en13143549.[2] K. Aliila Greyson, G. Bosinge Gerutu, C. Hamisi Mohamed, and P. Victor Chombo, “Exploring the adoption of e-bicycle for student mobility in rural and urban areas of Tanzania,” Sustain. Energy Technol. Assessments, vol. 45, no. April, p. 101206, 2021, doi: 10.1016/j.seta.2021.101206.
  • [3] R. Zhang and E. Yao, “Electric vehicles’ energy consumption estimation with real driving condition data,” Transp. Res. Part D Transp. Environ., vol. 41, pp. 177–187, 2015, doi: 10.1016/j.trd.2015.10.010.
  • [4] L. Liu and T. Suzuki, “Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities,” J. Transp. Heal., vol. 13, no. October 2018, pp. 150–163, 2019, doi: 10.1016/j.jth.2019.04.001.
  • [5] D. Drevon, S. R. Fursa, and A. L. Malcolm, “Intercoder Reliability and Validity of WebPlotDigitizer in Extracting Graphed Data,” Behav. Modif., vol. 41, no. 2, pp. 323–339, 2017, doi: 10.1177/0145445516673998.
  • [6] “Kigali to Musanze - Cycling Route - Bikemap.” https://www.bikemap.net/en/r/4425633/ (accessed Oct. 23, 2023).
  • [7] B. Qiujun Zhu and P. R. Komuniecki, “Driving Pattern Generation for Customized Energy Control Strategy in Hybrid Electric Vehicle Applications.” 2014.
  • [8] M. N. Yuniarto, S. E. Wiratno, Y. U. Nugraha, I. Sidharta, and A. Nasruddin, “Modeling, Simulation, and Validation of An Electric Scooter Energy Consumption Model: A Case Study of Indonesian Electric Scooter,” IEEE Access, vol. 10, pp. 48510–48522, 2022, doi: 10.1109/ACCESS.2022.3171860.
  • [9] I. Miri, A. Fotouhi, and N. Ewin, “Electric vehicle energy consumption modelling and estimation—A case study,” Int. J. Energy Res., vol. 45, no. 1, pp. 501–520, 2021, doi: 10.1002/er.5700.
  • [10] D. Jiménez, S. Hernández, J. Fraile-Ardanuy, J. Serrano, R. Fernández, and F. Alvarez, “Modelling the effect of driving events on electrical vehicle energy consumption using inertial sensors in smartphones,” Energies, vol. 11, no. 2, 2018, doi: 10.3390/en11020412.
  • [11] D. Penić, M. Štambuk, N. Raičević, and M. Vražić, “Estimation of required power and energy for bicycle electrification using global positioning system,” Renew. Energy Power Qual. J., vol. 17, no. 17, pp. 475–479, 2019, doi: 10.24084/repqj17.348.
  • [12] S. Hong, H. Hwang, D. Kim, S. Cui, and I. Joe, “Real driving cycle-based state of charge prediction for ev batteries using deep learning methods,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311285.
  • [13] S. T. Haumann, D. Bucher, and D. Jonietz, “Energy-based Routing and Cruising Range Estimation for Electric Bicycles,” Soc. Geo-Innovation short Pap. posters poster Abstr. 20th Agil. Conf. Geogr. Inf. Sci., no. 2006, 2017.
  • [14] G. Thejasree and R. Maniyeri, “E-bike system modeling and simulation,” Proc. - 2019 IEEE Int. Conf. Intell. Syst. Green Technol. ICISGT 2019, no. 1, pp. 9–14, 2019, doi: 10.1109/ICISGT44072.2019.00017.
  • [15] C. Abagnale, M. Cardone, P. Iodice, S. Strano, M. Terzo, and G. Vorraro, “A dynamic model for the performance and environmental analysis of an innovative e-bike,” Energy Procedia, vol. 81, pp. 618–627, 2015, doi: 10.1016/j.egypro.2015.12.046.
  • [16] S. Raghunath, “Hardware Design Considerations for an Electric BicycleUsing a BLDC Motor,” no. June, pp. 1–22, 2021.
  • [17] G. L. Plett, “1.Battery Boot Camp,” Batter. Manag. Syst. Vol. 1 Batter. Model., pp. 1–28, 2015.
  • [18] X. Zhao, Q. Yu, J. Ma, Y. Wu, M. Yu, and Y. Ye, “Development of a representative EV urban driving cycle based on a k-Means and SVM hybrid clustering algorithm,” J. Adv. Transp., vol. 2018, pp. 22–25, 2018, doi: 10.1155/2018/1890753.
  • [19] D. Perrotta, B. Ribeiro, R. J. F. Rossetti, and J. L. Afonso, “On the Potential of Regenerative Braking of Electric Buses as a Function of Their Itinerary,” Procedia - Soc. Behav. Sci., vol. 54, no. April 2015, pp. 1156–1167, 2012, doi: 10.1016/j.sbspro.2012.09.830.
  • [20] H. Yu, F. Tseng, and R. McGee, “Driving pattern identification for EV range estimation,” 2012 IEEE Int. Electr. Veh. Conf. IEVC 2012, 2012, doi: 10.1109/IEVC.2012.6183207.
  • [21] Q. Wu et al., “Driving Pattern Analysis for Electric Vehicle (EV) Grid Integration Study,” pp. 1–6, 2011, doi: 10.1109/isgteurope.2010.5751581.
  • [22] Z. H. Che Daud, Z. Asus, S. A. Abu Bakar, N. Abu Husain, P. Mohd Samin, and D. Chrenko, “Temperature prediction of lithium-ion battery used in realistic driving cycles,” 2017 IEEE Veh. Power Propuls. Conf. VPPC 2017 - Proc., vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/VPPC.2017.8330873.
  • [23] “E-bike data analysis – Blog for Medad Rufus Newman.” http://medadnewman.co.uk/2021/03/12/data-analysis-of-an-e-bike/ (accessed Oct. 23, 2023).
  • [24] W. Wang, “MODELING, ESTIMATION AND BENCHMARKING OF LITHIUM ION ELECTRIC BICYCLE BATTERY,” 2016, Accessed: Oct. 24, 2023. [Online]. Available: https://macsphere.mcmaster.ca/handle/11375/20293
  • [25] “How To Select The Right Motor For An Electric Bicycle – EBikeMarketplace.” https://ebikemarketplace.com/blogs/e-bike-parts-components-and-accessories/how-to-select-the-right-motor-for-an-electric-bicycle (accessed Oct. 23, 2023).
  • [26] S. Berntsen, L. Malnes, A. Langåker, and E. Bere, “Physical activity when riding an electric assisted bicycle,” Int. J. Behav. Nutr. Phys. Act., vol. 14, no. 1, pp. 1–8, 2017, doi: 10.1186/s12966-017-0513-z.
  • [27] A. Mohamed and A. Bigazzi, “Speed and road grade dynamics of urban trips on electric and conventional bicycles,” Transp. B, vol. 7, no. 1, pp. 1467–1480, 2019, doi: 10.1080/21680566.2019.1630691.
There are 26 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Aimable Ngendahayo 0000-0001-6017-2195

Adrià Junyent-ferré This is me 0000-0002-8500-9906

Joan-marc Rodriguez-bernuz This is me

Etienne Ntagwirumugara This is me 0000-0001-6358-5163

Early Pub Date December 1, 2023
Publication Date June 30, 2024
Acceptance Date September 25, 2023
Published in Issue Year 2024 Volume: 4 Issue: 1

Cite

Vancouver Ngendahayo A, Junyent-ferré A, Rodriguez-bernuz J-m, Ntagwirumugara E. E-bike load demand estimation for transport using cartographic data. Computers and Informatics. 2024;4(1):20-9.