Design and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool Integrated in a Complete Workflow to Study Electric Devices

Berr, F. Le; Abdelli, Abdenour; Postariu, Dragos; Benlamine, Raouf

doi:10.2516/ogst/2012029

Cited by 26 publications

(7 citation statements)

References 26 publications

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“…In this system modelling approach, electric machines are essentially described through efficiency maps obtained using an IFPEN inhouse software (EMTool) [Le Berr 2012]. EMTool offers the capability to size and to characterize EM from basic requirements (maximum power and torque, maximum motor speed, input voltage).…”

Section: Electric Machinesmentioning

confidence: 99%

Energy Efficiency Evaluation of a Plug-in Hybrid Vehicle under European Procedure, Worldwide Harmonized Procedure and Actual Use

Badin

Berr

Castel

et al. 2015

WEVJ

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This paper describes a two-fold approach carried out in collaboration between IFPEN and the French ADEME with the aim to evaluate a Plug-in Hybrid vehicle according to its conditions of use. The vehicle considered, an Opel Ampera, has been widely tested on a chassis dyno and simulated using IFPEN LMS.IMAGINE.Lab AMESim® platform. The software principles and its validation on different specific cases, thanks to experimental results, are described. The PHEV simulation tool is used to evaluate the influence of two European standard procedures and the influence of their parameters on the weighed CO 2 emission.

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Section: Electric Machinesmentioning

confidence: 99%

Energy Efficiency Evaluation of a Plug-in Hybrid Vehicle under European Procedure, Worldwide Harmonized Procedure and Actual Use

Badin

Berr

Castel

et al. 2015

WEVJ

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“…A 6-gear AMT layout is considered for the CV, and the P2 HEV, with corresponding gear-shift logic, developed the following approach illustrated in [33]. Efficiency maps and operational data of interior permanent magnet synchronous machines were generated for the EMs according to the corresponding methodology implemented in Amesim ® software and described in [59]. Battery SOC dependent parameters (i.e., voltage and resistance) related to 42 kWh and 2 kWh high-voltage battery packs were derived for the BEV and the HEV layouts, respectively.…”

Section: Resultsmentioning

confidence: 99%

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Anselma

2021

Energies

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Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

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“…The parameters for the motor model was estimated, using least squares, from a map of the power losses af a motor that was produced by a motor design tool developed in (Le Berr et al, 2012). The parameters for the battery was taken from (Energy, 2016).…”

Section: Electricsmentioning

confidence: 99%

Modelling and Validation of Hybrid Heavy Duty Vehicles with Exhaust Aftertreatment Systems

Holmer

Eriksson

2017

Linköping Electronic Conference Proceedings

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A model of a hybrid electric vehicle including an aftertreatment system is developed and validated. The model describes a vehicle with the same parallel hybrid architecture that is commonly used in commercial heavy duty vehicles and is validated using data gathered from vehicles during real world driving. The goal with the model is to describe the main dynamics of the system and give accurate estimations of fuel consumption and emissions while at the same time keeping simulation times short. The model consists of several sub components, out of which the most important ones are: combustion engine, electric motor, aftertreatment system, driveline, and vehicle chassis. The different components are interchangeable making it possible for the user to change specific components to make the model fit their needs.

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Design and Optimization of Future Hybrid and Electric Propulsion Systems: An Advanced Tool Integrated in a Complete Workflow to Study Electric Devices

Cited by 26 publications

References 26 publications

Energy Efficiency Evaluation of a Plug-in Hybrid Vehicle under European Procedure, Worldwide Harmonized Procedure and Actual Use

Energy Efficiency Evaluation of a Plug-in Hybrid Vehicle under European Procedure, Worldwide Harmonized Procedure and Actual Use

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Modelling and Validation of Hybrid Heavy Duty Vehicles with Exhaust Aftertreatment Systems

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