Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system

Ou, Kai; Wang, Yaxiong; Li, Zhen-Zhe; Shen, Yun-De; Xuan, Dongji

doi:10.1016/j.ijhydene.2015.04.080

Cited by 128 publications

(50 citation statements)

References 18 publications

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“…The work in [34] adopted an adaptive PID controller to regulate z 2 around a reference value z 2,r , which is taken equal to 2.4. The parameters of the PID controller are tuned by using an on-line fuzzy logic optimization loop.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…The parameters of the PID controller are tuned by using an on-line fuzzy logic optimization loop. Simulation are performed taking into account the same I st profile adopted in [34], which is shown in Figure 19(b). Figure 19 Figure 19(c) shows the zoomed plot of z 2 at t=15 s, where the proposed control strategy has improved greatly the transient response of z 2 compared to the control strategy presented in [34].…”

Section: Comparison Resultsmentioning

confidence: 99%

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Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Baroud

Benmiloud

Benalia

et al. 2017

International Journal of Hydrogen Energy

121

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This paper proposes a novel hybrid fuzzy-PID controller for air supply on Proton Exchange Membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given setpoint in order to prevent oxygen starvation and damage of the fuel-cell stack. The proposed control scheme consists of three parts: a fuzzy-logic controller (FLC), a fuzzy-based self-tuned PID (FSTPID) controller and a fuzzy selector. Depending on the value of the error between the current value of oxygen excess ratio and its setpoint value, the fuzzy selector decides which controller should play the greatest effect on the control system. The performance of the proposed control strategy is analysed through simulations for different load variations and for parameter uncertainties. The results show that the novel hybrid fuzzy-PID controller performs significantly better than the classical PID controller and the FLC in terms of several key performance indices such as the Integral Squared Error (ISE), the Integral Absolute Error (IAE) and the Integral Time-weighted Absolute Error (ITAE), as well as the overshoot, settling and rise time for the closed-loop control system.

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Section: Comparison Resultsmentioning

confidence: 99%

Section: Comparison Resultsmentioning

confidence: 99%

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Baroud

Benmiloud

Benalia

et al. 2017

International Journal of Hydrogen Energy

121

View full text Add to dashboard Cite

show abstract

“…Particularly, the membrane conductivity varies with the membrane water content and fuel cell temperature, this dependence was determined empirically for Nafion 117 membrane in terms of the model constants, reported also in [7]: c 1 = 10, c 3 = 2, b 2 = 350, b 11 = 0.005139, b 12 = 0.00326. Conversely, the concentration looses depend on the temperature and the reactant partial pressure, hence, the concentration overpotential was also determined empirically, in this case, in terms of c 2 and c 3 , with c 2 subject to the conditional statements of (12). Here, v con is determined also by i max = 2.2, which is the current density than causes abrupt voltage drop in the concentration region.…”

Section: Operation and Simulation Of The Pemfcmentioning

confidence: 99%

“…It has been acknowledged that oxygen excess ratio control is one of the most important actions to enhance protection and performance of fuel cells; thus, well-suited control strategies with this target have been proved, three related works are the following: a sliding mode control achieved by adjusting the compressor supply voltage was assisted with a nonlinear observer predicting the oxygen excess ratio and improved with a novel tuning procedure [11]. In a second work, a feedforward fuzzy-PID control was proposed; the model used for designing the oxygen excess ratio controller included cathode and anode mass flow transients, membrane hydration dynamics, as well as the fuel cell BOP simulation [12], the controller was developed to adapt the PID parameters to achieve the regulation of the air flow rate using on-line fuzzy logic optimization loop. A third approach used adaptive control under exigent scenarios [13,14].…”

Section: Introductionmentioning

confidence: 99%

Control of the Air Supply Subsystem in a PEMFC with Balance of Plant Simulation

et al. 2017

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This paper deals with the design of a control scheme for improving the air supply subsystem of a Proton Exchange Membrane Fuel Cell (PEMFC) with maximum power of 65 kW. The control scheme is evaluated in a plant simulator which incorporates the balance of plant (BOP) components and is built in the aspenONE R platform. The aspenONE R libraries and tools allows introducing the compressor map and sizing the heat exchangers used to conduct the reactants temperature to the operating value. The PEMFC model and an adaptive controller were programmed to create customized libraries used in the simulator. The structure of the plant control is as follows: the stoichiometric oxygen excess ratio is regulated by manipulating the compressor power, the equilibrium of the anode-cathode pressures is achieved by tracking the anode pressure with hydrogen flow manipulation; the oxygen and hydrogen temperatures are regulated in the heat exchangers, and the gas humidity control is obtained with a simplified model of the humidifier. The control scheme performance is evaluated for load changes, perturbations and parametric variations, introducing a growing current profile covering a large span of power, and a current profile derived from a standard driving speed cycle. The impact of the control scheme is advantageous, since the control objectives are accomplished and the PEMFC tolerates reasonably membrane damage that can produce active surface reduction. The simulation analysis aids to identify the safe Voltage-Current region, where the compressor works with mechanical stability.

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“…The nature of the liquid and friction of control mechanism and other factors make the system nonlinear [1,2]. In nowadays, the best-known industrial process controller is the PID controller because of its simplicity, robustness, high reliability and it can be easily implemented on any processor, but using a PID controller is not fully convenient when it comes to dealing nonlinear systems [3,4]. But these systems can be successfully controlled using fuzzy logic controllers because of their independency from the mathematical model of the system.…”

Section: Introductionmentioning

confidence: 99%

The Application of Fuzzy Control in Water Tank Level Using Arduino

Chabni¹,

Taleb²,

Benbouali³

et al. 2016

ijacsa

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Abstract-Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of the fuzzy logic controls are still implemented on expensive highperformance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low-cost controller applied to a water level control system. The paper also gives a low-cost hardware solution and practical procedure for system identification and control. First, the mathematical model of the process was obtained with the help of Matlab. Then two methods were used to control the system, PI (Proportional, Integral) and fuzzy control. Simulation and experimental results are presented.

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Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system

Cited by 128 publications

References 18 publications

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Novel hybrid fuzzy-PID control scheme for air supply in PEM fuel-cell-based systems

Control of the Air Supply Subsystem in a PEMFC with Balance of Plant Simulation

The Application of Fuzzy Control in Water Tank Level Using Arduino

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