Virtual Test Rig Research Articles

A virtual test rig for performance and efficiency assessment of heat pump thermal management system for battery electric vehicles

In a Battery Electric Vehicle (BEV), the optimization of its thermal state plays a crucial role in the achievement of relevant vehicle targets such as range, passenger comfort, and recharging times, as well as varying external ambient conditions. Therefore, selecting the most proper solution between the many possible architectures to manage the different thermal loads of a BEV needs the support of a robust simulation platform. In such a framework, a comprehensive numerical twin of an electric city car was developed in the GT-Suite environment and validated against experimental data. Through that, the promising performance of a Heat Pump (HP) based thermal layout has been demonstrated, against a standard Air Conditioning system integrated with a PTC heater, on a wide range of external ambient temperatures (reaching up to +20 % driving range improvement in cold conditions). The present layout proposed a sustainable alternative to the most common R134a as working fluid (i.e., propane) and the integration of multiple heat sources. In addition, different battery preconditioning strategies have been tested, tailored to the selected real-world mission profiles, obtaining a remarkable gain in terms of driving range for the HP system in cold conditions (up to 6 % at −10 °C and 4 % at −20 °C).

Development of a deep Q-learning energy management system for a hybrid electric vehicle

In recent years, Machine Learning (ML) techniques have gained increasing popularity in several fields thanks to their ability to find hidden and complex relationships between data. Their capabilities for solving complex optimization tasks have made them extremely attractive also for the design of the Energy Management System (EMS) of electrified vehicles. Among the plethora of existing techniques, Reinforcement Learning (RL) algorithms have unprecedented potential since they can self-learn by directly interacting with the external environment through a trial-and-error procedure. In this paper, a Deep Q-Learning (DQL) agent, which exploits Deep Neural Networks (DNNs) to map the state-action pair to its value, was trained to reduce the CO2 emissions of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The proposed methodology was tested on a virtual test rig of the investigated vehicle while operating on a charge-sustaining logic. A sensitivity analysis was performed on the reward to test the capabilities of different penalty functions to improve the fuel economy while guaranteeing the battery charge sustainability. The potential of the proposed control strategy was firstly assessed on the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) and benchmarked against a Dynamic Programming (DP) optimization to evaluate each reward. Then the best agent was tested on a wide range of type-approval and Read Driving Emission (RDE) scenarios. The results show that the best-performing agent can reach performance close to the DP reference, with a limited gap (7 %) in terms of CO2 emissions.

The method of virtual bench tests conducting to analyze the vehicle technical characteristics compatibility to detect and prevent the risk of resonant phenomena in the sprung cab

BACKGROUND: A relevant issue when designing vehicles with multiple levels of suspension systems is collaborative operation of these systems, which ensures the required vehicle dynamics and smoothness parameters, as well as the absence of risk for the occurrence of resonance phenomena. The vehicle cab is a dynamic system with 6 degrees of freedom, so its oscillatory motions have a complex spatial character with the oscillation energies overflow from one direction to another. The solving of problems of the sprung cab dynamics should be conducted in a spatial nonlinear formulation, which makes it possible to take into account the phenomena of redistribution of oscillation energy between the directions of space. This approach can be put into operation with simulation modeling methods through creating a spatial virtual rig of the cab with a suspension system and modeling its dynamics with the application of appropriate external inputs.
 AIM: Development of the method of using virtual tests to analyze the compatibility of technical characteristics of the cab suspension system, the frame system and the primary vehicle suspension systems to detect and prevent the risk of resonant phenomena in the cab.
 METHODS: Building a virtual test rig of a vehicle cab as a multibody dynamics system of connected bodies in space, mounted at the movable vehicle frame with spring-dampers. Virtual tests are conducted by simulation modeling methods according to the method for analyzing nonlinear phenomena when spatial resonances occur in the secondary suspension system of a vehicle cab.
 RESULTS: The method of vehicle cab virtual tests conducting for analyzing oscillatory processes occurring in the cab, including resonances, using a virtual test rig in a multibody dyamics software package.
 CONCLUSION: The developed method ensures an ability to take measures to detect and prevent the risk of spatial resonance phenomena in the vehicle cab at all stages of the components design and the cab suspension systems study.

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction

The energy management of a Hybrid Electric Vehicle (HEV) is a global optimization problem, and its optimal solution inevitably entails knowing the entire mission profile. The exploitation of Vehicle-to-Everything (V2X) connectivity can pave the way for reliable short-term vehicle speed predictions. As a result, the capabilities of conventional energy management strategies can be enhanced by integrating the predicted vehicle speed into the powertrain control strategy. Therefore, in this paper, an innovative Adaptation algorithm uses the predicted speed profile for an Equivalent Consumption Minimization Strategy (A-V2X-ECMS). Driving pattern identification is employed to adapt the equivalence factor of the ECMS when a change in the driving patterns occurs, or when the State of Charge (SoC) experiences a high deviation from the target value. A Principal Component Analysis (PCA) was performed on several energetic indices to select the ones that predominate in characterizing the different driving patterns. Long Short-Term Memory (LSTM) deep neural networks were trained to choose the optimal value of the equivalence factor for a specific sequence of data (i.e., speed, acceleration, power, and initial SoC). The potentialities of the innovative A-V2X-ECMS were assessed, through numerical simulation, on a diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. A virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against a wide database of experimental data. The simulations proved that the proposed approach achieves results much closer to optimal than the conventional energy management strategies taken as a reference.

Development of a neural network-based energy management system for a plug-in hybrid electric vehicle

The high potential of Artificial Intelligence (AI) techniques for effectively solving complex parameterization tasks also makes them extremely attractive for the design of the Energy Management Systems (EMS) of Hybrid Electric Vehicles (HEVs). In this framework, this paper aims to design an EMS through the exploitation of deep learning techniques, which allow high non-linear relationships among the data characterizing the problem to be described. In particular, the deep learning model was designed employing two different Recurrent Neural Networks (RNNs). First, a previously developed digital twin of a state-of-the-art plug-in HEV was used to generate a wide portfolio of Real Driving Emissions (RDE) compliant vehicle missions and traffic scenarios. Then, the AI models were trained off-line to achieve CO2 emissions minimization providing the optimal solutions given by a global optimization control algorithm, namely Dynamic Programming (DP). The proposed methodology has been tested on a virtual test rig and it has been proven capable of achieving significant improvements in terms of fuel economy for both charge-sustaining and charge-depleting strategies, with reductions of about 4% and 5% respectively if compared to the baseline Rule-Based (RB) strategy.

FRF-based non-simultaneous real-time hybrid testing of multiple subsystems with moderate nonlinearities

The existing cyber physical testing methods have been extended in a recent publication by non-simultaneous real-time hybrid testing, see Witteveen et al. [1]. There, an overall system is decomposed into two subsystems, one experimental and one numerical. The subsystems are loaded by certain inputs and simulated or tested separately in loops until compatibility conditions are fulfilled. If not, an input update based on the error of the kinematic compatibility in the frequency domain is computed for the next loop run. Each single test run is independent from the numerical model and therefore both is possible: Slow speed and real-time testing. In this work, the generalization to a systematic approach for an arbitrary number of subsystems is presented. The subsystems can be of numerical or experimental nature. While in the last publication the subsystems had to be tested one after the other within a loop run, they can be processed totally independently from each other in this generalized method. Further, both compatibility conditions (kinematic and cutting force) are considered for the update of the input signals. Finally, approximate models of the tested components can be considered in the numerical subsystems to improve convergence. The update for the inputs is again computed in the frequency domain based on FRFs of the isolated subsystems. The theory section is followed by two examples. The first one is of academic nature, and it is demonstrated that in case of convergence, all subsystems behave as they were part of one assembled overall system. The second one is a full vehicle hybrid testing where two shock absorbers are considered as real hardware while the vehicle is a multibody simulation model on a virtual four-poster test rig.

Development of a virtual methodology based on physical and data-driven models to optimize engine calibration

Virtual engine calibration exploiting fully-physical plant models is the most promising solution for the reduction of time and cost of the traditional calibration process based on experimental testing. However, accuracy issues on the estimation of pollutant emissions are still unresolved. In this context, the paper shows how a virtual test rig can be built by combining a fully-physical engine model, featuring predictive combustion and NOx sub-models, with data-driven soot and particle number models. To this aim, a dedicated experimental campaign was carried out on a 1.6 liter EU6 diesel engine. A limited subset of the measured data was used to calibrate the predictive combustion and NOx sub-models. The measured data were also used to develop data-driven models to estimate soot and particulate emissions in terms of Filter Smoke Number (FSN) and Particle Number (PN), respectively. Inputs from engine calibration parameters (e.g., fuel injection timing and pressure) and combustion-related quantities computed by the physical model (e.g., combustion duration), were then merged. In this way, thanks to the combination of the two different datasets, the accuracy of the abovementioned models was improved by 20% for the FSN and 25% for the PN. The coupled physical and data-driven model was then used to optimize the engine calibration (fuel injection, air management) exploiting the Non-dominated Sorting genetic algorithm. The calibration obtained with the virtual methodology was then adopted on the engine test bench. A BSFC improvement of 10 g/kWh and a combustion reduction of 3.0 dB in comparison with the starting calibration was achieved.

Key Technologies of Physical and Virtual Test Rig for Railway Freight Car body

On the one hand, considering that the traditional fatigue method of railway freight cars is based on damage as a parameter, the influence of stress waveform cannot be considered. On the other hand, physical experiments have the characteristics of lag, long period, and high cost. The full-scale physical test and virtual test of car body are carried out. First of all, the data processing method of small deletion and the inverse problem load acquisition method based on data to data are proposed. Secondly, the dynamic stress calculation method with the bench as the boundary is proposed. Finally, taking the obtained load as the input of the physical and virtual bench, a new fatigue test method for simulating the running attitude of the car body line is completed. The acceleration RMS error of the C70E gondola body is less than 6%, the stress RMS is less than 13%, and the equivalent mileage is 3.125 million highway test results show that the car meets the life requirements of the car body. The inverse problem analysis results of virtual and physical tests are basically consistent, and the study of this method provides a basis for improving the fatigue reliability of freight car bodies.

Disturbance observer-based adaptive position control for a cutterhead anti-torque system

To conveniently replace worn cutterhead tools in complicated strata, a novel cutterhead attitude control mechanism was recently designed. Meanwhile, the mechanism also causes an engineering problem of how to control a matching cutterhead anti-torque system (CATS) effectively, which is used to prevent a drive box of the cutterhead from rotation during a complex excavation process. In this paper, a disturbance observer-based adaptive position controller is proposed for the CATS. The proposed method presents a nonlinear adaptive controller with adaptation laws to compensate for the unknown time-varying load torque and damping uncertainty in the system. Based on the disturbance observer method and sliding mode control, an asymptotically stable controller proven by Lyapunov theory is constructed using the back-stepping technique. In addition, a virtual test rig based on MATLAB and AMESim co-simulation is built to verify the validity of the proposed controller. The simulation results show that the proposed method has good performance for tracking tasks in the presence of uncertainties compared with PID control. Together, the data support targeting disturbance observer-based adaptive position control as a potential control strategy for cutterhead anti-torque systems.

Low cycle fatigue properties assessment for rotor steels with the use of miniaturized specimens

The article deals with the development of advanced techniques of low cycle fatigue testing (LCF) using miniature test samples, which has the potential to be used to assess the degree of material degradation of large in-service components in the power industry. For this purpose, the results of a wide portfolio of test samples geometries have been investigated. Using the advanced techniques involving a special sampling device, a DIC virtual extensometer and test rig, a new possibility in strain-controlled LCF testing is presented. The results indicated that cyclic-strain and strain-life curves of proposed minisample geometries are almost of the same shape for both investigated rotor steels. Also the cyclic parameters obtained indicate a good consistency of results despite of a wide range of sample volumes and utilization of two different strain-control methods.

A Methodology for the Reverse Engineering of the Energy Management Strategy of a Plug-In Hybrid Electric Vehicle for Virtual Test Rig Development

A Methodology for the Reverse Engineering of the Energy Management Strategy of a Plug-In Hybrid Electric Vehicle for Virtual Test Rig Development

Enhancing Hierarchical Multiscale Off-Road Mobility Model by Neural Network Surrogate Model

Abstract A hierarchical multiscale off-road mobility model is enhanced through the development of an artificial neural network (ANN) surrogate model that captures the complex material behavior of deformable terrain. By exploiting the learning capability of neural networks, the incremental stress and strain relationship of granular terrain is predicted by the ANN representative volume elements (RVE) at various states of the stress and strain. A systematic training procedure for ANN RVEs is developed with a virtual tire test rig model for producing training data from the discrete-element (DE) RVEs without relying on computationally intensive full vehicle simulations on deformable terrain. The ANN surrogate RVEs are then integrated into the hierarchical multiscale computational framework as a lower-scale model with the scalable parallel computing capability, while the macroscale terrain deformation is described by the finite element (FE) approach. It is demonstrated with several numerical examples that off-road vehicle mobility performances predicted by the proposed FE-ANN multiscale terrain model are in good agreement with those of the FE-DE multiscale model while achieving a substantial computational time reduction. The accuracy and robustness of the ANN RVE for fine-grain sand terrain are discussed for scenarios not considered in training datasets. Furthermore, a drawbar pull test simulation is presented with the ANN RVE developed with data in the cornering scenario and validated against the full-scale vehicle test data. The numerical results confirm the predictive ability of the FE-ANN multiscale terrain model for off-road mobility simulations.

A novel approach for experimental identification of vehicle dynamic parameters

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

Hardware and Virtual Test-Rigs for Automotive Steel Wheels Design

<div class="section abstract"><div class="htmlview paragraph">The aim of this paper is to study in deep the peculiar test-rigs and experimental procedures adopted to the fulfilment of the principal requirements of automotive steel wheels, in particular regarding fatigue damaging. In the discussion, the standard requirements, the OEM specifications and the dimensional and geometric tolerances are approached. As result of an increasingly necessity to improve the performance of the components, innovative virtual test benches are presented. Differently from their traditional precursors, virtual test-rigs give an extended view of the physical behaviour of the component as the possibility to monitor stress-strain distribution in deep. In the first section, the state of the art and the specifications are listed. Secondly, the adopted hardware test-rigs as the experimental tests are described in detail. In the third one, proposed virtual test-rig is discussed. Finally, an experimental to numerical results comparison is performed focusing on the obtainable details.</div></div>

Development of Geometrically Accurate Continuum-Based Tire Models for Virtual Testing

AbstractIn this paper, an approach based on the integration of computer-aided design and analysis (I-CAD-A) is used to develop new continuum-based finite element (FE) tire models for the small and large deformation analyses. Based on given tire specifications, the mechanics-based geometry/analysis absolute nodal coordinate formulation (ANCF) is used to define the tire geometry with the same degree of accuracy as B-splines and nonuniform rational B-spline (NURBS), widely used in the computer-aided design (CAD) systems. In the case of large deformations, the ANCF geometry can be used directly as the analysis mesh without the need for conversion or adjustments. In order to define the material parameters that characterize the ANCF tire composite structure, a virtual test rig is developed, and the tire calibration process is performed according to the standards defined by the Society of Automotive Engineers (SAE). In order to develop small-deformation models that can be used in the prediction of the tire frequencies and mode shapes, the ANCF position vector gradients are consistently written in terms of rotation parameters, leading to geometrically accurate floating frame of reference (FFR) finite elements, referred to as ANCF/FFR elements. Using this mechanics-based geometry/analysis approach, new geometrically accurate reduced-order tire models are systematically developed and used to define vibration equations for the prediction of the tire frequencies, which are verified using a commercial FE software. The element stiffness matrix is calculated using the general continuum mechanics approach (GCM), and the effectiveness of the strain split method (SSM) for locking alleviation is tested. The results obtained in this investigation show that the I-CAD-A tire modeling approach can be used to develop geometrically accurate tire models suited for the large-deformation multibody system (MBS) problems as well as for the prediction of the tire frequencies and mode shapes.

Driving Cycle and Elasticity Manoeuvres Simulation of a Small SUV Featuring an Electrically Boosted 1.0 L Gasoline Engine

<div class="section abstract"><div class="htmlview paragraph">In order to meet the CO<sub>2</sub> emission reduction targets, downsizing coupled with turbocharging has been proven as an effective way in reducing CO<sub>2</sub> emissions while maintaining and improving vehicle driveability. As the downsizing becomes widely exploited, the increased boost levels entail the exploration of dual stage boosting systems. In a context of increasing electrification, the usage of electrified boosting systems can be effective in the improvement of vehicle performances. The aim of this work is therefore to evaluate, through numerical simulation, the impact of different voltage (12 V or 48 V) electric superchargers (eSC) on an extremely downsized 1.0L engine on vehicle performance and fuel consumption over different transient manoeuvres. The virtual test rig employed for the analysis integrates a 1D CFD Fast Running Model (FRM) engine representative of a 1.0L state-of-the-art gasoline engine featuring an eSC in series with the main turbocharger, an electric network (12 V or 48 V), a six speed manual transmission and a vehicle representative of a B-SUV segment car. A preliminary assessment of the steady state performances of the 1.0L engine with the electrified dual boosting system with both 12 V and 48 V electric supercharger was performed. Then, the vehicle performances were evaluated by means of, on the one hand, vehicle elasticity manoeuvres for the performance assessment and, on the other hand, type approval and RDE driving cycles, for the fuel economy assessment. An evaluation of possible engine and vehicle hardware modifications was also carried out. In particular, the effect of a variation of the final drive ratio, the increase of the turbine size and the usage of a high efficiency engine concept (featuring an increased compression ratio from 10 to 12 and a late intake valve closing, exploiting the advantages of a Miller cycle) were investigated.</div></div>

In-Plane Flexible Ring Tire Model—Part 2: Parameterization

ABSTRACT The flexible ring tire model has recently gained significant attention in vehicle dynamics and analysis of road loads because it is able to capture the tire belt deflection under various driving conditions and compute more efficiently than the complex finite element tire model. This article presents the second part of the in-plane flexible ring tire model study with the recently developed flexible ring tire model to investigate several important aspects about tire model parameterization. First, we use the FTire® model in the MSC ADAMS/View® virtual test rig to generate five sets of spindle longitudinal and vertical loads on cleats with different heights, static loads, and speeds. These spindle loads are considered the “experimental data” in view of proving the accuracy of the commercial FTire® model that can accurately predict the spindle loads, especially in well-controlled test rigs. Next, one set of tire model parameters identified with a specific cleat test case is applied to other cleat test cases to predict the tire spindle forces, which are then compared with those corresponding experimental data. Similarly, this process is repeated for each cleat test case to yield different sets of parameters, respectively. Afterward, the predicted spindle loads are compared with the experimental data, respectively, based on SAE Standard J2812, and the predicted errors are assessed to determine which cleat test case is the best choice to identify parameters of the tire model. Finally, the effects of the belt point number and tread block number on the prediction accuracy and efficiency are discussed.

Sophisticated but quite simple contact calculation for handling tire models

Handling tire models like Pacejka (Tire and Vehicle Dynamics, 3rd edn., Elsevier, Amsterdam, 2012) or TMeasy (Rill in Proc. of the XV Int. Symp. on Dynamic Problems of Mechanics, Buzios, RJ, Brazil, 2013) consider the contact patch as one coherent plane. As a consequence, the irregularities of a rough road profile must be approximated by an appropriate local road plane that serves as an effective road plane in order to calculate the geometric contact point and the corresponding contact velocities. The Pacejka/SWIFT tire model employs a road enveloping model that generates the effective height and slope by elliptical cams. TMeasy just uses four representative road points for that purpose. In addition, TMeasy replaces the geometric contact point by the static contact point and shifts it finally to the dynamic contact point that represents the point where the contact forces are applied. In doing so, a rather sophisticated but still simple contact calculation is possible. Simulations obtained with a virtual tire test rig and fully nonlinear three-dimensional multibody system models of a motor-scooter and a passenger car demonstrate the potential of this contact approach.

A CFD-based Virtual Test-rig for Rotating Heat Exchangers

Rotating heat exchangers are used in steel industry, air conditioning and thermal power plants to pre-heat air used in steam generators or for waste heat recovery. Here we focus on a rotating heat exchanger on a so-called Ljungström arrangement operated in thermal power plants to pre-heat the air fed to the steam generators. In these devices the heat exchange between two fluids is achieved through a rotating matrix that gets in contact alternatively with the two fluid streams and acts as a thermal accumulator. To increase the heat capacity and the overall exchange surface, the rotating matrix is filled by a series of folded metal sheets. In the paper we de-scribe a methodology to account for the effects of the Ljungström in a virtual test-rig implemented in a Computational Fluid Dynamics environment. To this aim, a numerical model based on the work of Molinari and Cantiano was derived and implemented in the OpenFOAM library. RANS numerical results were compared with those of a mono-dimensional tool used by ENEL to design Ljungström heat exchangers and validated against available measurements in a real configuration of a thermal power plant.