Simulation-based approach to application fitness for an E-Bike

Iosub, Alexandra; Kirscher, Jérôme; Rafaila, Monica; Buzo, Andi; Pelz, Georg; Goraș, Liviu

doi:10.1109/sas.2016.7479887

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Some articles addressed the fundamental aspects of an electric bike (see Figure 2), such as motor control (i.e., field-oriented [16,17], fuzzy logic [18][19][20], firefly algorithm [21], particle swarm optimization [22], model predictive [23], reinforcement learning [24], and others [25][26][27][28][29][30][31]) using different inputs, such as torque (human and machine), power and speed to control the bike's motor. Studies on battery management system (BMS) and energy recovery [18,23,[31][32][33][34], explored methods of supervising and charging the batteries used by the bikes (State of Health and State of Charges) and also explored the possibility of recovering energy by braking and in downhill situations.…”

Section: Inclusion Criteriamentioning

confidence: 99%

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

Avina-Bravo

Cassirame

Escriba

et al. 2022

Sensors

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This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.

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