This article proposes a novel adaptive pitch control strategy for floating offshore wind turbines (FOWTs) operating in Region III. The controller combines a supertwisting algorithm (STA) with a recurrent radial basis function neural network (RRBFNN) to estimate and compensate for external disturbances in real time. The control law is derived from a simplified nonlinear control-oriented model of a 5-MW semisubmersible FOWT, without relying on linearization. A Lyapunov-based framework guarantees closed-loop stability and online neural adaptation. The proposed RRBFNN-STA controller is implemented on a hardware-in-the-loop platform integrating OpenFAST, a CompactRIO emulator, and a Beckhoff industrial controller. Validation is performed under three realistic wind and wave conditions, including extreme turbulence scenarios. Compared to a conventional STA and a gain-scheduled proportional-integral baseline controller, the RRBFNN-STA achieves significantly lower root-mean-square errors (RMSE), reducing rotor speed tracking error from over 1.5 to 0.24 r/min, and platform pitch velocity RMSE to 0.24 degrees/s. These results confirm the robustness and realtime applicability of the proposed control approach for pitch regulation in FOWTs under nonlinear and turbulent operating conditions.

Reliable onboard estimation of the State of Health of lithium-ion batteries remains challenging because battery impedance is simultaneously affected by ageing, temperature, and operating conditions. Although Passive Electrochemical Impedance Spectroscopy enables impedance estimation under dynamic operating conditions without dedicated excitation signals, the resulting impedance measurements must account for temperature effects before being exploited for diagnostic purposes. This work proposes a diagnostic framework combining passive impedance measurements, internal temperature estimation, and supervised learning for battery SoH estimation. The approach relies on a thermal model identified using Thermal Impedance Spectroscopy to estimate the internal cell temperature and separate temperature-induced impedance variations from ageing-related effects. The charge-transfer resistance extracted from passive impedance measurements is then combined with the estimated temperature and State of Charge to estimate battery SoH using a supervised neural network. Unlike previous studies, which generally address passive impedance estimation, thermal modelling, or data-driven diagnosis separately, the proposed methodology integrates these components into a unified framework. The methodology is experimentally validated on lithium-ion cells under representative dynamic driving conditions and different ageing levels. The results demonstrate that coupling PEIS with internal temperature estimation improves the robustness of impedance-based diagnostic indicators, enabling accurate SoH estimation under realistic onboard operating conditions.

The control of Floating Offshore Wind Turbines (FOWTs) in Region III is challenging due to complex aerodynamic, hydrodynamic, and structural interactions. This paper presents a fully data-driven, model free Deep Reinforcement Learning (DRL) controller based on the Trust Region Policy Optimization (TRPO) algorithm to regulate the collective blade pitch of a 5 MW semi-submersible FOWT. The controller was trained in high-fidelity simulations and experimentally validated in a wave basin using a Software-In-the-Loop (SIL) approach. Results show improved generator speed regulation and platform stability compared to a baseline Gain-Scheduling Proportional-Integral (GSPI) controller. However, performance degradation with generator speed overshoots was observed under extreme wind conditions. This study highlights the potential of DRL for FOWT control and identifies future directions to enhance robustness in harsh environments.

This study investigates the impedance behavior of fuel cells under varying current conditions, with the aim of developing and evaluating a dynamic EIS-based fault detection methodology. Stationary Electrochemical Impedance Spectroscopy measurements were first carried out under controlled conditions to characterize the dependence of impedance on current. Based on Equivalent Circuit Models and polynomial approximations, an experimental current-dependent impedance model was established to assess the validity of impedance measurements under dynamic operating conditions. To compute impedance in this non-stationary regime, a dedicated signal processing algorithm was developed. The algorithm compensates for drift effects induced by non-stationarity and enables impedance estimation over short time windows, without requiring the control system to be held at a stationary operating point. Finally, representative driving cycles were applied to identify impedance spectra under dynamic conditions using the proposed model. The results are promising and confirm the relevance of the developed approach.

Impedance characterization of a Lithium-ion cell is a commonly used method to assess its state of health or internal temperature. However, Electrochemical Impedance Spectroscopy (EIS) requires a dedicated excitation system and can only be performed under steady-state conditions, making it challenging to implement onboard. The objective of this work is to develop a passive EIS method, which does not require an external excitation device and allows impedance estimation even under dynamic operating conditions. This paper proposes an impedance measurement approach based on the analysis of the naturally occurring harmonic content in electric vehicle driving cycles. To achieve this, a signal processing algorithm was developed to extract and analyze these harmonics while compensating for non-stationary effects related to current, State of Charge (SoC), and temperature. The methodology is validated through experimental tests reproducing realistic operating conditions.

This paper presents a novel model-based control strategy for collective blade pitch regulation in Region III of a 5 MW semi-submersible FOWT. The proposed approach combines a Radial Basis Function Neural Network (RBFNN) with a Super-Twisting Algorithm (STA) to enhance robustness against disturbances and improve dynamic performance. The control design is based on a reformulated nonlinear control-oriented model to support the synthesis of adaptive control laws. In addition to the control strategy, this work introduces a real-time Hardware-in-the-Loop (HIL) platform as a key contribution, specifically designed for the validation of FOWT control strategies. This platform provides a strictly synchronized execution framework for accurate real-time emulation and integrates OpenFAST for high-fidelity dynamic simulations. By enabling real-time validation, the HIL platform plays a crucial role in assessing control performance under realistic operating conditions. Experimental results confirm both the real-time feasibility of the proposed RBFNN-based STA controller and the effectiveness of the HIL platform in evaluating its performance, particularly in regulating generator speed and mitigating platform pitch motion under varying wind conditions.

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

This article addresses a new approach to non-invasive diagnostics of proton exchange membrane fuel cells. More specifically, it deals with the detection of several faults generated by inappropriate operating conditions using an original non-invasive methodology based on the measurement of the external magnetic field around the fuel cell stack, thanks to a specially designed ferromagnetic circuit analyzer. A large experimental campaign has been released on a fuel cell stack associated with this magnetic circuit analyzer for normal and faulty fuel cell operating conditions at different current values. These experiments provide a vector of the measured magnetic field at different positions around the fuel cell stack. Each experiment was labeled based on accurate electric and fluidic measurements realized on a laboratory fuel cell test bench. These magnetic field measurements were used as original variables for a data-driven diagnostics approach involving two steps: feature extraction and classification. Finally, the high accuracy given by this diagnostic methodology proves the ability of the new magnetic field analyzer to provide a significant (highly sensitive) information to detect local faults inside fuel cell during operation, and therefore to allow accurate discrimination between different fuel cell normal and faulty operating conditions.

A comprehensive Distribution of Relaxation Times (DRT) analysis is carried out for a proton exchange membrane fuel cell (PEMFC) in this research, to achieve stack-level fault diagnosis by Electrochemical Impedance Spectroscopy (EIS). The durability and reliability of PEMFC are still crucial challenges for its commercialization, and EIS is a potential characteristic tool for diagnosis. However, the explanation and application of EIS are still ambiguous and insufficiently exploited for the PEMFC stack. DRT is a useful tool for EIS analysis, but it has not been applied to stack-level PEMFC diagnosis yet. In this work, to explore the possibility of using DRT as a PEMFC diagnosis tool, experiments are designed and carried out under different operating conditions, while the current, stoichiometry of the cathode, stoichiometry of the anode, temperature, and relative humidity are adjusted. Further, the detailed method to analyze PEMFC EIS data by DRT is presented, and the relationship between the PEMFC fault and DRT is built according to massive experiments and quantitative analysis. The DRT peaks can be assigned to the corresponding inner processes, and the diagnostic significance of the DRT features is quantitatively compared. Additionally, a novel equivalent circuit model (ECM) structure is proposed according to the DRT analysis, which is composed of 6 resistor–capacitor circuits that correspond to the inside processes, thus significantly contributing to stack-level fault diagnosis.

Purpose The purpose of this paper is to propose a new method that allows to compare the magnetic pressures of different pulse width modulation (PWM) strategies in a fast and efficient way. Design/methodology/approach The voltage harmonics are determined using the double Fourier integral. As for current harmonics and waveforms, a new generic model based on the Park transformation and a dq model of the machine was established taking saturation into consideration. The obtained analytical waveforms are then injected into a finite element software to compute magnetic pressures using nodal forces. Findings The overall proposed method allows to accelerate the calculations and the comparison of different PWM strategies and operating points as an analytical model is used to generate current waveforms. Originality/value While the analytical expressions of voltage harmonics are already provided in the literature for the space vector pulse width modulation, they had to be calculated for the discontinuous pulse width modulation. In this paper, the obtained expressions are provided. For current harmonics, different models based on a linear and a nonlinear model of the machine are presented in the referenced papers; however, these models are not generic and are limited to the second range of harmonics (two times the switching frequency). A new generic model is then established and used in this paper after being validated experimentally. And finally, the direct injection of analytical current waveforms in a finite element software to perform any magnetic computation is very efficient.

The need for greener energy sources their storage, management and monitoring, are at the core of research around the globe. This paper focuses on the first step of developing an embedded system that is capable of conducting fast online impedance spectroscopy of batteries for integrated monitoring throughout the battery life cycle in real-world industrial and residential applications such as electric vehicles, energy storage systems in energy grids etc. It significantly covers the experimental methodology to conduct online EIS data its treatment and conversion into the frequency domain by Fast Fourier transformation. Selection of an equivalent circuit model to define various electrochemical characteristics of Li-Ion batteries. Further, it describes the exploitation of EIS data by an optimisation algorithm to estimate the parameters of equivalent circuit model closely resembling that to Li-battery, accuracy between various optimisation methods is compared. Finally, the proposed online EIS-ECM modelling methodology is validated by experimental tests and the results prove the capability of the test bench to design and evaluate monitoring methodology before their embedded implementation.

This paper presents an approach to enhance the performance of floating offshore wind turbines mounted on semi-submersible platforms through the integration of a Super-Twisting Sliding Mode Collective Blade Pitch Controller (STSM-CBPC) with a Recurrent Radial Basis Function Neural Network (RRBFNN). The proposed CBPC, developed based on a refined nonlinear control-oriented model, leverages the RRBFNN as an adaptive estimator to address lumped uncertainties and external disturbances, when operating above the rated wind speed. The recurrent neural network features a dual feedback loop structure. The internal feedback loop, operating on the hidden layer, and the external feedback loop, enabling the transmission of the output signal back into the input signal, collectively contribute to a comprehensive capture of the system's state information. To ensure convergence, adaptive laws governing the neural network are derived through Lyapunov analysis, ensuring real-time updates to the RRBFNN parameters. Simulation results demonstrate the superior performance of the proposed CBPC over the baseline gain scheduling proportional integral controller for regulating rotor speed and mitigating platform motion.

For electric vehicle motors, it is essential to control the sources of loss which could reduce the overall efficiency of the system. In this context, this article aims to present a detailed comparison of machine and inverter losses between the space vector pulse width modulation (SVPWM) and the discontinuous pulse width modulation 2 (DPWM2). To compare both PWM strategies on the WLTC (worldwide harmonized light vehicles test cycles) cycle in an efficient way, a full analytical model is used to predict inverter losses and a sequential use of an analytical and a finite element analysis is used to perform an in-depth analysis of machine losses. This semianalytical method which can be used for any pulse width modulation (PWM) strategy allows to speed up the calculations and the comparison of different PWM schemes. Experiments were then conducted for different operating points and supply voltage levels to validate the results. The theoretical and experimental results show that although the DPWM2 allows to reduce inverter losses, it still generates more machine losses compared to the SVPWM especially on the WLTC cycle, which makes the DPWM2 less efficient. Nevertheless, depending on the total supply voltage, the DPWM2 could be more or less interesting on the WLTC cycle with comparison with the SVPWM.

To address the robust speed control problem of the surface‐mounted permanent‐magnet synchronous motor (SPMSM) drive, a novel composite speed controller (CSC) is proposed, which consists of the modified super‐twisting sliding‐mode SC (STSM‐SC) and the extended state observer (ESO). Regarding the motion dynamics of the SPMSM drive, the unmodeled dynamics, endogenous, and exogenous disturbances are lumped together as the total disturbance. The ESO‐based feedforward compensation mechanism is used to deal with the total disturbance for the robustness improvement of the speed control achieved by the modified STSM‐SC‐based feedback regulation mechanism. On the basis of the field‐oriented control strategy, performance comparisons of the standard STSM‐SC, the ESO‐based standard STSM‐SC, the modified STSM‐SC, and the proposed CSC are carried out on a suitably developed experimental setup. The experimental results are presented to validate the superiority of the proposed CSC.

The floating offshore wind turbine (FOWT) technology has great energy potential, however, minimizing the movement of the structure, under the combined effect of wind and waves, while ensuring maximum power extraction on all operating ranges remains a challenge. This paper proposes the design of a deep reinforcement learning (DRL) controller for FOWTs in the operating area III. To our knowledge, this is the first time that DRL-based control approach is used for this application. The proposed DRL controller is based on trust region policy optimization (TRPO) algorithm, composed of two neural networks, the actor and the critic networks, for the learning of the optimal control law. Simulation results and comparison study are provided to validate the proposed DRL controller for the 5-MW baseline ITI Barge wind turbine model on OpenFAST.

Li-Ion batteries are among the key enablers of more sustainable use of energy. However, they need to be supervised and undergo continuous maintenance to assure safety and longevity. This paper focuses on the sensorless detection of the State of Temperature (SOT) of the Li-ion batteries during the operational life cycle of the battery irrespective of its state of charge. The paper presents the new Intelligent Gray Box Model (IGBM) to detect the SOT of Li-ion cells: that combines the three most powerful diagnostic tools Electrochemical Impedance Spectroscopy (EIS), Equivalent Circuit Model (ECM), and Artificial Neural Network Classifier (NNC). The work introduces the experimental test bench capable of emulating real-world and embedded constraints to conduct EIS onboard, its data preprocessing, and useful information extraction for the entire frequency spectrum. Furthermore, this paper presents a new hybrid parameter identification that combines the Whale Optimization Algorithm (WOA) and Levenberg Marquardt algorithm (LM) to identify the fractional order ECM. Finally, a neural network classifier is designed, optimized, and compared with different feature scaling techniques to evaluate its accuracy and robustness to detect and classify exact battery temperatures in real time from experimental data.

An original non-invasive methodology of the fuel cell diagnosis is proposed to identify different positions of the faults in Proton Exchange Membrane Fuel Cell (PEMFC) stacks from external magnetic field measurements. The approach is based on computing the external magnetic field difference between normal and faulty PEMFC operating conditions. To evaluate the external magnetic field distribution, in this paper, we propose an improved design of the magnetic field analyzer. This analyzer amplifies the magnetic field around the cell to perform an accurate detection of the fault position. Moreover, the main contribution of this work is represented by conceiving and implementing a 3D multi-physical current distribution emulator of a proton exchange membrane fuel cell. The new concept of a proton exchange membrane fuel cell emulator has been specially designed to emulate the magnetic field of a real fuel cell stack. This emulator concept is also beneficial for a new model of the fuel cell, which implies a multi-physical coupling between electrochemical electric conduction and the generated magnetic field. Finally, finally, the numerical model and the emulator have been involved in the realization of numerical simulations and experimental analysis to prove the ability of the system to detect and localize 3D faults.

This paper develops a super-twisting sliding-mode observer-based model reference adaptive speed controller (STSMO-MRA-SC) for the permanent-magnet synchronous motor-based variable speed drive (PMSM-VSD) system. A stable first-order linear model is selected as the reference model to describe the required speed trajectory. To make the actual speed of the PMSM-VSD system follow this trajectory, the proposed STSMO-MRA-SC comprises three terms: (1) the stabilization term dependent on known parameters of the motion dynamics and the selected reference model for stabilizing the speed tracking error dynamics asymptotically; (2) the disturbance compensation term based on the STSMO for compensating the lumped disturbance in the speed tracking error dynamics; and (3) the error compensation term updated online by the adaptive law for confronting the estimation error of the STSMO in practice. Comparative experimental tests among the classic MRA-SC, the radial basis function neural network-based MRA-SC and the proposed STSMO-MRA-SC are performed. Experimental results have verified the effectiveness and the superiority of the proposed STSMO-MRA-SC.

A new diagnostic method based on adaptive neural fuzzy inference system (ANFIS) and electrochemical impedance spectroscopy (EIS) is proposed for the proton exchange membrane fuel cell (PEMFC) system. Firstly, a new parameter identification method that combines genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is proposed to identify the fractional-order equivalent circuit model (ECM), in which the anode impedance, cathode impedance, and mass transfer are all considered. This new method allows better exploitation of the EIS diagrams, and the internal relationships between the fault conditions and the ECM parameters are thoroughly analyzed according to it. Then, based on these relationships, a new diagnostic algorithm based on k means clustering and ANFIS is designed to precisely identify several faults that can occur in the PEMFC, such as membrane flooding, drying, and mass transfer fault. Finally, the effectiveness of this method is demonstrated experimentally through the exploitation of EIS data under different faults and operating conditions of the PEMFC.