Novel Classification of Control Strategies for Hybrid Electric Vehicles

Chrenko, Daniela; Gan, Shiyu; Gutenkunst, Christian; Kriesten, Reiner; Moyne, Luis Le

doi:10.1109/vppc.2015.7352985

Cited by 15 publications

(6 citation statements)

References 54 publications

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“…(iv) GT : GT is a study of strategic decision making. Specifically, it is the study of mathematical models of conflict and cooperation between intelligent rational decision‐makers [52]. Literature [53] applies GT to the energy management of HEV with the operation of HEV regarded as a non‐cooperative game between the driver and the power system.…”

Section: Classical Emss For Conventional Hev/phevmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

186

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Section: Classical Emss For Conventional Hev/phevmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

186

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“…A study of different HEV CsT is presented by Zhang [13] which consists of the CsTs based on rules and optimization. Chrenko et al [14] provided a classification work of different CsTs of HEV which is shown in Figure 2.…”

Section: Energy Managementmentioning

confidence: 99%

Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool

Gan

Chrenko²,

Kéromnès

et al. 2018

Energies

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Hybrid electric vehicles (HEVs) are very promising sustainable mobility solutions. Series, parallel and series-parallel (SP) seem to be three most promising architectures among the multitude of hybrid architectures, and it is possible to find them in a multi-applications such as the motorcycles, family-cars, hybrid city busses and sport cars. It is import to have a well configured model in order to develop the different control strategies (CsTs) for each application. Therefore, a multi-architecture/multi-application (MAMA) approach capable of identifying the most energy efficient hybrid architecture considering both the dimensions of key components: electric motor (EM), battery, internal combustion engine (ICE) and the optimal control is presented. Basis of the model is the energetic macroscopic representation (EMR), which has been combined with object oriented programming (OOP) in order to enhance its modularity and reuse capabilities. The obtained results show, that different hybrid architectures are most adapted for different applications. Moreover, the robustness of the results using real time control algorithms are studied, showing that CsT matters. The obtained results contribute to simplify and harmonize the design of hybrid solutions for multiple applications.

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“…It is commonly admitted that Dynamic Programing (DP) is able to provide the best optimization strategy of a system [9], [16]. However, the calculation time makes it impossible to use on-line.…”

Section: Optimisation Strategymentioning

confidence: 99%

Predictive Energy Management for Hybrid Vehicles Based on Driving Cycle Recognition

Joud¹,

Chrenko²,

Kéromnès³

et al. 2017

2017 IEEE Vehicle Power and Propulsion Conference (VPPC)

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This article presents a novel approach to design an optimum energy management for a series plug-in hybrid electric vehicle based on driving cycle recognition. Thanks to an intensive study of driver habits, an important part of cycles is predictable. Optimized energy management can be developed for representative cycles via DP. Those results can be applied to the same cycle with slight adaptations thereafter with quality factors above 98%.

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Novel Classification of Control Strategies for Hybrid Electric Vehicles

Cited by 15 publications

References 54 publications

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool

Predictive Energy Management for Hybrid Vehicles Based on Driving Cycle Recognition

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