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Control Of Fuel Cell Power Systems Principles Modeling Analysis And Feedback Design


 Accurately estimating the state of charge (SOC) of lithium-ion batteries is critical for developing more reliable and efficient electric vehicles. However, the commonly used models cannot simultaneously extract effective spatial and temporal features from the original data, leading to an inefficient SOC estimation. This paper proposes a novel neural network method for accurate and robust battery SOC estimation, which incorporates the temporal convolutional network (TCN) and the long short-term memory (LSTM), namely TCN-LSTM model. Specifically, the TCN is employed to extract more advanced spatial features among multivariate variables, and the LSTM captures long-term dependencies from time-series data and maps battery temporal information into current SOC and historical inputs. The proposed model performs well in various estimation conditions. The average value of mean absolute error, root mean square error, and maximum error of SOC estimation achieve 0.48%, 0.60%, and 2.3% at multiple temperature conditions, respectively, and reach 0.70%, 0.81%, and 2.7% for a different battery, respectively. In addition, the proposed method has better accuracy than the LSTM or TCN used independently and the CNN-LSTM network. The computational burden with varying length of input is also investigated. In summary, experiment results show that the proposed method has excellent generalization and robustness.

State of Charge Estimation for Lithium-Ion Batteries Based on TCN-LSTM Neural Networks

The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputacion Foral de Alava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work.

High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control

Novel co-estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium-ion battery

Internal temperature prediction model of the cylindrical lithium-ion battery under different cooling modes

Based on full and rigorous study, this paper addresses the issue of ensuring a feasible association in practice between a Proton Exchange Membrane Fuel Cell (PEMFC) and DC-DC power converters including a buck and boost converter. This association is mathematically modeled, analyzed, and controlled by an optimal PID controller. Using absolute stability tools with a rigorous analytical approach that takes into consideration windup effects, in addition to the nonlinear behavior of the Proton Exchange Membrane Fuel Cell, sufficient conditions are provided to ensure that the closed-loop system is L2-stable. These conditions allow the optimal PID controller parameters to be chosen and ensure the closed-loop system stability, by tracking reference outputs, and an optimal performance against perturbations. Formal analysis, numerical simulations, and experimental validation were carried out to verify that the PID controller designed with an anti-windup action is robust and meets all of the pre-defined objectives. Moreover, this study demonstrates that the association between the PEMFC and the DC-DC converters is achieved if only certain conditions are met.

https://www.mdpi.com/2079-9292/9/11/1889/pdf

Modeling, Analysis and Experimental Validation of the Fuel Cell Association with DC-DC Power Converters with Robust and Anti-Windup PID Controller Design

The present work investigates different models of polymer electrolyte membrane fuel cell. More specifically, three models are studied: a nonlinear state-space model, a generic dynamic model integrated into MATLAB/Simulink, and an equivalent RC electrical circuit. A new equivalent electrical RL model is proposed, and the methodology for determining its parameters is also given. An experimental test bench, based on a 1200-W commercial PEMFC, is built to compare the static and dynamic behaviour of the existing models and the proposed RL model with the experimental data. The comparative analysis highlights the advantages and drawbacks of each of these models. The major advantages of the proposed RL model lie in both its simplicity and its ability to provide a similar transitory behaviour compared to the commercially manufactured PEMFC employed in this research.

New Equivalent Electrical Model of a Fuel Cell and Comparative Study of Several Existing Models with Experimental Data from the PEMFC Nexa 1200 W.

This paper deals with the problem of sampled-data observer design to estimate the state of health (SoH), the state of charge (SoC), and the internal temperature for batteries The observation is ba

Sampled-Data Observer for Estimating the State of Charge, State of Health, and Temperature of Batteries

It is well known that the classical topologies of Buck–Boost converters drain pulsating current from the power source. These pulsating currents entail acceleration of the aging rate of the fuel cell. In this paper, we are considering a Buck–Boost DC–DC converter topology featuring continuous input current. The converter interleaved structure ensures the substantial increase in power density compensating power losses related to the converter switching nature. The control objective is to enforce the DC-bus voltage to track its desired value despite load uncertainties and to ensure adequate current sharing between the different parallel modules of the fuel cell interleaved Buck–Boost converter (FC-IBBC). The point is that the internal voltage of the fuel cell is not accessible for measurement. Therefore, the state-feedback control, which consists of nonlinear control laws, is designed on the basis of a nonlinear model of the FC-IBBC system. We formally prove that the proposed controller meets its objectives, i.e., DC-bus voltage regulation and equal current sharing. The theoretical proof relies on the asymptotic stability analysis of the closed-loop system using Lyapunov stability tools. The theoretical results are well confirmed both by simulation, using MATLAB®/Simulink®, and by experimental tests using DS 1202 MicroLabBox.

/pdf/state-feedback-control-of-interleaved-buck-boost-dc-dc-power-2gk2uf9b.pdf

State-Feedback Control of Interleaved Buck–Boost DC–DC Power Converter with Continuous Input Current for Fuel Cell Energy Sources: Theoretical Design and Experimental Validation

This work presented a nonlinear control for a reversible power buck–boost converter (BBC) in order to control energy storage in a supercapacitor (SC) used in hybrid electric vehicles (HEV). The aim was to control a power converter in order to satisfy the following two requirements: (i) perfect tracking of SC current to its reference signal and (ii) asymptotic stability of the closed-loop system. The two objectives were achieved using an integral sliding mode control. In order to validate the proposed approach, an experimental prototype was built. The controller was integrated into dSPACE prototyping systems using the DS1202 card. It was clearly shown, using formal analysis, simulation, and experimental results, that the designed controller metall the objectives, namely, the stability of the system and the control of the current at its reference.

https://www.mdpi.com/2032-6653/11/3/49/pdf

Zakariae El Idrissi

Papers

Modeling, Analysis and Experimental Validation of the Fuel Cell Association with DC-DC Power Converters with Robust and Anti-Windup PID Controller Design

New Equivalent Electrical Model of a Fuel Cell and Comparative Study of Several Existing Models with Experimental Data from the PEMFC Nexa 1200 W.

Sampled-Data Observer for Estimating the State of Charge, State of Health, and Temperature of Batteries

State-Feedback Control of Interleaved Buck–Boost DC–DC Power Converter with Continuous Input Current for Fuel Cell Energy Sources: Theoretical Design and Experimental Validation

Theoretical Design and Experimental Validation of a Nonlinear Controller for Energy Storage System Used in HEV