Road Slope Estimation in Bicycles without Torque Measurements

Corno, Matteo; Spagnol, Pierfrancesco; Savaresi, Sergio M.

doi:10.3182/20140824-6-za-1003.00302

Cited by 20 publications

(7 citation statements)

References 16 publications

(16 reference statements)

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“…The BMS implements safety checks and estimates the battery state of charge as explained in [14]. • An Inertial Measurement Unit (IMU), that, among other tasks, estimates the road slope using the results of [15]. • A programmable Electric Control Unit (ECU).…”

Section: Electric Shared Bike Descriptionmentioning

confidence: 99%

Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle

Corno

Duz

Savaresi

2022

IEEE Trans. Contr. Syst. Technol.

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This paper proposes an energy management system (EMS) for shared electric bicycles.The objective is to guarantee electric assistance to the cyclist while avoiding discharging the battery. The basic working principle exploits the cycling efficiency gaps.The proposed multi-layered EMS is specifically tailored to a free-floating bike sharing setting. The innermost layer manages the assistance and energy harvesting with the objective of yielding an intuitive human-machine interface. The middle level modulates the level of assistance so to track a desired average battery power. This is an adaptive model-based controller designed on a control-oriented model of the cyclist and bicycle energy dynamics. A cyclist profiling mechanism enables the model adaptation. The outermost loop guarantees the long term robustness by tracking a desired battery state of charge profile.Extensive simulations and experimental tests validate this approach in terms of usability and charge sustenance, proving that the cyclist profiling is of paramount importance.

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Section: Electric Shared Bike Descriptionmentioning

confidence: 99%

Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle

Corno

Duz

Savaresi

2022

IEEE Trans. Contr. Syst. Technol.

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“…Indeed, each state equation in Kalman filters is complemented with a noise term whose variance characterizes the (un)reliability of the corresponding equation (e.g., a higher variance for (10e) than for (10c) reflects that (10e) is less reliable than (10c)) 1 . The approach in (10e) is conceptually the same as the one used when using Kalman filtering solutions for the estimation of the road slope [22]- [25], based on which we wrote (10d). The choice of state equations (10b)-(10e) calls naturally for the state vector…”

Section: Complete Model Of the Driver Intentionmentioning

confidence: 99%

Longitudinal Jerk Estimation of Driver Intentions for Advanced Driver Assistance Systems

Bisoffi

Biral

Lio

et al. 2017

IEEE/ASME Trans. Mechatron.

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This work aims at estimating the longitudinal jerk of the vehicle as it is acted by a human driver, in the context of preventive safety. A reliable estimate is needed to infer the current driver intention in an advanced driving assistance system developed by the authors. The derived intention-oriented model for the longitudinal dynamics is embedded into an enhanced Kalman filter that provides the user with a knob to trade off between responsiveness of the estimate and noise rejection. The scheme is fit for on-line usage, relies on signals commonly available on the CAN bus of modern vehicles, and requires a very limited number of parameters. Its effectiveness is validated on experimental data, and compared with alternative approaches.

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“…The aforementioned methods use driving resistance forces and longitudinal dynamics for the estimation problem. This paper uses a similar approach described by Corno et al [10], because it is more suitable for the application to motorcycles. The idea is that the gravitational acceleration is measured by the IMU in longitudinal direction while riding up-or downhill, see Figure 4.…”

Section: Road Slope Estimatormentioning

confidence: 99%

“…These models exist in a variety of publications, see e.g. [6,10,19,20]. They differ in complexity and degrees of freedom for their individual application.…”

Section: Wheel Force Calculationmentioning

confidence: 99%