On the effect of multirate co-simulation techniques in the efficiency and accuracy of multibody system dynamics

González, Francisco Javier Miranda; Naya, Miguel Ángel; Luaces, Alberto; González, Manuel

doi:10.1007/s11044-010-9234-7

Cited by 69 publications

(44 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According with [22] due to the increasing demand of quality and performance for the actual vehicles, the traditional design approach based on a sequential design of the components can no longer be applied, therefore engineers need to model and simulate the dynamic response of the whole system, taking into account the simultaneous interaction phenomena between components.…”

Section: Dynamic Co-simulationmentioning

confidence: 99%

Vehicle Gear Shifting Co-Simulation to Optimize Performance and Fuel Consumption in the Brazilian Standard Urban Driving Cycle

Eckert¹,

Santiciolli²,

Costa³

et al. 2014

Anais Do XXII Simpósio Internacional De Engenharia Automotiva

View full text Add to dashboard Cite

The vehicle longitudinal dynamics is responsible for calculating the vehicle power consumption undergone a specific route, by means of the estimation of the forces acting on the system such as aerodynamic drag, rolling resistance, climbing resistance and the driver behavior. The gear shifting tactics influences the vehicle performance and fuel consumption because it changes the powertrain inertia and the engine speed. The literature presents gear-shifting strategies based on the engine power and torque as well as the fuel economy. The last tactics are difficult to determinate, because they depend on a large number of factors like vehicle speed, available transition ratios, engine efficiency, required acceleration, tire-ground traction limit and engine decoupling during the gear shifting process. This paper shows a study based on the Brazilian standard urban driving cycle NBR6601. As there are many factors involved in the vehicle behavior and also in the vehicle dynamics, it was developed an algorithm to optimize the gear shifting process: it makes a choice of the most adequate tactic for each cycle stretch. The analysis were performed by co-simulation between the multibody dynamics software Adams TM and Matlab/Simulink TM , in which is defined the vehicle power demand.

show abstract

Section: Dynamic Co-simulationmentioning

confidence: 99%

Vehicle Gear Shifting Co-Simulation to Optimize Performance and Fuel Consumption in the Brazilian Standard Urban Driving Cycle

Eckert¹,

Santiciolli²,

Costa³

et al. 2014

Anais Do XXII Simpósio Internacional De Engenharia Automotiva

View full text Add to dashboard Cite

show abstract

“…The actual model integration and calculation can then be executed in co-simulation (see further), or the system equations of one model can be embedded in those of the other model [22][23][24][25]. This situation is characteristic for applications such as vehicle dynamics, internal engine dynamics, aircraft control surfaces, satellite antennas, etc.…”

Section: Engineering Challenges For Mechatronic Vehicle Systemsmentioning

confidence: 99%

“…One may embed state equations with a description of the plant system (e.g. MBS or 1D model) into these of the control (or vice versa) to enable the use of one solver, or adopt a true co-simulation approach where each system part runs its own solver [22][23][24][25]. Figure 6 shows a summary of various approaches for the case of an MBS and a CACE (computer aided control engineering) model.…”

Section: Connecting Multi-physics System Engineering To Controls Engimentioning

confidence: 99%

Virtual engineering at work: the challenges for designing mechatronic products

Auweraer

Anthonis

Bruyne

et al. 2012

Engineering with Computers

View full text Add to dashboard Cite

The product race has become an innovation race, reconciling challenges of branding, performance, time to market and competitive pricing while complying with ecological, safety and legislation constraints. The answer lies in ''smart'' products of high complexity, relying on heterogeneous technologies and involving active components. To keep pace with this evolution and further accelerate the design cycle, the design engineering process must be rethought. The paper presents a mechatronic simulation approach to achieve this goal. The starting point is the current virtual prototyping paradigm that is widely adopted and that continues to improve in terms of model complexity, accuracy, robustness and automated optimization. Two evolutions are discussed. A first one is the extension to multi-physics simulation answering the design needs of the inherent multi-disciplinarity of ''intelligent'' products. Integration of thermal, hydraulic, mechanical, haptic and electrical functions requires simulation to extend beyond the traditional CAD-FEM approach, supporting the use of system, functional and perception models. The second evolution is the integration of control functions in the products. Where current industrial practice treats mechanical system design and control design as different design loops, this paper discusses their integration in a modelbased design process at all design stages, turning concepts such as software-in-the-loop and hardware-in-the-loop into basic elements of an industrial design approach. These concepts are illustrated by a number of automotive design engineering cases, which demonstrate that the combined use of perception, geometric and system models allows to develop innovative solutions for the active safety, lowemission and high-comfort performance of next-generation vehicles. This process in turn poses new challenges to the design in terms of the specification and validation of such innovative products, including their failure modes and fault-tolerant behaviour. This will imply adopting a modelbased system engineering approach that is currently already common practice in software engineering.

show abstract

“…The examples are an integrated simulation of electric power steering (EPS) and the dynamics behaviour of the vehicle 1 , evolving vehicle stability control logic for four-wheel drive hybrid electric vehicle with fuzzy control 2 , controlling a vehicle platooning system by interfacing automatic dynamic analysis of mechanical systems (ADAMS) and MATLAB 3 , simulation of multi-body dynamic model of truck [4][5][6][7] and simulating the dynamics of robots 8 .…”

Section: Introductionmentioning

confidence: 99%

Investigation on Semi-active Suspension System for Multi-axle Armoured Vehicle using Co-simulation

et al. 2017

View full text Add to dashboard Cite

The objective of the study is to evaluate the performance of various semi-active suspension control strategies for 8x8 multi-axle armoured vehicles in terms of comparative analysis of ride quality and mobility parameters during negotiation of typical military obstacles. Since the cost, complexity and time precludes realisation of actual system, co-simulation technique has been effectively implemented for this investigation. Co-simulation combines advanced virtual prototyping and control technology which offers a novel approach to investigate the dynamics of such complex system. The simulations for the integrated control system along with multi body model of the vehicle are carried out for the control strategies, viz. continuous sky hook control, cascade loop control and cascade loop with ride control and compared with passive suspension system. The vehicle with 8x8 configuration is run on the real world obstacle profiles, viz. step, trench, trapezoidal bump and corrugated road and the effect of control strategies on ride comfort, wheel displacement and ground reaction is presented. It is observed that cascade loop with ride control in semi-active mode offers better vehicle ride comfort while crossing the said obstacles. The improved performance parameters are achieved through stabilisation of heave, pitch and roll motions of the vehicle through outer loop and isolation of vehicle level uneven disturbances through the fuzzy logic controller employed in inner loop.

show abstract

On the effect of multirate co-simulation techniques in the efficiency and accuracy of multibody system dynamics

Cited by 69 publications

References 13 publications

Vehicle Gear Shifting Co-Simulation to Optimize Performance and Fuel Consumption in the Brazilian Standard Urban Driving Cycle

Vehicle Gear Shifting Co-Simulation to Optimize Performance and Fuel Consumption in the Brazilian Standard Urban Driving Cycle

Virtual engineering at work: the challenges for designing mechatronic products

Investigation on Semi-active Suspension System for Multi-axle Armoured Vehicle using Co-simulation

Contact Info

Product

Resources

About