A real time fuzzy logic power management strategy for a fuel cell vehicle

Hemi, Hanane; Ghouili, Jamel; Chériti, A.

doi:10.1016/j.enconman.2013.12.040

Cited by 194 publications

(82 citation statements)

References 7 publications

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“…There are a number of EMSs for FCVs, such as intelligent-based EMSs and optimization-based EMSs. Among intelligent-based EMSs, FLC has a quite suitable performance, as reported in many studies [14][15][16]. Overall, FLC furnishes rm deduction, from inexact and inexplicit information, about the state of the system in an undemanding way.…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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

2016

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“…Activation over voltage ηact which is dominant at low FC currents is caused by the slowness of the reactions taking place on the surface of the electrodes, which can be expressed as [10]:…”

Section: Fuel Cell System Modelmentioning

confidence: 99%

“…η ohmic Ohmic over voltage, η conc Concentration over voltage.Also the equilibrium electrode potential E is given by the Nernst equation [10]:…”

Section: Fuel Cell System Modelmentioning

confidence: 99%

“…In the found research works [13,14], this technique seem to be well adapted for four leg inverter with an output LC filter. Several control approaches to minimize the Chattering in Induction-Motors, 6 DOF Octorotor, Doubly-Fed Induction Motor and Wind Energy Conversion have been reported in the literature [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…For state machine control [8,9] each rule or state is defined based on heuristic or empiric past experience. Another rule strategy is based on fuzzy logic [10], where a membership functions are associated to the inputs and the outputs in order to achieve the desired performance. As statement, the performance of rule based strategies depends highly on the knowledge of the system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sliding Mode Control Using SVM for Power Quality Enhancement in StandAlone System Based on Four-Leg Voltage

Djerioui¹,

Ouali²,

Ladjal³

2018

IJIES

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This paper proposes a sliding mode control strategy for hybrid power system (HPS). The hybrid power system consists of four-leg voltage source Inverter Bridge. The HPS ensures full compensation for harmonic phase currents, neutral current, reactive power compensation and unbalanced nonlinear load currents. The Sliding Mode control strategy with a three dimensional space vector modulation deals with power quality enhancement in standalone power-supply systems with the key objective to compensate for AC side load and phase disturbances that result essentially from the presence of nonlinear and unbalanced loads. The four-leg voltage source inverters play an important role by providing both balanced and unbalanced loads with clean power and balanced output voltage. The simulation results show that the performance of the four-leg HPS with the proposed control algorithm.

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Energy storage management of hybrid solar/wind standalone system using adaptive neuro‐fuzzy inference system

Varghese

Reji

2019

Int Trans Electr Energ Syst

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Summary This paper proposes a charge controller for ultracapacitor that acts as an energy storage device so that it can minimize the effect of irregular solar radiation and widely varying wind velocity. A suitable supervision for each generating unit and ultracapacitor are mandatory for a standalone system. The primary energy sources solar and wind systems are operating at maximum power point. Adaptive neuro‐fuzzy inference system (ANFIS) is utilized to foresee the voltage of panel at which extreme power is obtained for a solar system and also to ascertain the most extreme power delivered by the nonuniform and unpredictable characteristics of solar and wind energy sources. A common energy management technique is developed to manage the control flow between the distinctive power sources, loads, and ultracapacitor in the system. The results obtained by ANFIS are compared with artificial neural network. A Simulink model of the hybrid standalone system was developed and tested for various insolation, temperature, and wind velocity.

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A real time fuzzy logic power management strategy for a fuel cell vehicle

Cited by 194 publications

References 7 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

Sliding Mode Control Using SVM for Power Quality Enhancement in StandAlone System Based on Four-Leg Voltage

Energy storage management of hybrid solar/wind standalone system using adaptive neuro‐fuzzy inference system

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