Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes

Lee, Jin‐Shyan; Jiang, Jun-Wei

doi:10.1049/iet-its.2019.0092

Cited by 16 publications

(17 citation statements)

References 19 publications

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“…Furthermore, research shows a decrease in VKT per e-bike user of 20-28% [65,66], and a decrease in the distance travelled by car as a total modal share of around 10% [67]. Current developments in urban cycling include the introduction of adaptive systems for electric bikes to increase the number of users [68,69]. Such enhancements can improve cycling comfort through the use of innovative technologies that are usually found in individual motorised transport.…”

Section: Measures Influencing Travel Demand and Modal Shift In Cyclingmentioning

confidence: 99%

Bicycle Traffic Model for Sustainable Urban Mobility Planning

2021

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Modelling tools and transport models are required to assess the impact of measures for the effective planning of cycling routes in cities. This paper presents the methodology for developing a four-stage macroscopic model of bicycle traffic for the city of Gdynia, and its use in planning new bicycle routes, considering a modal shift. The model presented in this paper allows for the evaluation of the influence of the characteristics of the cycling infrastructure, along with the development of the cycling network based on the choice of cycling as an alternative to other modes of transport, by taking into account the modal shift. The model takes into account the influence of the longitudinal gradient, link, and surface type of cycling routes on the distribution and demand for bicycle traffic. The results of our research allow us to assess the impact of planned cycling routes on the reduction in the volume of car traffic, which is crucial for reducing energy consumption and negative environmental impacts. Experiences from the application of the model in Gdynia suggest that the model provides a strong basis to support mobility planning and monitoring processes in cities worldwide. Cities should take into account the methods proposed in this paper when planning the development of their transport systems.

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Section: Measures Influencing Travel Demand and Modal Shift In Cyclingmentioning

confidence: 99%

Bicycle Traffic Model for Sustainable Urban Mobility Planning

2021

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“…Jin-Shyan Lee et al used the user-adaptive mechanism fuzzy logic controller (UAFLC) for rider safety and comfort in urban regions. These controller inputs are velocity error, pedal, and motor torque [43].…”

Section: Introductionmentioning

confidence: 99%

A novel e‐bike energy management for improvement of the rider metabolism

Ebnealipour

Masih‐Tehrani

Nazemian

2023

IET Intelligent Trans Sys

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In this study, a switch controller manages the power‐sharing between the battery and human mode to improve the rider's metabolism and manage the battery SOC. The main idea is to optimize this power source switching element for changing the status to reach a trade‐off between lack of tiredness and keeping the SOC high. Calorie burning is closely related to the rider's physical characteristics. In this paper, these parameters are investigated to calculate calorie burning. When the electric‐powered mode is activated, the SOC level comes down. When the human‐powered mode is activated, the human power source provides energy. The model converts the bicycle speed into the rider's heart rate and then changes it into burned calories based on some equations. These equations are obtained by poly fitting after experiments. This optimization causes 33.5% and 50% burning calorie reduction in Cleaveland and Portuguese driving cycles. Also, in the Portuguese driving cycle, the battery usage percentage decreases 39.56% from to 20.54% after optimization; therefore, the burning calorie decreases 265.84 Kcal to 176.83 Kcal.

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“…The higher efficiency of E-bike systems tends to be achieved at the cost of the riding experience, including considerations such as comfortability, safety, and jerk motions, which are typically underrated in the E-bike industry [16]. While previous studies have considered the comfortability (riding velocity stability) and safety (riding acceleration stability) of E-bikes in improving the riding experience [17][18][19], these studies do not appear to have either taken energy consumption into account or ensured the riding efficiency of E-bikes.…”

Section: Introductionmentioning

confidence: 99%

A Power Assistant Algorithm Based on Human–Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes

et al. 2021

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As robots are becoming more accessible in our daily lives, the interest in physical human–robot interaction (HRI) is rapidly increasing. An electric bicycle (E-bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E-bike in close proximity. Most commercially available E-bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E-bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E-bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E-bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm.

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Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes

Cited by 16 publications

References 19 publications

Bicycle Traffic Model for Sustainable Urban Mobility Planning

Bicycle Traffic Model for Sustainable Urban Mobility Planning

A novel e‐bike energy management for improvement of the rider metabolism

A Power Assistant Algorithm Based on Human–Robot Interaction Analysis for Improving System Efficiency and Riding Experience of E-Bikes

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