EM-TFL identification for Particle Swarm Optimization of HEV powertrain

Al-Aawar, N.; Hijazi, T. M.; Arkadan, A.A.

doi:10.1109/iemdc.2009.5075191

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…al. [98,100], the design optimisation environment consists mainly of a PSO module and an Electromagnetic-Team Fuzzy Logic (EM-TFL) module. As shown in Figure 17, the PSO optimiser searches the database of the EM-TFL algorithm to obtain the best population.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Modelling and control of hybrid electric vehicles (A comprehensive review)

Enang

Bannister

2017

Renewable and Sustainable Energy Reviews

275

152

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The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guarantying improved fuel economy and reduced emissions.The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilized. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle. To this effect, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of theseliteratures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimization.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Electromagnetic-Team fuzz logic PSO optimization process for an HEV (source[98]) Algorithm flow chart of the MOSADE approach (source[99])…”

mentioning

confidence: 99%

Modelling and control of hybrid electric vehicles (A comprehensive review)

Enang

Bannister

2017

Renewable and Sustainable Energy Reviews

275

152

View full text Add to dashboard Cite

show abstract

“…For a charge sustaining operation this strategy gives better fuel efficiency. Al-Aawar et al [145] combined PSO and electromagnetic-team fuzzy logic (EM-TFL) together for the design optimization of the HEV power train system to find best electromechanical component sizes for higher efficiency and reduced fuel consumption. Desai and Williamson [146] optimized both power train and control strategy (objective function and constrained function) parameters using PSO for improved fuel economy and efficiency and reduced emissions.…”

Section: Real-time Optimizationmentioning

confidence: 99%

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

Panday

Bansal

2014

International Journal of Vehicular Technology

189

111

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Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

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“…A simulation model for driveability assessment and optimization of hybrid drive trains was also proposed [23]. Al Aawar et al investigated the optimization method for the powertrain of hybrid electric vehicles to achieve good driveability [24]. Jauch et al developed a generic model of the driveline of a plug-in hybrid vehicle [25].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Subjective Evaluation Method of Driveability for Fuel Cell Vehicles

Zhan,

Zhu,

Duan

et al. 2024

Energies

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Aiming at the demand for subjective evaluation of driveability for fuel cell vehicles, the modeling and evaluation method of driveability for fuel cell vehicles were studied in this paper. Firstly, a real-time model of the fuel cell vehicle powertrain system was established, which included the fuel cell model, power battery model, DC/DC converter model and drive motor model. Secondly, it was integrated with the vehicle dynamics model to form a virtual prototype of a fuel cell vehicle. And a virtual subjective evaluation platform for fuel cell vehicles was built by combining the virtual prototype and high-fidelity driving simulator. Thirdly, a subjective evaluation method of driveability for fuel cell vehicles was proposed, which included the starting performance, acceleration performance, uniform speed performance and tip-in/tip-out performance. Finally, the paper used the platform and method mentioned above to conduct subjective evaluations of the fuel cell vehicles.

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EM-TFL identification for Particle Swarm Optimization of HEV powertrain

Cited by 6 publications

References 6 publications

Modelling and control of hybrid electric vehicles (A comprehensive review)

Modelling and control of hybrid electric vehicles (A comprehensive review)

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

Modeling and Subjective Evaluation Method of Driveability for Fuel Cell Vehicles

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