Groupe de Recherche en Énergie Électrique de Nancy

The SuperRail project is the world’s first installation of a high-temperature superconducting (HTS) cable system for commercial operation in a railway electric grid. The aim is to increase traffic capacity and achieve national low-carbon emission targets by strengthening the railway grid. The project involves the development, manufacture, installation, and operation of an HTS DC cable system at Paris’ Montparnasse railways station. Due to limited available rights-of-way, conventional cable technologies are not feasible, making HTS cable technology the only viable solution for supplying power to the railway tracks in such a dense urban area. Two HTS cables are installed in parallel. Each one can carry 2.25 MW and support fault conditions of 67 kA for 100 ms due to their direct connection to the transmissions grid. In order to validate the HTS cable system design and the accessories performances, a complete 35-m HTS cable type test loop was installed and tested at the SNCF Railway Test Agency. This paper shows the results, including dielectric performance, current carrying capacity and fault current tests. The HTS cable system successfully passed all the tests, and the design has been fully qualified.

Superconducting motors have high power densities and thus have a high potential to be used for the propulsion of large electric aircraft. However, superconductors also dissipate ac loss when they are subject to a time-varying magnetic field or when they are carrying ac current. Refining an analytical machine evaluation model in the literature, this paper provides a fast analytical model that incorporates new ac loss formulae to model machines with a superconducting MgB₂ armature, and rotor excitation via either permanent magnets (PMs) or superconducting coils. Machines with and without an iron yoke are modeled. In addition, the machine models are used in multiobjective optimization to explore the trade-off between minimizing the active mass and minimizing the armature ac loss, using the optimization package “pymoo” in Python. The machines are rated at 3 MW, 4,500 rpm, based on NASA's N3-X concept aircraft. Results suggest that the lighter, yokeless machines do not have much higher armature ac loss than the machines with yoke (difference less than 0.1% of machine power), which may be contrary to expectation.

This paper presents a novel Model-Free Deadbeat Predictive Current Controller (MF-DBPC) tailored for grid connected two-level inverters incorporating resistance-inductance (R-L) filters. Unlike traditional approaches, the MF-DBPC leverages a data-driven model derived solely from current and voltage measurements, eliminating the need for explicit systems parameter inputs. Central to the MF-DBPC’s functionality is an Auto-Regressive with Exogenous Input (ARX) model, complemented by a Recursive Least Squares (RLS) estimator for real-time parameter identification. This strategy offers enhanced adaptability to dynamic system conditions and achieve robustness against parameter mismatches inherent in grid-connected inverter systems. To demonstrate this, two distinct model-free predictive control strategies have been benchmarked: one grounded in the deadbeat control principle and the other utilizing a rolling optimization technique. Simulation analyses demonstrate that the MF-DBPC, driven by the deadbeat principle, yields superior current waveform quality while requiring a sampling frequency five times lower than its rolling optimization technique. Experimental validation further confirms the efficacy of the MF-DBPC across steady-state and dynamic performance metrics. Notably, its robustness against filter inductance mismatches is highlighted, showcasing resilience under challenging real-world conditions.

ABSTRACTObjectivesBladder diary (BD) is an important tool to understand patients' lower urinary tract symptoms. But few data exist on adult neurogenic lower urinary tract dysfunction (ANLUTD) patients. The aim was to assess in real‐life practice the feasibility, reliability and influencing factors of BD completion in patients with ANLUTD.Materials and MethodsAll patients with ANLUTD who had a first neuro‐urologic consultation between January 2022 and April 2023 were included. No patients underwent intermittent self‐catheterization. We collected demographic data, validated questionnaires about symptoms and quality of life, the presence and analysis of the 3‐day BD. BD was classified as feasible if patients returned it nonempty and was deemed reliable if patients had completed at least 2 consecutive days, if it did not have an intake‐output imbalance, and if it were consistent with the USP questionnaire. We investigated influencing factors of feasibility and reliability with a general linear regression.ResultsOne hundred and nighty‐nine patients were included (mean age 56 ± 14; 111 (55.7%) women). Eighty‐four (42.2%) had demyelinating disease, 55 (27.6%) Parkinson's disease, 51 (25.6%) neuro‐genetic disorder and 7 (4.5%) spinal cord injury or cauda equina syndrome. BD were classified as feasible for 128 (64%) patients and 45 (40.5%) were deemed reliable. In a generalized linear model, no factor was associated with BD feasibility or reliability.ConclusionsIn a ANLUTD population, the BD is a feasible tool, but its interpretation should be met with caution due to its limited reliability. No factor was associated with the feasibility or reliability of the BD.

In various applications, the reliable and efficient detection of faults in electric machines is crucial, particularly in environments with high noise levels. To this end, the current study introduces an effective fault detection model utilizing the differential of Short-Time Fourier Transform (STFT) and a channel-wise regulated Convolutional Neural Network (CNN). The novel use of the differential of STFT is presented to enhance the diagnostic model's performance in noisy conditions compared with the conventional STFT. According to the inherent time-frequency domain information within the differential of STFT, a regulated CNN-based model is proposed to integrate spatio-temporal information into the feature map, thereby enhancing accuracy and reducing the computational demand. The method is evaluated on three datasets: the widely used Case Western Reserve University (CWRU) benchmark featuring bearing fault and vibration measurements, a dataset involving Permanent Magnet Synchronous Motor (PMSM) data with varying levels of Inter-Turn Short-Circuit (ITSC) fault and current measurements, and a dataset consisting of a mixture of mechanical and electrical faults. Comparative analysis highlights the superior performance of the proposed model over existing robust methods in the literature under both normal and noisy conditions.

Development and modeling of proton exchange membrane fuel cells (PEMFCs) need accurate identification of unknown factors affecting mathematical models. The trigonometric function-based sine cosine algorithm (SCA) may solve such problems, but it traps in local optima, making it inappropriate for larger optimization tasks. This paper introduces a novel multi-trial vector-based sine cosine algorithm (MTV-SCA) for the identification of seven unknown parameters of PEMFCs. The proposed MTV-SCA incorporates MTV methodology utilizing three control parameters to achieve the desired optimization targets. A key contribution of this work is the development of four distinct search strategies, to mitigating early convergence issues. These strategies leverage various sinusoidal and cosinusoidal factors to improve the algorithm. The optimization goal is to minimize sum square error (SSE) between measured and simulated stack voltages. Five PEMFC stack mode:250W, BCS 500W, SR-12, H-12, and Temasek 1 kW, validate the MTV-SCA algorithm’s efficacy and robustness Compared to previously established optimization approaches, MTV-SCA extracts optimum PEMFC parameters more accurately and reliably. Statistical testing further demonstrates the method’s durability and consistency. Analyzing PEMFC performance at different pressures and temperatures helps verify the adjusted parameters. Simulations show that the MTV-SCA solves difficult PEMFC parameter identification issues better than SCA and other approaches.

This paper presents the design, simulation, and experimental validation of a constant current–constant voltage (CC–CV) wireless electric vehicle (EV) charging system utilizing Power Balance Control (PBC) in both primary-side (PS) and secondary-side (SS) converter configurations. The system was tested using a step-resistance load and a typical 72 V, 50 Ah lithium-ion NMC battery to assess control precision, dynamic behavior, and charging efficiency of the system. Experimental results demonstrate that both PS and SS converters employing PBC successfully achieve stable CC–CV operation. The SS converter achieved a peak efficiency of 82%, outperforming 77% from the PS converter. Regarding transient performance, the PBC significantly enhanced settling times over the conventional proportional–integral (PI) controller. Specifically, the settling time was reduced from 600 ms to 100 ms in the PS converter and from 400 ms to 200 ms in the SS converter, with minimal voltage overshoot. Power loss analysis indicated that the highest losses occurred in CC mode, particularly in the inductive power transfer (IPT) coils and high-frequency inverters. The SS control effectively minimized these losses by dynamically adjusting its input resistance to match the optimal load resistance. Overall, the proposed PBC approach, especially in the SS converter, demonstrates efficiency, faster dynamic response, and robust power regulation for next-generation wireless EV charging systems.

The state of charge (SOC) of lithium-ion batteries needs to be accurately estimated for safety and reliability purposes. For battery packs made of a large number of cells, it is not always feasible to design one SOC estimator per cell due to limited computational resources. Instead, only the minimum and the maximum SOC need to be estimated. The challenge is that the cells having minimum and maximum SOC typically change over time. In this context, we present a low-dimensional hybrid estimator of the minimum (maximum) SOC, whose convergence is analytically guaranteed. We consider for this purpose a battery consisting of cells interconnected in series, which we model by electric equivalent circuit models. We then present the hybrid estimator, which runs an observer designed for a single cell at any time instant, selected by a switching-like logic mechanism. We establish a practical exponential stability property for the estimation error on the minimum (maximum) SOC thereby guaranteeing the ability of the hybrid scheme to generate accurate estimates of the minimum (maximum) SOC. The analysis relies on non-smooth hybrid Lyapunov techniques. A numerical illustration is provided to showcase the relevance of the proposed approach.