Modeling of hybrid control systems using the DEVSLib Modelica library

Sanz, Victorino; Urquía, Alfonso; Cellier, François E.; Dormido, Sebastián

doi:10.1016/j.conengprac.2010.11.014

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…(Fig. 4) The hyboost controller model includes several function modules such as electronic idle, the brake energy recovery, power train torque demand management and the intelligent charging and discharging module (Crolla et al, 2012;Zou et al, 2015;Sanz et al, 2012). The electronic idle determines whether the engine should be dragged by the BSG motor to maintain the idle speed.…”

Section: Component Modelsmentioning

confidence: 99%

Modelica-Based Modeling and Application Framework on the Hybrid Electric Vehicles

Liu

Chen

Zhao³

et al. 2019

Linköping Electronic Conference Proceedings

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In order to meet low-emissions criteria outlined in Chinese regulations, which require a progressively increasing percentage of automobiles to be ultralow or zero emissions, in this paper a sort of light hybrid electric drivetrain is studied and modeled in detail using Modelica. An application framework is also designed to improve the usability and the efficiency of the models. Performance of the whole vehicle and some key components are analyzed.. Comparison between simulation results and experiment results is performed, which validates the effectiveness of the models. Based on the comparison, we conclude that, the methods presented in this paper can support a rapid design of hybrid electric vehicles and further optimization.

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Section: Component Modelsmentioning

confidence: 99%

Modelica-Based Modeling and Application Framework on the Hybrid Electric Vehicles

Liu

Chen

Zhao³

et al. 2019

Linköping Electronic Conference Proceedings

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“…Ni Li, has compared few simulation frameworks and the individual frameworks like UML & Modelica [1] and elaborated by Garson et al [2][3][4] . In recent years, different frameworks and tools have been leveraged for process modeling & simulation objectives, such as MATLAB, Aspen etc.…”

Section: Comparitive Study Of Related Workmentioning

confidence: 99%

Process Simulation Framework Design and Validation with Grinding Systems

Sridevi*

2019

IJEAT

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Process Industrial & their complex control operations require comprehensive simulation software systems for modeling plant dynamics and analyzing gaps and to achieve optimal control efficiency. These models support in training plant engineers on various process scenarios in controlled pseudo real time environment. Higher degree of model designing customization, flexibility, scalability, cost efficiency and domain agnostic solution features, are the desired characteristics of any process simulation framework. This paper formulates prototype design of an integrated generic process simulator platform and its components, enabling intuitive and interactive representation of intelligent model formats, facts, knowledge, rules & behaviors. The benefits range from safer process training, analysis / synthesis of controller models; control optimization and theoretical learning. The simulation performance of proposed framework is verified through material fineness control modeling of rotary vertical grinding mill. The adaptive leaning features, with hybrid prediction model validations results in the simulation accuracy and results are compared with prevalent systems.

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“…Different research teams [8][9][10]20,21,[23][24][25][26][27] have shown that discrete event methods (in general) and DEVS (in particular), present several advantages: Computational time reduction: for a given accuracy, the number of calculations can decrease. Hierarchical modular modeling, which improves verification and validation, also enhancing reusability.…”

Section: The Devs Formalismmentioning

confidence: 99%