Automatic Driving Cycle Generator for Electric Vehicles

Desreveaux, Anatole; Bouscayrol, Alain; Trigui, Rochdi; Castex, Élodie

doi:10.1109/vppc.2018.8605019

Cited by 3 publications

(6 citation statements)

References 6 publications

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“…Energetic Macroscopic Representation (EMR) is used to interconnect the models [29]. A more detailed description can be found in [28].…”

Section: Forward Simulation Of the Studied Ev A Modeling Of The Studi...mentioning

confidence: 99%

“…The driving cycle generator was developed initially for a subway application [27] and has been extended to an automotive application [28]. Only the main principle is described in this section.…”

Section: Automatic Driving Cycle Generator a Algorithm Of The Driving...mentioning

confidence: 99%

“…Driving cycle generators have been developed to select independently the characteristics of the cycles, mainly the maximal velocity [24]- [28]. The driving cycle generators have been used for autonomous vehicle [26] or for energy consumption evaluation of EVs [24]- [25].…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated that this method lead to a more accurate estimation of energy consumption. This method has been extended to a 2-seat electric vehicle for very simple trips without stops and traffic lights [28].…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this paper is to determine the effect of the maximal velocity and acceleration of a driving cycle on the energy consumption of an EV. To that end, the forward approach developed in [27]- [28] couples a traction simulation model with a specific driving cycle generator. A Renault Zoe and more realistic trips are now considered for validation of the developed method and for the study of the impact of the velocity profile.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

Desreveaux

Bouscayrol

Trigui³

et al. 2019

IEEE Trans. Veh. Technol.

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The driving range of electric vehicles is an important issue. The velocity profile can have an important impact on their energy consumption and thus their driving range. In this paper, a commercial electric vehicle is studied. The model of its traction subsystems is developed using a forward approach. A specific driving cycle generator is then coupled with this model. A feedback from the model to the generator allows for recalculation of the velocity reference in case of a limitation due to the electrical drives, for a more accurate estimation of energy consumption. Different trips are then studied with this method. The maximal velocity has a strong impact on the energy consumption while the maximal acceleration has a low impact.

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“…Energetic Macroscopic Representation (EMR) is used to interconnect the models [29]. A more detailed description can be found in [28].…”

Section: Forward Simulation Of the Studied Ev A Modeling Of The Studi...mentioning

confidence: 99%

Section: Automatic Driving Cycle Generator a Algorithm Of The Driving...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

Desreveaux

Bouscayrol

Trigui³

et al. 2019

IEEE Trans. Veh. Technol.

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Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Gurusamy,

Ashok,

Mason

2023

IEEE Access

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Electric mobility is getting prominence in modern transportation as government policies aim to reduce greenhouse gas (GHG) emissions. In the context of real-time testing, numerical modelling and simulation of electric vehicle (EV) powertrains play a vital role in developing an efficient electric powertrain and charging infrastructure as it consumes less time and cost. Also, it enhances the overall performance by optimizing the size and configuration of the EV powertrain under different driving conditions. Thus, the review paper explores the different modelling approaches used for estimating the energy consumption (EC) and driving range (DR) initially. Further, the vehicle analytical model is discussed in detail with sub-models of powertrain components and vehicle dynamics, which have the mathematical correlation related to power losses and energy flow. Additionally, the necessity, development process, characterization and accuracy of localized driving cycles (DCs) and commonly used driver controller models for EVs are critically elaborated. Further, the impact of various influential input parameters such as vehicle parameters and driving conditions on EV performance characteristics is analyzed along with different improvisation methods utilized in the existing literature. From this extensive review, it can be concluded that simulation results by using an analytical vehicle model have good accuracy with chassis dynamometer-based testing and it can be used for optimizing the size and configuration of EV powertrain components under different scenarios. Finally, the present status and future research required in the field of EV powertrain development through modelling and simulation are summarized to extend the application of EVs in transportation sectors.

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Energy-efficient electrification of public transportation fleets based on generic driving cycles for the city of Cairo, Egypt

Asfoor

Ali

2021

2021 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Automatic Driving Cycle Generator for Electric Vehicles

Cited by 3 publications

References 6 publications

Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

Impact of the Velocity Profile on Energy Consumption of Electric Vehicles

Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Energy-efficient electrification of public transportation fleets based on generic driving cycles for the city of Cairo, Egypt

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