Particle Swarm Optimization of Coupled Electromechanical Systems

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

doi:10.1109/tmag.2010.2096802

Cited by 21 publications

(6 citation statements)

References 5 publications

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“…Rule-based energy management methods are obtained based on experience, including the definite rule EMS and the fuzzy rule EMS. With the development of intelligent algorithms, some advanced algorithms including dynamic programming (DP) [20,21], convex programming [22], model predictive control (MPC) [23,24], particle swarm optimization (PSO) [25] and reinforcement learning (RL) [26] are applied to hybrid electric vehicle energy management. Song et al [27] compared four semi-active hybrid energy storage system topologies and proposed on-line control strategies related to different topologies.…”

Section: B Energy Management Strategymentioning

confidence: 99%

Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement

Bai

et al. 2020

IEEE Access

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Section: B Energy Management Strategymentioning

confidence: 99%

Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement

Bai

et al. 2020

IEEE Access

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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%

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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“…Varesi and Radan [148] used PSO to find the optimal degree of hybridization in series-parallel HEV using advance vehicle simulator (ADVI-SOR) to optimize the vehicle performance with reduced fuel consumption and emissions. To optimize the various components of HEV, EM-TFL with PSO has been used by [149] in the form of a case study which concludes that a smaller size engine, electric motor performance, is optimized and fuel economy is improved by 22% and reduction in toxic emissions is noticed. Junhong [150] proposed PSO for energy optimization in PHEVs using MATLAB/Simulink and showed an improvement in fuel economy and reduction in pollutant 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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Particle Swarm Optimization of Coupled Electromechanical Systems

Cited by 21 publications

References 5 publications

Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement

Optimal Design of a Hybrid Energy Storage System in a Plug-In Hybrid Electric Vehicle for Battery Lifetime Improvement

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

A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

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