Oxygen Excess Ratio Research Articles

A cascaded NPID/PI scheme for the regulation of stack voltage in proton exchange membrane fuel cell

In this work, a higher-order Proton Exchange Membrane (PEM) Fuel Cell system is controlled using a cascaded control approach. The primary and secondary controllers, NPID/PI, are non-linear proportional-integral-derivative and proportional-integral, respectively. The suggested cascade approach controls the stack voltage by adjusting the air compressor voltage to maintain the oxygen excess ratio value within limits. Two additional cascade control structures, PID/PI and FOPID/FOPI (fractional order PID and fractional order PI), are also created for a fair comparison. A genetic algorithm is used to determine the controller's optimal parameters by minimising the time integral absolute error of the primary controller. Results show that NPID/PI control structure achieves the minimum value of settling time and overshot (0.9514 s, 0.021%) compared to FOPID/PI (1.8980 s, 0.036%) and PID/PI (3.2308 s, 0.092%), respectively. Finally, it can be concluded from the result of setpoint tracking, disturbance rejection, and noise suppression that the proposed controller is efficient and robust compared to other designed controllers.

Active fault-tolerant coordination energy management for a proton exchange membrane fuel cell using curriculum-based multiagent deep meta-reinforcement learning

This paper addresses the challenge of active fault-tolerant coordination control (AFTCC) for proton exchange membrane fuel cells (PEMFCs), which are complex nonlinear systems with multiple inputs and outputs. Conventional fault-tolerant control methods cannot properly coordinate multiple operating variables and prevent constraint violations in PEMFCs. Our proposed AFTCC method seeks to stabilize the output performance of four operating variables and avoid PEMFC operating constraint violations during failure scenarios. Our method is supported by a curriculum-based multiagent deep meta-deterministic policy gradient (CMA-DMDPG) algorithm, which integrates meta-reinforcement learning, multiagent reinforcement learning and curriculum learning to achieve multitask collaboration of multiple agents, thereby enhancing PEMFC robustness. The algorithm consists of a meta-learner and a base learner. The base learner regards the hydrogen controller, oxygen controller, pump controller and radiator controller as four independent agents and thus achieves a cooperative control policy. The meta-learner detects PEMFC faults and selects an appropriate cooperative control policy. The performance of AFTCC under various stochastic and fault conditions is evaluated using a 75 kW PEMFC model. The results showed that the performance of AFTCC surpassed 11 other fault-tolerant control methods in terms of output voltage, oxygen excess ratio, and stack temperature, and avoided constraint violations.

Oxygen excess ratio control of PEM fuel cell systems with prescribed regulation time

In this paper, we focus on addressing the air supply problem for fuel cells. The air supply system faces a challenge: operating at maximum load consumes a significant amount of power, while insufficient air can lead to oxygen starvation problems in fuel cells. An important metric, the oxygen excess ratio, indicates whether the fuel cell is receiving the appropriate amount of air. Unfortunately, directly measuring this ratio is generally impractical. To overcome this limitation, we propose a fixed-time observer that reconstructs the oxygen excess ratio within a short predetermined period. By utilizing this reconstructed index, we introduce a cascaded double-loop controller. Specifically, both the external and internal loops are regulated using a modified prescribed time control strategy. This approach enables the regulation of the oxygen excess ratio to the optimal value within a prescribed short time. The advantages of our proposed method are validated through hardware in-loop experiments, showcasing its superiority over conventional finite-time control techniques.

Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system

Regulating the air supply is crucial for high efficiency and reliable operation of proton exchange membrane fuel cell systems (PEMFCs). In this study, a fused multi-model predictive control (FM-MPC) with an adaptive compensation is proposed for the oxygen excess ratio (OER) regulation of the air supply system. The FM-MPC is designed based on the linearized PEMFC model at low and high power phases, with two linear MPCs implemented and combined using adaptive featured weights. An adaptive compensation strategy is created to address the imbalance of the two MPCs and external load disturbances. The stability of the proposed control is analyzed using Lyapunov's second law. Simulation results demonstrate that the proposed method exhibits less overshoot and faster response than conventional MPCs, with the OER total sum-of-squares error (TSSE) reduced by 59.4% and 87.7% for New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) conditions, respectively. Finally, a Hardware-In-the-Loop (HIL) experiment verifies the real-time application potential of the proposed controller, with a mean relative error (MRE) of 1.12% between experiment and simulation.

Oxygen excess ratio control of PEM fuel cell: fractional order modeling and fractional filter IMC-PID control

Abstract Concerning global warming, an energy-efficient power source must produce low or no pollutant emissions and provide an unlimited fuel supply. Proton Exchange Membrane fuel cell (PEMFC) is an electrochemical device that transforms chemical energy into electrical energy. The performance and durability of PEM fuel cells are affected by voltage reversals and fuel starvation. Oxygen Excess Ratio (OER) is a crucial factor in controlling the fuel starvation of the PEMFC system. First, this work identified the PEMFC as an integer order and fractional-order first order plus time delay models using the predictor error method and Grunwald–Letnikov simulation method based on a trust-region-reflect algorithm, respectively. Fractional order models more accurately represented the PEM fuel cell system dynamics. Then, robust fractional filters cascaded with PID controllers based on the Internal Model Control scheme (IMC) are designed for identified integer and fractional order models to regulate the OER by compressor voltage manipulation. The genetic Algorithm (GA) optimization technique is used to find the optimal fractional filter tuning parameters. The proposed controller’s performance regarding Integral Absolute Error (IAE) and Total Variance (TV) is analyzed. Furthermore, the robustness of a perturbed plant and fragility with perturbed controllers are elucidated. The results show that a fractional filter cascaded with fractional order PID controller improves the performance compared to a fractional filter cascaded with integer order PID controllers.

Estimation‐Based Event‐Triggered Adaptive Terminal Sliding Mode Control without Pressure Sensors for a Polymer Electrolyte Membrane Fuel Cell Air Feeding System

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).

Switching threshold event-triggered estimation and control for unmeasured oxygen excess ratio of automotive PEMFC air feeding system with input and prescribed performance constraints

Switching threshold event-triggered estimation and control for unmeasured oxygen excess ratio of automotive PEMFC air feeding system with input and prescribed performance constraints

Control of Oxygen Excess Ratio for a PEMFC Air Supply System by Intelligent PID Methods

The hydrogen fuel cell is a quite promising green device, which could be applied in extensive fields. However, as a complex nonlinear system involving a number of subsystems, the fuel cell system requires multiple variables to be effectively controlled. Oxygen excess ratio (OER) is the key indicator to be controlled to avoid oxygen starvation, which may result in severe performance degradation and life shortage of the fuel cell stack. In this paper, a nonlinear air supply system model integrated with the fuel cell stack voltage model is first built, based on physical laws and empirical data; then, conventional proportional-integral-derivative (PID) controls for the oxygen excess ratio are implemented. On this basis, fuzzy logic inference and neural network algorithm are integrated into the conventional PID controller to tune the gain coefficients, respectively. The simulation results verify that the fuzzy PID controller with seven subsets could clearly improve the dynamic responses of the fuel cells in both constant and variable OER controls, with small overshoots and the fastest settling times of less than 0.2 s.

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Evaluation method of oxygen excess ratio control under typical control laws for proton exchange membrane fuel cells

For real-used proton exchange membrane fuel cells (PEMFC), it is critical to design an effective controller and evaluate its performance. Current evaluations of controllers are often empirical or qualitative, and quantitative evaluation methods are lacking. In this paper, the quantifiable objective evaluation method is proposed for assessing the controller performance, including optimal control, adaptive control, variable structure control, and model-based control, aiming at the oxygen excess ratio. In the method, the anti-starvation, transient-state, steady-state, and multiple load-changing performances are comprehensively considered through weighting, rating, and especially the introduction of negative scores through the integration of four independent indexes. The importance and effectiveness of evaluation method are verified through the specific analysis of four controllers and the internal states of PEMFC. Besides, the evaluation method can be extended appropriately, such as considering the robustness, optimal output power, and other practical problems, which is significant for the development of PEMFC system controller.

Effect of the high oxygen excess ratio design on the performance of air-cooling polymer electrolyte membrane fuel cells for unmanned aerial vehicles

Oxygen excess ratio (OER) is a key parameter that affects output power and operating current density of air-cooling polymer electrolyte membrane fuel cells (PEMFCs). In this work, the design method and performance model of air-cooling PEMFCs for UAVs with high OER are proposed. The high OER is realized by using high-speed airflow behind propeller to feed PEMFC cathode. The effect of high OER on the PEMFC performance is investigated by measuring OER and the output power of PEMFC. The test results indicate that the high OER design can further release the performance potential of the PEMFC constrained by heat dissipation while discarding the parasitic power caused by cooling fans. Compared with current PEMFC with low OER by using cooling fans, the temperature distribution variation of PEMFC stack is reduced by 29.8% and the voltage consistency of the single cell at high current density is doubled. The PEMFC with the high OER design exhibits the “current jump” effect, the continuous operating current of PEMFC is increased by 25.0%. The PEMFC net power is increased from 1070 W to 1320 W, and the power density is also increased by 67.8%. Such results provide good reference values for the current PEMFC-powered propulsion system.

Active Disturbance Rejection-Based Performance Optimization and Control Strategy for Proton-Exchange Membrane Fuel Cell System

In this paper, to maximize the net output power and realize better performance optimization and control of the oxygen excess ratio, a complete dynamic model of the proton-exchange membrane fuel cell system is developed and an active disturbance rejection control strategy is proposed. The active disturbance rejection control drives the uncertainties and perturbations of the system to an extended state, which is predicted and eliminated by real-time input–output data. The simulation results indicate that, compared with the proportion–integral–differential and fuzzy proportion–integral–differential control, the active disturbance rejection control strategy can effectively improve the control performance with a lower control cost and less wear on the compressor, and the integral absolute error of the oxygen excess ratio control is reduced by up to 50%. In addition, the output voltage is improved and the power generation efficiency of the proton-exchange membrane fuel cell under the active disturbance rejection-based oxygen excess ratio control is 1.84% and 0.95% higher than that of the proportion–integral–differential and fuzzy proportion–integral–differential control, respectively. Moreover, the proposed optimal-reference control strategy increases the net power by up to 1.85% compared with the fixed-reference control strategy.

Pressure and oxygen excess ratio control of PEMFC air management system based on neural network and prescribed performance

The simultaneous control of the pressure and oxygen excess ratio (OER) plays an important role in improving the performance and safety of polymer electrolyte membrane fuel cell (PEMFC). Nevertheless, the coupling characteristics and nonlinearity existing in the PEMFC air management system need to be solved. To this end, the original coupled nonlinear PEMFC air management system is first transformed into the cathode pressure and OER subsystems by using input–output linearization. For the cathode pressure subsystem, the neural network (NN) control scheme is proposed to maintain the stable tracking of cathode pressure. For the OER subsystem, a prescribed performance function (PPF) is proposed and therefore, the overshoot and steady state of OER tracking error is guaranteed within the quantitative boundary. Moreover, the restriction that the initial error is within the performance function bound is relaxed by proposing a tuning function. Finally, the Lyapunov stability theory, numerical simulations and hardware-in-loop (HIL) experiments show the effectiveness of the proposed controller. Compared with the proportional integral derivative (PID) controller, NN controller without PPF and NN controller with conventional PPF, the NN controller with the proposed PPF can realize the quantitative adjustment of OER and give more improvements to the system safety.

Finite-Time Command Filtered Control for Oxygen-Excess Ratio of Proton Exchange Membrane Fuel Cell Systems with Prescribed Performance

In recent years, proton exchange membrane fuel cell (PEMFC) has received growing attention as a new sustainable energy source because of its high-power density and zero-emission. In the PEMFC system, the air supply control has a significant impact on the efficiency and lifetime of the PEMFC stack. However, external disturbances and output constraints regularly have negative effects on air supply control. This paper aims to investigate a novel system analysis and advanced strategy control for the oxygen-excess ratio of a PEMFC system under the variant load current disturbance. The air-supply dynamic model is established which takes into account the supply manifolds, compressor, and the PEMFC stack. The proposed control method is designed based on finite-time command-filter control (FTCFC) to improve the tracking performance and ensure the finite-time convergence. Moreover, owing to the suggested prescribed performance function, the oxygen-excess ratio output remains in the pre-boundedness. Theoretical analysis exhibits that the closed-loop system stability is guaranteed by the Lyapunov theory. Finally, the simulation and hardware-in-loop (HIL) experiments are carried out on MATLAB environment and a 100 W power PEMFC system to validate the effectiveness of the suggested methodology.

Adaptive energy management for fuel cell hybrid power system with power slope constraint and variable horizon speed prediction

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint.

Heterogeneous Catalytic and Non-Catalytic Supercritical Water Oxidation of Organic Pollutants in Industrial Wastewaters Effect of Operational Parameters

This work reports supercritical water oxidation (SCWO) of organic pollutants in industrial wastewater in the absence and presence of catalysts. To increase the efficiency of the oxidation process, the SCWO of organic compounds in industrial wastewater was performed in the presence of various iron- and manganese-containing heterogeneous catalysts (Fe-Ac, Fe-OH, and Mn-Al). The catalytic and non-catalytic SCWO of organic compounds in wastewater from PJSC “Nizhnekamskneftekhim”, generated from the epoxidation of propylene with ethylbenzene hydroperoxide in the process of producing propylene oxide and styrene (PO/SM), was performed. The effect of operational parameters (temperature, pressure, residence time, type of catalysts, oxygen excess ratio, etc.) on the efficiency of the process of oxidation of organic compounds in the wastewater was studied. SCWO was studied in a flow reactor with induction heating under different temperatures (between 673.15 and 873.15 K) and at a pressure of 22.5 MPa. The reaction time ranged from 1.8 to 4.83 min. Compressed air was used as an oxidizing agent (oxidant) with an oxidant ratio of two to four. A pseudo-first-order model expressed the kinetics of the SCWO processes, and the rate constants were evaluated. In the present work, in order to optimize the operation parameters of the SCWO process, we used the thermodynamic properties of near- and supercritical water by taking into account the asymmetric behavior of the liquid–vapor coexistence curve.

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

In this research work, a Neural Network (NN) and Uniform Robust Exact Differentiator (URED) observer-based Fast Terminal Integral Sliding Mode Control (FTISMC) has been proposed for Oxygen Excess Ratio (OER) regulation of a Polymer Electrolyte Membrane Fuel Cell (PEMFC) power systems for vehicular applications. The controller uses URED as an observer for supply manifold pressure estimation. NN is used to estimate the stack temperature which is unavailable. The suggested control method increased the PEMFC’s effectiveness and durability while demonstrating the finite-time convergence of system trajectories. By controlling the air-delivery system in the presence of uncertain current requirements and measurement noise, the approach ensures maximum power efficiency. The Lyapunov stability theorem has been used to confirm the stability of the presented algorithm. In addition, the suggested method eliminated the chattering phenomenon and improved power efficiency. Given these noteworthy characteristics, the research has the potential to decrease sensor dependence and production costs while also improving the transient and steady-state response in vehicular applications.

<inline-formula><tex-math id="M1">$ \mu $</tex-math></inline-formula> and <inline-formula><tex-math id="M2">$ H_\infty $</tex-math></inline-formula> optimization control based on optimal oxygen excess ratio for the Proton Exchange Membrane Fuel Cell (PEMFC)

<p style='text-indent:20px;'>The fuel cell (FC) is a new technology for large-scale power generation. Providing of fast and sufficient air concentration in the FC cathode is a key to prevent the oxygen starvation and extend the life cycle of the FC. The proton exchange membrane fuel cell (PEMFC) parameter variations and output current disturbances can significantly influence on the airflow subsystem performance. For this purpose, this paper addresses the robust airflow control synthesis of the PEMFC based on the <inline-formula><tex-math id="M3">\begin{document}$ \mu $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M4">\begin{document}$ H_\infty $\end{document}</tex-math></inline-formula> control techniques. The presented advanced controllers are designed to regulate the oxygen concentration in the FC cathode on the desired value. Such robust controllers are tuned to guarantee PEMFC performance against parameter variations and output current disturbances. Then, the obtained results are compared with the optimal PID controller tuned by the well-known internal model control (IMC) method. Simulation results show that in comparison of the IMC-based PID controller, the applied robust control methodologies are more effective. The designed <inline-formula><tex-math id="M5">\begin{document}$ \mu $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ H_\infty $\end{document}</tex-math></inline-formula> controllers efficiently provide the required airflow of the PEMFC on the desired value, and avoid the oxygen starvation. Furthermore, due to the structured type of uncertainties, the <inline-formula><tex-math id="M7">\begin{document}$ \mu $\end{document}</tex-math></inline-formula> controller keeps the airflow on the desired value quite better than the <inline-formula><tex-math id="M8">\begin{document}$ H_\infty $\end{document}</tex-math></inline-formula> controller.</p>

Cascade Control Method of Sliding Mode and PID for PEMFC Air Supply System

Proton exchange membrane fuel cells (PEMFC) are vulnerable to oxygen starvation when working under variable load. To address these issues, a cascade control strategy of sliding mode control (SMC) and Proportion Integration Differentiation (PID) control is proposed in this study. The goal of the control strategy is to enhance the PEMFC’s net power by adjusting the oxygen excess ratio (OER) to the reference value in the occurrence of a load change. In order to estimate the cathode pressure and reconstruct the OER, an expansion state observer (ESO) is developed. The study found that there is a maximum error of about 2200Pa between the estimated cathode pressure and the actual pressure. Then the tracking of the actual OER to the reference OER is realized by the SMC and PID cascade control. The simulation study, which compared the control performance of several methods—including PID controller, adaptive fuzzy PID controller and the proposed controller, i.e., the SMC and PID cascade controller—was carried out under various load-changing scenarios. The outcomes demonstrate that the proposed SMC and PID cascade controller method really does have a faster response time. The overshoot is reduced by approximately 3.4% compared to PID control and by about 0.09% compared to fuzzy adaptive PID. SMC and PID cascade control reference OER performs more effectively in terms of tracking compared to PID control and adaptive fuzzy PID control.

A novel controller based on the sum of squares relaxation for the uncertain model of the PEMFC considering external disturbances and actuator saturation

The current paper aims to model the uncertainties of the proton exchange membrane fuel cell system and control its main parameters, such as compressor speed and oxygen excess ratio, in the presence of external disturbances and actuator saturation using the improved controller, which is designed based on the sum of squares relaxation for nonlinear models. The open-loop nonlinear system is considered in state-dependent polynomial equations. The state feedback method is used to obtain the appropriate control responses. To numerically solve the state-dependent Linear Matrix Inequalities, semi-definite programming based on the sum of squares decomposition is also used. First, the transient response of the system is improved in the design phase of the controller. Then, the actuator saturation problem of PEMFC is considered for the state feedback controller proposed in polynomial systems, and the obtained results are expanded for the uncertain system. Results are also validated in the presence of external disturbances. The results are compared with those of the robust and adaptive control methods. The proposed controller not only has good agreement with the results of robust and adaptive control methods but also outperforms the previous controllers in terms of accuracy, rise time, fluctuation, and overshoot.