A Reinforcement-Learning-Based Assisted Power Management With QoR Provisioning for Human–Electric Hybrid Bicycle

Hsu, Roy Chaoming; Liu, Cheng-Ting; Chan, Din-Yuen

doi:10.1109/tie.2011.2141092

Cited by 49 publications

(39 citation statements)

References 27 publications

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“…In 2012, Hsu et al assisted with the power management of a "pedelec," or hybrid bicycle, using the Q-learning algorithm [12]. They quantified the pedelec's safety and comfort targets as the quality of riding and improved energy utilization in the battery.…”

Section: Simplex Algorithmsmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

199

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E nergy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field.

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Section: Simplex Algorithmsmentioning

confidence: 99%

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

et al. 2019

EEE Ind. Electron. Mag.

199

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“…The essential assisted motor power can be thus calculated from the forecasted pedal power under the preferred comfortable speed. A reinforcement learning based controller is proposed in (Hsu et al, 2012), where the riding comfort is achieved by maintaining a velocity comfort zone. This provides the advantage of reducing the instantaneous forward acceleration which can make the rider feel unsafe if sudden assisted power produced too much acceleration.…”

Section: Control Systemsmentioning

confidence: 99%

An Intelligent Control System for an Electrically Power Assisted Cycle (EPAC)

Dissanayake

Kannangara

Kulasekera

et al. 2018

American Journal of Engineering and Applied Sciences

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This paper is focused on proposing an enhanced controller for a hybrid drive mechanism in an Electrically Power Assisted Cycle (EPAC) to improve the battery energy utilization while maintaining the rider's physical comfort. A real time data acquisition system was set up on a conventional bicycle using sensors and interfacing modules to verify the operating parameters. A fuzzy logic based novel control algorithm was developed upon the acquired data, to overcome the limitations in proportional assist control aided EPACs. The fuzzy controller was implemented using a novel 'iControl' algorithm. The developed control algorithm was installed on the newly developed EPAC and practically implemented. The developed algorithm showed the capability to improve range via better energy utilization and maintain rider comfort at the same time.

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“…El precio del petroleo ha mostrado un crecimiento sostenido a partir del año 2016, lo cual se atribuye al agotamiento de las reservas a nivel mundial junto con la gran demanda, en particular por parte del sector energético y de la industria automotriz. Según la Academia Internacional de Energía (IEA) más del 57 % de la producción de petroleo anual es consumido por sis-temas de transporte, lo que a su vez contribuye en gran manera al incremento de CO 2 en el ambiente (Hsu et al, 2012), (IEA, 2019). Ante estos problemas, en losúltimos años los vehículos eléctricos (VE) han sido propuestos como una solución a los problemas energéticos y de contaminación ambiental.…”

Section: Introductionunclassified

Observador del par ejercido por el ciclista en bicicletas eléctricas con asistencia al pedaleo

González

Aligia

Pezzani

et al. 2020

Rev. iberoam. autom. inform. ind.

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<p>En este trabajo se propone un observador para estimar el par efectuado por un ciclista en el eje trasero de una bicicleta eléctrica cuya información es utilizada para aplicar una estrategia de asistencia al pedaleo que cumple con la normativa europea. Para ello, en primer lugar, se obtiene el modelo dinámico longitudinal de la bicicleta incluyendo la carga resistente. A partir de este modelo se propone un observador para la estimación del par del ciclista utilizando las mediciones disponibles en el control del motor eléctrico, donde la velocidad es estimada a partir de un algoritmo PLL. De esta manera, la estrategia de asistencia se puede aplicar sin la necesidad de sensores de par ni de posición/velocidad. Por último, para validar el observador propuesto, se presentan resultados experimentales utilizando una bicicleta eléctrica rodado 26 conducida por dos conductores de diferente peso.</p>

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A Reinforcement-Learning-Based Assisted Power Management With QoR Provisioning for Human–Electric Hybrid Bicycle

Cited by 49 publications

References 27 publications

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects

An Intelligent Control System for an Electrically Power Assisted Cycle (EPAC)

Observador del par ejercido por el ciclista en bicicletas eléctricas con asistencia al pedaleo

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