Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles

Montazeri-Gh, Morteza; Poursamad, Amir; Ghalichi, Babak

doi:10.1016/j.jfranklin.2006.02.015

Cited by 222 publications

(115 citation statements)

References 14 publications

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“…It should be noted that as a control strategy has no considerable impact on the sizing procedure [33], the component sizing process is done by employing the thermostat control strategy.…”

Section: Energy Management 31 Component Sizingmentioning

confidence: 99%

Designing an Optimal Fuzzy Controller for a Fuel Cell Vehicle Considering Driving Patterns

2016

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Abstract. Design of an optimal fuzzy scheme for a fuel cell/battery vehicle to control the power ow between the main components, i.e. the fuel cell, electric motor, and battery, under various driving conditions, is considered in this paper. For this purpose, rstly, the optimum sizes of the main components are calculated by means of a Particle Swarm Optimization (PSO) algorithm. Subsequently, a Fuzzy Logic Controller (FLC) is devised for the control of the power ow. Finally, the FLC is optimized for various driving patterns and an optimal control scheme, based on PSO application, is proposed for energy management of the Fuel Cell Vehicle (FCV) under various tra c conditions. In each one of the mentioned stages, the same optimization process is conducted by applying a Genetic Algorithm (GA) for comparison with the result of the PSO. The results of the computer simulation are compared over diverse driving conditions. The results give an acceptable indication of progress in fuel economy for various driving patterns, using the proposed optimal fuzzy controller.

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“…It should be noted that as a control strategy has no considerable impact on the sizing procedure [33], the component sizing process is done by employing the thermostat control strategy.…”

Section: Energy Management 31 Component Sizingmentioning

confidence: 99%

Designing an Optimal Fuzzy Controller for a Fuel Cell Vehicle Considering Driving Patterns

2016

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“…In [19,20] the application of the genetic algorithm (GA) was described for the optimization of the control parameters in parallel HEV. The objective function was defined so as to minimize the vehicle engine fuel consumption and emissions with regard to vehicle dynamic requirements.…”

Section: Figurementioning

confidence: 99%

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

2016

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Preliminary noteOne of the major issues in automotive industry is to develop hybrid electric vehicles (HEVs) as the prime candidate for improving fuel efficiency and emissions reduction. This study addresses the impact of an actual drive pattern and control strategy on the conventional and plug-in hybrid electric vehicles (PHEV). In the first step the development procedure of driving cycle for real condition is proposed. Then effects of driving pattern and energy management strategy as the main factors, which strongly affect the fuel consumption and emission of pollutants, are investigated for HEVs and PHEVs. For this purpose fuzzy logic controller which is optimized with genetic algorithm is developed for energy management system. Then vehicle performance is simulated in ADVISOR. Simulation results demonstrate the effectiveness of the approach for reducing the fuel consumption and emissions. Furthermore, simulation results indicate that PHEVs in comparison to HEVs improve fuel efficiency and reduce emissions in real world driving cycle. Keywords: emission; fuel consumption; fuzzy controller; genetic algorithm; hybrid vehicle; plug-in hybrid vehicleIstraživanje potrošnje energije i ispuštanja štetnih plinova kod plug-in i hibridnih električnih vozila Prethodno priopćenje Jedan od osnovnih zadataka u automobilskoj industriji je razvoj hibridnih električnih vozila (HEVs) kao primarnog kandidata za poboljšanje učinkovitosti goriva i smanjenje ispušnih plinova. Ovo se istraživanje bavi učinkom aktualnog modela pogona i upravljanja na konvencionalna i plug-in hibridna električna vozila (PHEV). Najprije se predlaže postupak razvoja pogonskog ciklusa za stvarne uvjete. Tada se za HEVs i PHEVs istražuju učinci pogonskog modela i načina upravljanja potrošnjom energije kao glavnih čimbenika koji bitno djeluju na potrošnju goriva i ispuštanje plinova koji zagađuju okolinu. U tu je svrhu za sustav upravljanja energijom razvijen upravljač neizrazite logike optimiziran genetskim algoritmom. Tada je rad vozila simuliran u ADVISOR-u. Rezultati simulacije pokazuju učinkovitost pristupa kojim se smanjuje potrošnja goriva i količina ispušnih plinova. Nadalje, ti rezultati pokazuju da PHEVs u usporedbi s HEVs poboljšavaju učinkovitost goriva i smanjuju količinu ispušnih plinova u stvarnom pogonu. Ključne riječi: genetički algoritam; hibridno vozilo; ispuštanje plinova; plug-in hibridno vozilo; potrošnja goriva; upravljač neizrazite logike

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“…Furthermore, it also increases the accuracy of the PDF approximation by replicating particles with high weights. The sequential importance resampling (SIR) algorithm is a very popular form of the PF and may be summarized by (7) to (10). The first equation draws samples or particles from the proposal distribution.…”

Section: Particle Filtermentioning

confidence: 99%

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

Gadsden

Al-Shabi

Habibi

2011

ISRN Signal Processing

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This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.

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Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles

Cited by 222 publications

References 14 publications

Designing an Optimal Fuzzy Controller for a Fuel Cell Vehicle Considering Driving Patterns

Designing an Optimal Fuzzy Controller for a Fuel Cell Vehicle Considering Driving Patterns

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle

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