Processor in the Loop for Testing Series Motor Four Quadrants Drive Direct Current Chopper for Series Motor Driven Electric Car

Arof, Saharul; Diyanah, N. H. N.; Yaakop, Nurazlin Mohd; Mawby, Philip; Arof, Hamzah

doi:10.1007/978-3-030-05621-6_5

Cited by 22 publications

(5 citation statements)

References 13 publications

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“…In order to design a novel topology of Four quadrants drive DC chopper for DC motor, considering driving and braking application of low consumption of EVs. Then they build the simulation model and verify the performance of the proposed method [49,50].…”

Section: Vehicle Designmentioning

confidence: 99%

“…The discussed EVs mainly utilize alternating current, and the research has drawn many people's attention since introducing highly efficient BDC runs at low voltage [49]. In order to design a novel topology of Four quadrants drive DC chopper for DC motor, considering driving and braking application of low consumption of EVs.…”

Section: Vehicle Modeling and Energy Flow Analysismentioning

confidence: 99%

See 1 more Smart Citation

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.

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Section: Vehicle Designmentioning

confidence: 99%

Section: Vehicle Modeling and Energy Flow Analysismentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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“…Based on the real-time sensor commands, the controller unit (processor in loop [PIL] simulation) is used to estimate the essential driver's brake torque in the vehicle. 192 Then, the brake controller is used to distribute the optimal brake torque between hydraulic and RBS. Also, it can estimate the vehicle operating state for maximum braking by observing the driver's request.…”

Section: Development Process Of Regenerative Braking Control Systemmentioning

confidence: 99%

“…During energy regeneration, the RBS model is used to calculate the driver's demand brake torque based on several sensor commands from the vehicle. Based on the real‐time sensor commands, the controller unit (processor in loop [PIL] simulation) is used to estimate the essential driver's brake torque in the vehicle 192 . Then, the brake controller is used to distribute the optimal brake torque between hydraulic and RBS.…”

Section: Development Process Of Regenerative Braking Control Systemmentioning

confidence: 99%

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Saiteja

Ashok

Wagh

et al. 2022

Intl J of Energy Research

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Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

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“…Este artículo aborda el problema de cómo implementar una simulación Processor In the Loop (de ahora en adelante PIL) (Arof, et al, 2019) enfocada en el ámbito de las turbinas eólicas. Específicamente, busca obtener un sistema de control (planta + controlador) que trabaje de una manera determinística y en tiempo real, ayudando al ingeniero en el desarrollo de controladores.…”

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Entorno PIL para la validación de controladores de turbinas eólicas basados en IEC-61131

Martínez-Inchusta,

Sierra García,

Santos

et al. 2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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Cada día los sistemas de control son más complejos; los controladores de turbinas eólicas son un claro ejemplo de ello. Esto conlleva una demanda creciente de nuevas técnicas de verificación y validación que minimicen los posibles riesgos durante la fase de implementación en el dispositivo final. Las pruebas MIL (Model In the Loop), SIL (Software In the Loop), PIL (Processor In the Loop) y HIL (Hardware In the Loop) se realizan en momentos específicos del desarrollo del producto con el objetivo de obtener un sistema de control robusto y confiable de cara a su integración en el mundo real. El método de validación PIL ejecuta el software de control en el hardware real y comunica éste con la planta virtual, todo ello en tiempo real y de forma determinística. Esta técnica de validación permite corroborar que el software de control se ejecuta dentro de los tiempos de procesado requeridos (valida el hardware) y que las salidas generadas son las correctas para llevar al sistema a su estado objetivo (valida el software de control). En este trabajo se ha demostrado la posibilidad de implementar un PIL mediante un controlador desarrollado en IEC- 61131 ejecutado en tiempo real basado en PC y una planta virtualizada bajo Simulink Desktop Real-Time®.

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Processor in the Loop for Testing Series Motor Four Quadrants Drive Direct Current Chopper for Series Motor Driven Electric Car

Cited by 22 publications

References 13 publications

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Entorno PIL para la validación de controladores de turbinas eólicas basados en IEC-61131

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