Energy Management of Electric Bicycles Given a Traveling Elevation Profile

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Sebastián Seria Vanessa Quintero Pablo A. Espinoza Aramis Pérez Francisco Jaramillo Matías Benavides Marcos Orchard

Abstract

This research proposes a method for energy management in electric bicycles with Lithium-Ion batteries. This method optimizes the way energy is consumed to maximize the rider’s comfort, subject to constraints on the battery State-of-Charge once destination is reached. The algorithm considers the elevation profile of the route chosen by the rider, predicting the battery energy consumption based on physical parameters of the user and the bicycle. The route is partitioned into equispaced segments, and the optimization problem is then formulated to decide when to pedal or when to use the bicycle electric motor. Binary Particle Swarm Optimization (BPSO) is used to solve the optimization problem, while particle-filter-based estimators are used to determine the initial battery State-of-Charge. We surmise that management of the variability associated with the State-of-Charge swing range, in a systematic manner, will help to increase the battery life.

How to Cite

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
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Keywords

Li-ion Battery, Binary Particle Swarm Optimization, Electric Bicycle, Energy Management

References
Muetze, A., & Tan, Y. C. (2005, October). Performance evaluation of electric bicycles. In Industry Applications Conference, 2005. Fourtieth IAS Annual Meeting. Conference Record of the 2005 (Vol. 4, pp. 2865-2872). IEEE.
International Energy Agency. (2012). Energy Technology Perspectives 2012: Pathways to a Clean Energy System. IEA.
Corno, M., Berretta, D., Spagnol, P., & Savaresi, S. M. (2016). Design, control, and validation of a charge-sustaining parallel hybrid bicycle. IEEE Transactions on Control Systems Technology, 24(3), 817-829. Kennedy, J., & Eberhart, R. C. (1997, October). A discrete binary version of the particle swarm algorithm. In Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on (Vol. 5, pp. 4104-4108). IEEE. Lee, S., Soak, S., Oh, S., Pedrycz, W., & Jeon, M. (2008). Modified binary particle swarm optimization. Progress in Natural Science, 18(9), 1161-1166.
Section
Technical Papers

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