A full hybrid electric bike: How to increase human efficiency

Spagnol, Pierfrancesco; Alli, Giovanni; Spelta, Cristiano; Lisanti, P.; Todeschini, Fabio; Savaresi, Sergio M.; Morelli, Alessandro

doi:10.1109/acc.2012.6314871

Cited by 26 publications

(17 citation statements)

References 7 publications

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“…Notice that, from (2), k i−1 > k i , ∀i = 2, … , T. The numerical results for the considered experiments are reported in Table 1. For the 3 gear ratios tested (1,4,7), the values of from the FFT coincide with the expected ones computed using k and the average wheel speed̄. The previous analysis shows that the velocity measurements exhibit 2 main features, ie, (1) an actual oscillation coupled to the pedaling cadence and (2) a spurious measurement periodic noise.…”

Section: Preliminary Analysissupporting

confidence: 71%

“…Potentially, the pedaling asymmetry effect can be employed to derive the pedaling cadence; however, care must be taken. The influence of the first measurement noise harmonic can be critical, especially for medium gear ratios (i ∈ [2,3,4]) and low speed values. For instance, in the figure, for i = 3, the fundamental component of the disturbance is perfectly overlapped to 3 (since k 3 = 1).…”

Section: Preliminary Analysismentioning

confidence: 99%

“…Consider now to introduce the additional information deriving from the transmission model (4). Since the measurement of is available, its inclusion in the model of the signal allows one to take into account the partial knowledge of the frequency deriving from the proportionality to the rotational speed.…”

Section: Indirect Cadence Estimation Via Gear Ratio Detectionmentioning

confidence: 99%

“…As such, they are attracting the attention not only of the industry (the market is posed to reach a value of 2 billion USD by 2050) but also of the scientific community. Researchers have focused mainly on 3 different directions, ie, (1) the study of the impact of EPAC's on the pedaling efficiency, [4][5][6][7][8][9][10][11] (2) the design of efficient actuation devices, [12][13][14][15][16][17][18] and (3) solving sensing issues as slope estimation 19 and pedaling torque measurement. [20][21][22]…”

Section: Introductionmentioning

confidence: 99%

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Real‐time cycling cadence estimation via wheel speed measurement

Rallo

Formentin

Corno

et al. 2018

Adaptive Control & Signal

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The need to reduce emissions and improve mobility in overcrowded cities is promoting the use of bicycles as transportation means. Bicycles have a small footprint, are easy to use, and cost effective. The introduction of modern electric bicycles has also widened the user base, extending the reach of bicycles as a commuter's option. Electric bicycles, in order to meet regulation standards, need sensors that are not usually available on muscular bicycles, like torque or cadence sensors. In this paper, we develop a cadence estimation strategy based on the wheel speed encoder only, thus allowing to remove the cadence sensor. Specifically, we propose 2 approaches, ie, a direct cadence estimate and an indirect one via gear ratio estimate. Both estimation problems are shown to be equivalent to a frequency tracking problem, which can be solved by Kalman filtering. The final algorithm embeds a logic supervisor that guarantees the reliability of the procedure in all working conditions, including freewheeling. The whole analysis and development are based upon a thorough experimental campaign using an instrumented bicycle.

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Section: Preliminary Analysissupporting

confidence: 71%

Section: Preliminary Analysismentioning

confidence: 99%

Section: Indirect Cadence Estimation Via Gear Ratio Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real‐time cycling cadence estimation via wheel speed measurement

Rallo

Formentin

Corno

et al. 2018

Adaptive Control & Signal

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“…More recently, the interest in the electrification of transportation [8] is also focused on new vehicles for indoor mobility [9], light Electric Vehicles [10], and Electric Bicycle (EBs) [11][12] [13]. Electric mobility also provides an important contribution to smooth the intermittent energy production from renewable sources [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Mobile Cockpit System for Enhanced Electric Bicycle Use

Ferreira

Monteiro

Afonso

2015

IEEE Trans. Ind. Inf.

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This paper presents the project of a Mobile Cockpit System (MCS) for smartphones, which provides assistance to Electric Bicycle (EB) cyclists in Smart Cities' environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related with the EB's use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycle in general.

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