Smart IoT Energy Optimisation and Localisation Monitoring for E-Bike Sharing
DOI:
https://doi.org/10.31436/iiumej.v26i2.3448Keywords:
Smart City, Electric Motorcycle, electric vehicle (EV), localization, Energy optimization, Internet of Things (IoT)Abstract
E-bike sharing has emerged as a sustainable and convenient mode of transportation, offering lightweight, energy-efficient mobility solutions. However, existing systems face challenges such as limited input parameters for modeling, leading to inefficiencies in energy optimization algorithms and power assist mechanisms. A significant concern is the rapid depletion of batteries, which reduces the availability of e-bikes, increases operational costs for fleet managers, and impacts user satisfaction. To address these challenges, this project developed a Smart IoT Energy Optimization and Localization Monitoring System that integrates multi-sensor data, IoT connectivity, and advanced data analytics to monitor real-time usage patterns, battery levels, and the location of e-bikes. The methodology involved integrating sensors to collect key data, implementing connectivity for real-time monitoring, and developing an energy optimization algorithm to prolong battery life, improving the efficiency of the e-bike sharing system. The results demonstrated a 15% improvement in energy efficiency, which increased battery state-of-charge (SOC) and extended operational range. Additionally, the system enabled better fleet management by ensuring optimal energy usage and the availability of e-bikes for users. This approach aligns seamlessly with the Sustainable Development Goals (SDGs) by promoting eco-friendly transportation and enhancing user accessibility. The integration of IoT technology has proven effective in overcoming the limitations of traditional systems, offering a scalable and efficient solution for modern urban mobility.
ABSTRAK: Perkongsian e-basikal telah muncul sebagai kaedah pengangkutan yang lestari dan mudah, menawarkan penyelesaian mobiliti yang ringan dan cekap tenaga. Walau bagaimanapun, sistem sedia ada menghadapi cabaran seperti parameter input yang terhad untuk pemodelan, yang menyebabkan ketidakcekapan dalam algoritma pengoptimuman tenaga dan mekanisme bantuan kuasa. Masalah utama adalah penurunan bateri yang cepat, yang mengurangkan ketersediaan e-basikal, meningkatkan kos operasi untuk pengurus armada, dan memberi kesan kepada kepuasan pengguna. Untuk mengatasi cabaran ini, projek ini membangunkan Sistem Pemantauan Pengoptimuman Tenaga dan Lokalisasi IoT Pintar yang mengintegrasikan data multi-sensor, sambungan IoT, dan analitik data lanjutan untuk memantau corak penggunaan masa nyata, tahap bateri, dan lokasi e-basikal. Metodologi ini melibatkan pengintegrasian sensor untuk mengumpulkan data penting, pelaksanaan sambungan untuk pemantauan masa nyata, dan pembangunan algoritma pengoptimuman tenaga untuk memanjangkan hayat bateri, dengan itu meningkatkan kecekapan sistem perkongsian e-basikal. Hasil kajian menunjukkan peningkatan kecekapan tenaga sebanyak 15%, yang meningkatkan status pengecasan bateri (SOC) dan memanjangkan jarak operasi. Selain itu, sistem ini membolehkan pengurusan armada yang lebih baik dengan memastikan penggunaan tenaga dan ketersediaan e-basikal yang optimum untuk pengguna. Pendekatan ini selaras sepenuhnya dengan matlamat pembangunan mampan (SDG) dengan mempromosikan pengangkutan mesra alam dan meningkatkan aksesibiliti pengguna. Integrasi teknologi IoT terbukti berkesan dalam mengatasi kelemahan sistem tradisional, menawarkan penyelesaian berskala dan cekap untuk mobiliti bandar moden.
Downloads
Metrics
References
V. Albuquerque, M. S. Dias, and F. Bacao, “Machine learning approaches to bike-sharing systems: A systematic literature review,” ISPRS Int J Geoinf, vol. 10, no. 2, Feb. 2021, doi: 10.3390/ijgi10020062. DOI: https://doi.org/10.3390/ijgi10020062
S. H. Yusoff, A. A. Abdullah, N. N. Nanda, and A. S. A. Badawi, “PERFORMANCE ANALYSIS ON DYNAMIC WIRELESS CHARGING FOR ELECTRIC VEHICLE USING FERRITE CORE,” IIUM Engineering Journal, vol. 23, no. 1, pp. 46–59, 2022, doi: 10.31436/IIUMEJ.V23I1.1663. DOI: https://doi.org/10.31436/iiumej.v23i1.1663
T. Bieli?ski, A. Kwapisz, and A. Wa?na, “Electric bike-sharing services mode substitution for driving, public transit, and cycling,” Transp Res D Transp Environ, vol. 96, Jul. 2021, doi: 10.1016/j.trd.2021.102883. DOI: https://doi.org/10.1016/j.trd.2021.102883
L. Stilo, D. Segura-Velandia, H. Lugo, P. P. Conway, and A. A. West, “Electric bicycles, next generation low carbon transport systems: A survey,” Transp Res Interdiscip Perspect, vol. 10, Jun. 2021, doi: 10.1016/j.trip.2021.100347. DOI: https://doi.org/10.1016/j.trip.2021.100347
T. Bieli?ski and A. Wa?na, “Electric scooter sharing and bike sharing user behaviour and characteristics,” Sustainability (Switzerland), vol. 12, no. 22, pp. 1–13, Nov. 2020, doi: 10.3390/su12229640. DOI: https://doi.org/10.3390/su12229640
Shen, W., & Li, J. (2018). Design and Implementation of Parking Monitoring System of Bike Sharing In the Electronic Barrier. Proceedings of the 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018). 2018 Joint International Advanced Engineering and Technology Research Conference (JIAET 2018), Xi’an, China. https://doi.org/10.2991/jiaet-18.2018.6 DOI: https://doi.org/10.2991/jiaet-18.2018.6
N. B. Hung, J. Sung, K. Kim, and O. Lim, “A Simulation and Experimental Study of Operating Characteristics of an Electric Bicycle,” in Energy Procedia, Elsevier Ltd, 2017, pp. 2512–2517. doi: 10.1016/j.egypro.2017.03.723. DOI: https://doi.org/10.1016/j.egypro.2017.03.723
C. H. Hsu, B. S. Liu, S. L. Jhuang, and Y. C. Li, “The study of ergonomic evaluation and critical design factors for electric scooter,” in 2016 International Conference on Applied System Innovation, IEEE ICASI 2016, Institute of Electrical and Electronics Engineers Inc., Aug. 2016. doi: 10.1109/ICASI.2016.7539571. DOI: https://doi.org/10.1109/ICASI.2016.7539571
S. Pejhan, M. Agelin-Chaab, M. Yusuf, and D. Eng, “Analysis of ebike dynamics and cyclists’ anxiety levels and interactions with road vehicles that influence safety,” Accid Anal Prev, vol. 159, Sep. 2021, doi: 10.1016/j.aap.2021.106272. DOI: https://doi.org/10.1016/j.aap.2021.106272
S. I. . Ao, Len. Gelman, D. W. L. . Hukins, Andrew. Hunter, and Alexander. Korsunsky, World Congress on Engineering?: WCE 2015?: 1-3 July, 2015, Imperial College London, London, U.K. Newswood Limited?: International Association of Engineers, 2015.
Aguiari, D., Delnevo, G., Monti, L., Ghini, V., Mirri, S., Salomoni, P., Pau, G., Im, M., Tse, R., Ekpanyapong, M., & Battistini, R. (2018). Canarin II: Designing a smart e-bike eco-system. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), 1–6. https://doi.org/10.1109/CCNC.2018.8319221 DOI: https://doi.org/10.1109/CCNC.2018.8319221
Seria, S., Quintero, V., Espinoza, P. A., Pérez, A., Jaramillo, F., Benavides, M., & Orchard, M. (2017). Energy Management of Electric Bicycles Given a Traveling Elevation Profile. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2390 DOI: https://doi.org/10.36001/phmconf.2017.v9i1.2390
D. Makurat and K. Wojewódzka-Król, “PROBLEMS OF IMPLEMENTING THE MEVO METROPOLITAN BICYCLE SYSTEM.” [Online]. Available: https://doi.org/10.26881.etil.2019.81.11 DOI: https://doi.org/10.26881/etil.2019.81.11
W. Deleenheer, L. Jáneš, and A. Jayakumar, “DEVELOPMENT OF AN ELECTRIC BICYCLE FOR A SHARING SYSTEM IN PRAGUE,” Acta Polytech CTU Proc, vol. 12, p. 24, Dec. 2017, doi: 10.14311/app.2017.12.0024. DOI: https://doi.org/10.14311/APP.2017.12.0024
C. Kiefer and F. Behrendt, “Smart e-bike monitoring system: Real-time open source and open hardware GPS assistance and sensor data for electrically-assisted bicycles,” IET Intelligent Transport Systems, vol. 10, no. 2, pp. 79–88, Mar. 2016, doi: 10.1049/iet-its.2014.0251. DOI: https://doi.org/10.1049/iet-its.2014.0251
A. Triwiyatno, S. Munahar, M. Munadi, and J. D. Setiawan, “APPLICATION OF DRIVING BEHAVIOR CONTROL SYSTEM USING ARTIFICIAL NEURAL NETWORK TO IMPROVE DRIVING COMFORT BY ADJUSTING AIR-TO-FUEL RATIO,” IIUM Engineering Journal, vol. 24, no. 2, pp. 337–353, 2023, doi: 10.31436/iiumej.v24i2.2781. DOI: https://doi.org/10.31436/iiumej.v24i2.2781
G. A. Aranda-Corral, M. A. Rodríguez, I. F. de Viana, and M. I. G. Arenas, “Genetic hybrid optimization of a real bike sharing system,” Mathematics, vol. 9, no. 18, Sep. 2021, doi: 10.3390/math9182227. DOI: https://doi.org/10.3390/math9182227
M. Giliberto, F. Arena, and G. Pau, “A fuzzy-based solution for optimized management of energy consumption in e-bikes,” J Wirel Mob Netw Ubiquitous Comput Dependable Appl, vol. 10, no. 3, pp. 45–64, Sep. 2019, doi: 10.22667/JOWUA.2019.09.30.045.
Al-Ali, A. R., Zualkernan, I., & Aloul, F. (2010). A Mobile GPRS-Sensors Array for Air Pollution Monitoring. IEEE Sensors Journal, 10(10), 1666–1671. https://doi.org/10.1109/JSEN.2010.2045890 DOI: https://doi.org/10.1109/JSEN.2010.2045890
MathWorks. (2023). MATLAB (R2023a). The MathWorks, Inc. https://www.mathworks.com
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 IIUM Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
