Oxygen Excess Ratio Research Articles

Observer‐based event‐triggered adaptive output feedback control of oxygen excess ratio for uncertain polymer electrolyte membrane fuel cell system

Observer‐based event‐triggered adaptive output feedback control of oxygen excess ratio for uncertain polymer electrolyte membrane fuel cell system

A Novel Control Algorithm of the Air Supply Subsystem: Based on Dynamic Modeling of Proton Exchange Membrane Fuel Cell

In this paper, a novel second-order active disturbance rejection control (2-ADRC) algorithm is proposed to optimize the control of the air supply subsystem for Proton Exchange Membrane Fuel Cell (PEMFC). To improve the optimal control effect of the air supply subsystem for PEMFC, the modeling theory of the air supply subsystem considering dynamic characteristics of the PEMFC system is first studied, and the dynamic Simulink model of the PEMFC system is established and verified. Then, the optimal oxygen excess ratio (OER) parameters under different load currents are obtained, and the optimal OER parameters are also used as the OER control reference for the designed algorithms. In addition, a 2-ADRC algorithm is designed and proposed to make the actual OER parameters close to the optimal OER in real time. Furthermore, compared with PID and MPC algorithms, the 2-ADRC algorithm can comprehensively consider the two parameters of mass flow and pressure ratio to make the compressor work in the high-efficiency zone and improve the net power and efficiency of the PEMFC system.

Adaptive control of oxygen excess ratio in a proton exchange membrane fuel cell system

Proton exchange membrane fuel cell (PEMFC) has drawn extensive attention as alternative energy with high power generation efficiency and low pollution. The dynamic performances of the PEMFC air system in a city bus, including oxygen excess ratio (OER) tracking and compressor movement, are essential for its stability and economy. In this paper, the study is concentrated on the control of OER under current disturbance and parameter uncertainty. To this end, a Lyapunov-based model reference adaptive controller (Lyapunov-MRAC) with PD controller is employed. Lyapunov-MRAC adjusts the controller in time to ensure the output of the plant following the desired reference model, and the PD controller further improves the dynamic performance of the controller. The proposed strategy, traditional Lyapunov-MRAC, and proportional–integral (PI) controller are implemented on a widely accepted nonlinear model describing the dynamics of the air supply system. The simulation results indicate that the proposed strategy outperforms traditional methods in OER tracking and overshoot restriction under current disturbance, and improves the robustness under the perturbation of compressor rotational inertia. In addition, the proposed strategy can reduce the control effort of the compressor so as to prolong the lifetime of the compressor.

Improve hydrogen economy for vehicular fuel cell system via investigation and control of optimal operating oxygen excess ratio

This study investigates and controls the optimal operating oxygen excess ratio (OER) for PEMFC, which effectively prevents oxygen starvation and improves the hydrogen economy of proton exchange membrane fuel cells (PEMFC). Firstly, the PEMFC output characteristic model and the five-order nonlinear air supply system model are established. Moreover, an adaptive algebraic observer was developed to observe the partial pressure of gas in PEMFC and further reconstruct OER. Secondly, to achieve the minimum hydrogen consumption under the required power, the reference OER is determined by analyzing the PEMFC system output power with its minimum current. Finally, the super-twisting algorithm is adopted to track reference OER. Simulation results show that the average absolute observation errors of oxygen, nitrogen, and cathode pressures under the Highway Fuel Economy Test are 1351.1 Pa (5.1%), 1724.2 Pa (0.9%), and 409.9 Pa (1.6%), respectively. The OER adjust average absolute error is 0.03. Compared with the commonly used fixed OER (e.g., OER of 1.5 and 2.3), the optimal OER strategy can reduce the hydrogen consumption of the PEMFC system by 5.2% and 1.8%, respectively. Besides, a DSP hardware in loop test is conducted to show the real-time performance of the proposed optimal method.

Model Predictive Control for Regulating Fuel Cell Stack Temperature and Air Flow Rate

Stack temperature and airflow rate are vital control problems for Proton Exchange Membrane fuel cell (PEMFC). Two separate Model Predictive Controllers (MPC) have been employed to regulate these problems. The controllers utilized Laguerre and exponential weight functions to reduce its numerical instability and computational time. The temperature MPC considered delayed and constrained coolant pump voltage as manipulated input and stack temperature as the desired output. While airflow MPC manipulated compressor motor voltage to maintain the desired level of oxygen excess ratio subjected to starvation, surge, and choke constraints. Results showed that both controllers worked well together. The desired temperature and oxygen excess ratio were maintained subjected to all the constraints, even with the presence of external disturbances. This study highlights that MPC manages to handle both control problems without any conflict. Yet, it also proves that MPC can handle a large time delay process.

Performance improvement in polymer electrolytic membrane fuel cell based on nonlinear control strategies-A comprehensive study.

A Polymer Electrolytic Membrane Fuel Cell (PEMFC) is an efficient power device for automobiles, but its efficiency and life span depend upon its air delivery system. To ensure improved performance of PEMFC, the air delivery system must ensure proper regulation of Oxygen Excess Ratio (OER). This paper proposes two nonlinear control strategies, namely Integral Sliding Mode Control (ISMC) and Fast Terminal ISMC (FTISMC). Both the controllers are designed to control the OER at a constant level under load disturbances while avoiding oxygen starvation. The derived controllers are implemented in MATLAB/ Simulink. The corresponding simulation results depict that FTISMC has faster tracking performance and lesser fluctuations due to load disturbances in output net power, stack voltage/power, error tracking, OER, and compressor motor voltage. Lesser fluctuations in these parameters ensure increased efficiency and thus extended life of a PEMFC. The results are also compared with super twisting algorithm STA to show the effectiveness of the proposed techniques. ISMC and FTISMC yield 7% and 20% improved performance as compared to STA. The proposed research finds potential applications in hydrogen-powered fuel cell electric vehicles.

Robust oxygen excess ratio control of PEMFC systems using adaptive dynamic programming

To improve the power generation efficiency and cells lifetime of proton exchange membrane fuel cells, the oxygen excess ratio (OER) control problem is investigated in this paper. With the use of the double-loop cascade control structure, our work reinforces the iteration adaptive dynamic programming (ADP) algorithm and develops a robust OER control strategy drawing upon rich tools and techniques in sliding mode control theory. Our contribution is threefold. First, the proposed OER control policy is insensitive to various dynamics and disturbances by using refined super-twisting sliding mode controller in the external control loop. Second, the ADP-based controller is employed in the internal control loop to approximate the real optimal tracking control policy. The algorithm is computationally efficient and easy to implement in hardware platforms. Third, simulation results show that the proposed OER control strategy has a quick, smooth response, which can be further refined by means of numerous advanced neural network algorithms.

Design and control of air supply system for PEMFC UAV based on dynamic decoupling strategy

The oxygen excess ratio (OER) and air pressure of the proton exchange membrane fuel cell (PEMFC) cathode need to be relatively stable to improve energy conversion and utilization. However, the OER and air pressure adjustments are coupled in the centrifugal compression system and the fluctuations of OER and pressure may cause oxygen starvation and destroy the pressure balance between anode and cathode respectively, affecting the output power and service life of PEMFC. When the PEMFC unmanned aerial vehicle (UAV) performs the mission, the nonlinearity and time-varying characteristics of the model bring greater challenges to the design and control of air supply system for PEMFC UAV. Firstly, in view of nonlinear factors such as the variable space environments and different load conditions, an air supply system model suitable for PEMFC UAV is established. Secondly, in order to supply stable voltage to the UAV load and the air compressor through the DC/DC power converter as well as reduce PEMFC output power loss and device stress, a four-phase interleaved boost converter (IBC) and controller are designed. The devised IBC and its control scheme are beneficial to decrease energy loss and strengthen the anti-interference ability for high-power PEMFC applications. What’s more, based on the air compressor dynamic model as well as the analysis of the static and dynamic characteristics of PEMFC system, the power consumption relationships among the external UAV load, the internal compressor load, and the stack source are analyzed in detail. Then, when considering the strong coupling and nonlinearity of the mass flow and the pressure under the variable-altitude centrifugal compression system, we propose a dynamic decoupling strategy for OER and pressure control based on fuzzy logic. Finally, compared with the results of PI controllers and feedforward compensation decoupling controllers, the fuzzy decoupling controllers can realize the rapid adjustment of OER and maintain the stability of air pressure robustly.

Fuzzy Logic based Control Strategies for Proton Exchange Membrane Fuel Cell System

In this work, new fuzzy logic-based control strategies have been proposed to improve the performance of the non-linear Proton Exchange Membrane Fuel Cell (PEMFC) system. This paper considers the empirical approach to designing the controller. This allows the controller’s design to be straightforward, and the controller can be fine-tuned as and when necessary. Three control strategies have been proposed in this paper, viz. Fuzzy feedforward control strategy (Strategy 1), Fuzzy feedforward controller with a feedback loop (Strategy 2), and Fuzzy feedforward controller with a feedback loop and conditional switch (Strategy 3) are proposed. The performance comparison of the proposed strategies is evaluated in terms of performance indicators such as the integral square error (ISE), integral absolute error (IAE), and total variation (TV) of the closed-loop control system. Furthermore, the proposed strategies keep the voltage at a given setpoint under load current variation. Finally, Strategy 3 is used to regulate the oxygen excess ratio (OER) at a reasonable value under load current variation to avoid oxygen starvation and keep the voltage at a defined point of a PEM fuel cell system. The results show that Strategy 3 provides better performance than other strategies

Fault-tolerant control through dynamic surface triple-step approach for proton exchange membrane fuel cell air supply systems

Due to complex electrochemical and thermal phenomena, varying operations towards automotive applications, and vulnerable ancillaries in proton exchange membrane fuel cells (PEMFCs), fault diagnosis and fault-tolerant control (FTC) design have become important aspects to improve the reliability, safety and performance of PEMFC systems. This paper presents a novel FTC scheme for automotive PEMFC air supply systems via coordinated control of the air flow rate and the pressure in cathodes. A dynamic surface triple-step approach is first proposed considering nonlinear dynamics and the multi-input multi-output (MIMO) property, which combines the advantage of dynamic surface control in avoiding an “explosion of complexity” and the advantage of triple-step control in guaranteeing a simple structure and high performance. The normal case, the faulty case at the supply manifold and the faulty case in the back pressure valve are considered in the FTC design, with the stability of the overall system proved using Lyapunov methods. MATLAB/Simulink simulations with a high-fidelity PEMFC model verify the effectiveness of the proposed FTC scheme in regulating the air flow rate and oxygen excess ratio and maintaining the pressure of the cathode at a desired level even under faulty conditions.

Novel Hybrid Control Scheme of a Proton Exchange Membrane Fuel Cell Air Supply System

A hybrid control scheme of the fuel cell air supply system based on model predictive control (MPC) and proportional integral differential (PID) control is proposed for adjusting the oxygen excess ratio at a given setpoint in this paper. In the proposed scheme, the cooperative control of air compressor and back pressure valve in the fuel cell air supply system is realized by MPC. When the system is in steady state under the MPC, PID is introduced, so that the steady‐state error, which is caused by the mismatch between the control model in MPC and the controlled system during MPC control, can be eliminated. To reduce the mismatch between the control model and the controlled air supply system, back propagation neural network and look‐up table method based on bilinear interpolation are used to calculate the output air flow rate and efficiency of the air compressor in the air supply system. Simulation results show that the proposed control scheme has a better performance compared with proportional integral control, active disturbance rejection control, and single MPC. In addition, the proposed control scheme also shows good fault tolerance in the case of air supply system failure.

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.

Analysis of Optimal Oxygen Excess Ratio and Nonlinear Tracking Control of Vehicle PEMFC Air Supply System

To a large extent, the efficiency and durability of the proton exchange membrane fuel cell (PEMFC) depend on the effective control of air supply system. However, dynamic load scenarios, internal and external disturbances, and the characteristics of strong nonlinearity make the control of complex air supply systems challenging. This paper mainly studies the modeling of PEMFC air supply system and the design of a nonlinear controller for oxygen excess ratio tracking control. First, we analyze and calibrate the system’s optimal oxygen excess ratio control target and explore how the system temperature and humidity impact it, respectively; second, a second-order affine oriented control model which can represent the static and dynamic characteristics of the air supply system is derived, and a disturbance observer is designed to estimate and compensate the “lumped error” online. Then, aiming at the problem of unmeasurable cathode pressure, a state observer based on Kalman optimal estimation algorithm is proposed to realize the real-time estimation of cathode pressure; finally, a dynamic output feedback control system based on observer and backstepping nonlinear controller is proposed, and the comparison and evaluation of two control strategies based on constant oxygen excess ratio tracking and optimal oxygen excess ratio tracking are carried out. The simulation results show the effectiveness and superiority of the designed control system compared with the reference controller.

Observer-based adaptive neural network control for PEMFC air-feed subsystem

Oxygen excess ratio (OER) tracking of polymer electrolyte membrane fuel cell (PEMFC) air-feed subsystem is an interesting topic to obtain a high energy conversion ratio. The control purpose is challenging due to the unknown nonlinear function, time-varying external disturbance and unmeasured variable. To deal with the above problems, a neural network control scheme based on tracking error observer and disturbance observer is proposed. Firstly, a canonical form of the PEMFC air-feed subsystem is developed based on the input–output feedback linearization method. Meanwhile, a tracking error observer which only uses the system output is developed for the reconstruction of the unmeasured variable. Secondly, the neural network based on a novel adaptive learning law is proposed to approximate the unknown nonlinear function. An identification model with a second-order low-pass filter is designed such that the adaptive learning law is adjusted by the observer error and modeling error, simultaneously. Finally, to reduce the effect caused by compound disturbance including the external disturbance and neural network approximation error, a nonlinear disturbance observer is proposed. The main contribution of this study is not only avoiding the prior knowledge of system uncertainty, external disturbance and the unmeasured variable but also improving the OER tracking performance under the measurement noise. Lyapunov theory analysis shows that the system tracking error is uniformly ultimately bounded. Numerical simulations and hardware-in-loop (HIL) experiments show that the proposed neural network control algorithm can maintain OER at the target value even under external disturbance. The experimental results show that the performance indexes including the mean absolute error (MAE), the root mean square error (RMSE) and the standard deviation (SD) of the proposed controller during the whole process are 0.0213, 0.0547 and 0.0538, respectively. Compared with the proportional–integral–derivative (PID) controller (MAE 0.0466, RMSE 0.0762 and SD 0.0745), robust adaptive radial basis function neural network (RBFNN-H∞) controller (MAE 0.0841, RMSE 0.0839 and SD 0.0813) and hybrid robust adaptive radial basis function neural network (HARBFNN-1st) controller (MAE 0.0255, RMSE 0.0613 and SD 0.0603), the proposed controller performs a better performance.

Cooperative control of air and fuel feeding for PEM fuel cell with ejector-driven recirculation

• A model of fuel cell system with ejector-driven recirculation is established. • Cooperative control for air and fuel feeding system of fuel cell is proposed. • The proposed control strategy has faster response and better chattering rejection. • Excess ratios of oxygen and hydrogen and pressure difference are well controlled. Air and hydrogen feeding control is a hot topic for the operation of proton exchange membrane (PEM) fuel cell system. However, there is little consideration for the cooperative control of the oxygen excess ratio, hydrogen excess ratio and the pressure balance between anode and cathode. This paper proposed an adaptive super-twisting (ASTW) based sliding mode nonlinear control to regulate air, hydrogen feeding, and pressure balance of PEM fuel cell system with ejector-driven recirculation. The target is to control the oxygen excess ratio, hydrogen excess ratio and the pressure difference on both sides of the membrane, to avoid the oxygen starvation and damage of the membrane. First, a nonlinear model of PEM fuel cell system with ejector-driven recirculation is developed, including fuel cells, ejector and auxiliary equipment. Then, an ASTW based sliding mode control is proposed to achieve the smoothing regulating actions and chattering rejection. The performance of the developed ASTW based sliding mode control strategy is verified through two simulation scenarios and compared with the conventional PID method and STW sliding mode control. Simulation results indicate that the developed control strategy can provide shorter regulation time (less than 5 s) and smaller overshoot (2.1 × 10 4 Pa) of oxygen excess ratio, hydrogen excess ratio and pressure difference on both sides of the membrane than conventional PID controller. Moreover, better chattering rejection can be achieved during various load conditions compared with STW sliding mode control.

Simulation study on the PEMFC oxygen starvation based on the coupling algorithm of model predictive control and PID

The frequent variable load operation of Proton exchange membrane fuel cell (PEMFC) may cause the mismatch with the hysteresis of gas flow and diffusion, which will lead to oxygen starvation in the stack. To improve this phenomenon, a PEMFC system model was established and simplified to a linear time-invariant system, and the optimal oxygen excess ratio (OER) curve under different currents was fitted with the control objective of maximal net power in this study. Based on this, this study compared the transient control performance under PID control and model predictive control (MPC), then unprecedentedly proposed the series and parallel coupling control algorithm of MPC and PID, respectively. Simulation results manifested that the OER responds rapidly under PID control, but along with the serious overshoot and oscillation. Conversely, MPC possesses a stable control effect owing to the ability to predict and constrain the system state. More importantly, under the parallel coupling control, the PEMFC system distinguishes itself in OER tracking, output power, and output voltage by critical superiorities of stability and response speed, in which, the error integrals ISE, IAE, ITAE, and ITSE of OER tracking reach the minimum with 0.19, 0.45, 2.85, and 0.59, respectively. The output voltage fluctuations of DC-DC converter under different control modes are similar, and errors are all less than 1%, besides, the current of the DC-DC converter is more stable under the parallel coupling control. Therefore, the parallel coupling control algorithm of MPC and PID has the optimal comprehensive control effects on improving oxygen starvation.

Modeling and Fuzzy Feedforward Control of Fuel Cell Air Supply System

As an important part of the fuel cell subsystem, the air supply system of the proton exchange membrane fuel cell (PEMFC) plays an important role in improving the output performance and durability of fuel cells. It is necessary to control the oxygen excess ratio of fuel cell systems in the process of variable load, preventing the oxygen starvation in the loading process and excessive parasitic power consumption caused by oxygen saturation. At this time, the modeling of fuel cell systems and the development of control strategies are critical. The development of a control strategy depends on the construction of the control model. Aiming at the difficulty of air supply system modeling, this paper uses radial basis function (RBF) neural network and state equation method to establish the dynamic model of air supply systems. At the same time, PID, fuzzy logic plus PID (FL + PID), feedforward plus PID (FF + PID), fuzzy feedforward plus fuzzy PID (FF + FLPID) control strategy are proposed to control the oxygen excess ratio of the system. The simulation results show that fuzzy feedforward plus fuzzy PID (FF + FLPID) has the best effect and the oxygen excess ratio can be followed in 1 s.

Air-flow control in fuel cells using delay-based load governor and feedforward augmented dynamic inversion

The frequent and sudden increase of load in electric vehicles depletes oxygen from the fuel cell cathode, leading possibly to oxygen starvation. This paper presents the design of an air flow controller for fuel cell systems (FCS) that aims to replenish the depleted oxygen. A dynamic inversion is used as the feed-forward compensation for the disturbance effect coming from the load current change. However, the inverted dynamics have negative relative degree (improper) and not physically realizable. To make it proper, an augmentation by delay is done, with the same delay added into demand current. The current-dependent time delay introduced in the demand current gives the FCS the opportunity to prepare a sufficient air flow that can withstand the dip in oxygen excess ratio (OER). Besides, the FCS plant is linearized at various operating points, and the dynamic feedforward controller is designed at several operating points with interpolation applied for in-between values. The simulation results show a considerable improvement in the OER response where the constraint is met and the recovery time is reduced.

Multi-objective optimal control for proton exchange membrane fuel cell via large-scale deep reinforcement learning

To achieve a high operation efficiency and control performance of proton exchange membrane fuel cell (PEMFC), a multi-objective optimal fractional-order proportion integration differentiation (FOPID) controller is proposed to maintain the oxygen excess ratio (OER) and output voltage at the reference value simultaneously. In addition, a novel large-scale deep reinforcement learning called demonstration curriculum strategy large-scale multi-delay deep deterministic policy gradient (DCSL-MD3PG) algorithm is designed to operate as the tuner of this controller. This algorithm applies the teacher–student large-scale deep reinforcement learning strategy as well as the demonstration curriculum learning strategy and a number of techniques in the offline training to improve robustness and optimize control ability of the controller. Simulation results show that, taking the benefits of the excellent adaptability and model-free features of deep reinforcement learning, the adaptive optimal FOPID algorithm can ensures the optimal and comprehensive control performance of OER and output voltage while satisfying the security constraints of PEMFC.

Large-scale multi-agent deep reinforcement learning-based coordination strategy for energy optimization and control of proton exchange membrane fuel cell

To maximize the net output power and ensure the safe operation of proton exchange membrane fuel cells (PEMFCs), a coordination strategy is proposed in this paper to realize better optimization and control of a cell’s oxygen excess ratio (OER). There are two agents in this strategy. An optimization agent is employed to obtain an ideal OER in different states by considering both the maximum net output power and the power control performance and safety of the PEMFC. A control agent effectively controls the OER of the PEMFC by determining the ideal OER. In addition, an evolutionary curriculum imitation large-scale multiagent deep reinforcement learning algorithm (ECILS-MADDPG) is introduced for this framework, which draws from the pedagogical technique of curriculum learning and imitation learning to improve the robustness of the coordination strategy. Proven by experiments, the proposed coordination strategy can obtain the maximum net power while maintaining OER control performance and the safety constraints of the PEMFC. Moreover, the proposed method increases the net power by up to 0.70% compared with an equivalent net power minimization strategy, and the integrated absolute error (IAE) in the OER is reduced by up to 99%.