Model-based design and testing for electric vehicle driveability analysis

Ciceo, Sebastian; Mollet, Yves; Sarrazin, Mathieu; Gyselinck, Johan; Auweraer, Herman Van der; Marțiș, Claudia

doi:10.1109/eeeic.2016.7555884

Cited by 16 publications

(7 citation statements)

References 7 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, the OEMs prefer to use Model-based-Development (MBD) techniques for PE components, which introduce HiL tests for the EVs traction subsystems [19], [20]. At the design stage, the HiL testbench can be utilized to verify the effect of the inclusion of Wide Band-Gap semiconductors (WBGs), such as Silicon-Carbide (SiC) and Gallium Nitride (GaN), in the EVs of the PE converters [19], [21].…”

Section: Necessity Of Hardware-in-the Loop Testingmentioning

confidence: 99%

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

et al. 2020

View full text Add to dashboard Cite

Automotive Original Equipment Manufacturers (OEMs) require varying levels of functionalities and model details at different phases of the electric vehicles (EV) development process, with a trade-off between accuracy and execution time. This article proposes a scalable modelling approach depending on the multi-objective targets between model functionalities, accuracy and execution time. In this article, four different fidelity levels of modelling approaches are described based on the model functionalities, accuracy and execution time. The highest error observed between the low fidelity (LoFi) map-based model and the high fidelity (HiFi) physics-based model is 5.04%; while, the simulation time of the LoFi model is ~10 4 times faster than corresponding one of the HiFi model. A detailed comparison of all characteristics between multi-fidelity models is demonstrated in this paper. Furthermore, a dSPACE SCALEXIO Hardwarein-the-Loop (HiL) testbench, equipped with a minimal latency of 18μsec, is used for real-time (RT) model implementation of the EV's HV DC/DC converter. The performance of the entire HiL setup is compared with the Model-in-the-Loop (MiL) setup and the highest RMSE is limited to 0.54 among the HiL and MiL results. Moreover, the accuracy (95.7%) of the passive component loss estimation is verified through the Finite Element Method (FEM) software model. Finally, the experimental results of a full-scale 30-kW SiC DC/DC converter prototype are presented to validate the accuracy and correlation between multi-fidelity models. It has been observed that the efficiency deviation between the hardware prototype and multi-fidelity models is less than 1.25% at full load. Furthermore, the SiC Interleaved Bidirectional Converter (IBC) prototype achieves a high efficiency of 98.4% at rated load condition. INDEX TERMS DC/DC interleaved converter, EV, efficiency, electro-thermal modelling, multi-fidelity models, optimization, scalable modelling, Hardware-in-the-loop, and wide-bandgap technology.

show abstract

Section: Necessity Of Hardware-in-the Loop Testingmentioning

confidence: 99%

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Hence, detailed experimental studies related to this objective have been conducted. In these studies, experiments have been conveyed in three different testing environments: 1) simulation platforms [1][2][3]; 2) model-based test platforms [4,5]; and 3) real vehicle tests [6][7][8][9][10]. Furthermore; fuzzy logic [11], neural network method [12] and machine learning [6] are some of the approaches that have been applied for the classification of drivers.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Approach on the Evaluation of Driving Styles and Investigation of Drivability Calibration Effects

Aksit

Yavuz

Şen

2022

Gazi University Journal of Science

View full text Add to dashboard Cite

Increased customer expectations lead the automobile manufacturers to develop innovative solutions, such as mode selection functions that provide different performance and comfort settings for the drivers. Almost all brands have different types of driving modes installed on their vehicles, such as sport mode, economy mode, off-road mode, etc. In the current technology, the mode selection is manually done by the driver. Thus, no effort is taken to match the driver style with available driving modes. However, driving mode selection should be done through an intelligent system such as vehicle control unit, in order to optimize customer expectations related to vehicle performance, driving comfort, and fuel consumption. This can be achieved by the analysis of all drivability maneuvers during any driving cycle. Based on the results of these analyses, drivability calibration settings of the vehicle can be adjusted depending on driver behaviors. In addition, fuel consumption can be improved using suitable calibration for each driver type. In this study, an experimental investigation is carried out in which vehicle data is collected for eleven different drivers at three different drivability calibrations. Furthermore, fuzzy logic algorithms are utilized in order to distinguish the driver characteristics. First, data from nine drivers are used in order to train the fuzzy logic approach. Then, the trained fuzzy logic scheme is used to assess the characteristics of two other drivers, who were left out in the training data set. Hence, it is aimed to obtain an intelligent prediction procedure that can estimate the characteristics of a driver based on their driving styles.

show abstract

“…Due to increased energy demand, the usage of fuel oil has globally increased, with a lot of fuel oil usage going to electric power generation and internal combustion engine vehicles (ICEVs). Electric Vehicles (EVs) are the optimum substitute to mitigate the effects of ICEVs on the planet due to the scarcity of fuel energy security and environmental matters (Ciceo et al, 2016;Hasanzadeh et al, 2012). The notable power source in ordinary electric vehicles is the battery, The older recharging schemes for EVs take longer to charge the battery of EVs .…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Dynamic Wireless Power Transfer Technique for Electric Vehicles

Kumar,

Rathore,

Yadav

2022

INTERNATIONAL CONFERENCE ON POWER ELECTRONICS &Amp; SUSTAINABLE DEVELOPMENT (ICPESD)

View full text Add to dashboard Cite

Creating a green and sustainable ecosystem needs to reduce carbon emissions from our plane, this may possible by reducing the usage of fossil fuel-powered vehicles and moving towards electric vehicles (EVs). EVs can be charged either in stationary mode or dynamic mode. By the static charging mode, fully charged EVs can’t travel too far. To extend its range, it will need more batteries. EVs have the possibility of using dynamic wireless charging to enhance their range and minimize the necessity of large battery packs. This paper reviewed a comparison of the different WPT techniques, transmitter coil designing for the dynamic wireless power transfer (DWPT). The air gap between the transmitter coil and receiver coil is responsible for the variation in coupling coefficient and also for the mutual inductance, which is represented. When the EV is in motion on the transmitter track then the EV’s speed and misalignment affect the DWPT system, which is also reviewed. This paper included Modelling and simulation of dynamic wireless power transfer in MATLAB/SIMULINK work and power transfer calculation for EV with 10KWh battery pack in the dynamic condition are also represented.

show abstract

Model-based design and testing for electric vehicle driveability analysis

Cited by 16 publications

References 7 publications

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

Scalable Modeling Approach and Robust Hardware-in-the-Loop Testing of an Optimized Interleaved Bidirectional HV DC/DC Converter for Electric Vehicle Drivetrains

A Fuzzy Logic Approach on the Evaluation of Driving Styles and Investigation of Drivability Calibration Effects

Modeling and Simulation of Dynamic Wireless Power Transfer Technique for Electric Vehicles

Contact Info

Product

Resources

About