Analysis of non ambiguous BOC signal acquisition performance Acquisition

The Binary Offset Carrier planned for future GNSS signal, including several GALILEO Signals as well as GPS M-code, presents a high degree of spectral separation from conventional signals. It also greatly improves positioning accuracy and enhances multipath rejection. However, with such a modulation, the acquisition process is made more complex. Specific techniques must be employed in order to avoid unacceptable errors. This paper assesses the performance of three method allowing to acquire and track BOC signal unambiguously : The Bump-jumping technique, The "BPSK-like" technique and the subcarrier Phase cancellation technique

Classical EIS and square pattern signals comparison based on a well-known reference impedance

International audienceElectrochemical impedance spectroscopy or ac impeda nce methods are popularly used for the diagnosis of electrochemical generators (batteries or fuel cell) . It is now possible to acquire and quantitatively interpret the experimental electrical impedances of such syst ems, whose evolutions indirectly reflect the modifications of the internal electrochemical proce ss. The scope of these measurement methods is to identify the frequency response function of the sys tem under test by applying a small signal perturbat ion to the system input, and measuring the corresponding r esponse. Once identified, and according to the application, frequency response functions can provi de useful information about the characteristics of the system. Classical EIS consists in applying a set of frequency-controlled sine waves to the input of th e system. However, the most difficult problem is the integration of this type of measuring device in embedded systems. In order to overcome this problem , we propose to apply squared pattern excitation signals to perform such impedance measurements. In this paper, we quantify and compare the performance of classical EIS and the proposed broadband identif ication method applied to a well-known impedance circuit

Optimisation d'une chaîne de réception pour signaux de radionavigation à porteuse à double décalage (BOC) retenus pour les systèmes GALILEO et GPS modernisé

Avec le développement de nombreux systèmes de navigation, la nécessité de partager efficacement la bande spectrale allouée aux nombreux signaux de ces futurs systèmes est apparue. Dans ce souci, la sous-modulation BOC a été retenue pour un grand nombre de signaux GNSS. Cette sous-modulation présente non seulement de très bonnes propriétés en terme de séparation spectrale, mais apporte aussi une meilleure précision et une robustesse accrue vis à vis des multitrajets. Néanmoins, l'utilisation de cette sous-modulation BOC rend l'acquisition des signaux plus complexe. Ce travail de thèse concerne l'optimisation d'une chaîne de réception de signaux BOC, et des signaux composites dérivés du BOC. Nous avons analysé les problèmes que pose l'utilisation de cette modulation lors de l'acquisition du signal, celle-ci étant rendue ambiguë. Plusieurs algorithmes résolvant ce problème d'ambiguïté ont été évalué. Les résultats ont été validés grâce à un simulateur de récepteur. Ensuite, l'étude s'est focalisée sur l'acquisition des signaux BOC en présence de multitrajets. Après une analyse approfondie de l'impact des multitrajets sur le traitement des signaux BOC, une étude visant à obtenir une forme optimisée du discriminateur de boucle de code a été menée. Utilisant au mieux les caractéristiques des signaux BOC, ce discriminateur a été recherché sous la contrainte de lutter le plus efficacement possible contre les multitrajets sans pour autant dégrader la robustesse face au bruit. Une autre méthode originale de réduction de l'erreur due aux multitrajets basée sur un concept différent a été proposée et analysée. Cette méthode très simple affiche de très bonnes performances.TOULOUSE-ISAE (315552318) / SudocSudocFranceF

Robust and adaptive online estimation of Li-ion battery cell capacity

International audienceThe proposed algorithm aims to estimate the instantaneous capacity of an electrical battery. The battery capacity is a crucial parameter of the battery management system and the knowledge of its value is necessary for the state of charge, state of function and remaining useful life estimators. Moreover, operation of lithium batteries raises safety issues, and the very accurate estimation of the battery capacity makes it possible to always guarantee its use in its Safe Operating Area (SOA). As a starting point, the classical Coulomb counting relation is taken as an observation equation in a Kalman framework based on a state equation of the capacity evolution. The observation equation is a biased linear relation with the variables of interest being affected by errors and corrupted by outlier samples. A random consensus resampling is thus applied to reject these outliers and a pre-estimation of the ordinate at the origin of the affine function is performed. The bias reduction is performed thanks to a modified hough- transform. This hough-transform uses the a-priori prediction of the capacity and a multi-resolution recursive approach is used to optimize the bias reduction while limiting the computational complexity. The algorithm is robust tothe presence of a high level of outlier and the Kalman approach makes the estimation adaptive. Moreover a statistical test is realized to determine whether the estimated capacity should be directly injected back in the SOC estimator or not. Our algorithm is tested on a database delivered by the NASA DashLink plateform. Statistical simulations confirm the robustness and the adaptive nature of the method. The obtained RMS of the relative errors over the entire ageing cycles are below 3%

Multi-channel extended Kalman filter for tracking BOC modulated signals in the presence of multipath

Multipath is one of the main error sources when tracking signals in the GNSS systems. The presence of reflected signals gives place to a bias when estimating the propagation delay of the direct signal. There are several ways for mitigating this multipath problem. Several solutions have already been discussed for different techniques such as Narrow Correlator, Double delta or Early1/Early2 where the key factor concerns the discriminator curve shape to be used in a conventional DLL(delay lock loop)

An enhanced correlation processing multipath mitigation technique for BOC signals

Multipath is an issue of paramount importance in the GNSS context. The presence of reflected signals gives place to a worrying bias when estimating the propagation delay of the direct signal. This paper presents the design and the evaluation of a correlation processing technique that computes an estimation of the tracking error induced by the presence of multipath. This technique is especially designed for BOC signals and attempt to exploit the particular shape of its autocorrelation function

Solving the correlation peak ambiguity of BOC signals

The Binary Offset Carrier (BOC) modulation is planned for future GNSS signal. This modulation improves positioning accuracy and enhances multipath rejection. However this modulation brings some drawbacks resulting from the representation of the autocorrelation function (ACF). The ACF presents multiple peak bringing about risk of false acquisition or false tracking, especially in a noisy environment. This paper presents a new efficient alternative technique allowing to acquire BOC signals unambiguously. It is based on a non linear quadratic operator called Teager-Kaiser (TK) operator. This TK operator has shown a high efficiency to mitigate multipath on classical C/A GPS signals and can be very simply implemented [7]. In this study, we analyze the effectiveness of this operator to exploit the structure of the correlation function between the received signal and the reference BOC signal. So it is shown that the operator can be defined as an unambiguous solution for BOC acquisition. It is also adapted for DLL loop monitoring by detecting false peak tracking

Towards physics-informed machine learning-based predictive maintenance for power converters – A review

International audiencePredictive maintenance for power electronic converters has emerged as a critical area of research and development. With the rapid advancements in deep learning techniques, new possibilities have emerged for enhancing the performance and reliability of power converters. However, addressing challenges related to data resources, physical consistency, and generalizability has become crucial in achieving optimal strategies. This comprehensive review article presents an insightful overview of the recent advancements in the field of predictive maintenance for power converters. It explores three paradigms: model-based approaches, data-driven techniques, and the emerging concept of physics-informed machine learning (PIML). By leveraging the integration of physical knowledge into machine learning architectures, PIML holds great promise for overcoming the aforementioned concerns. Drawing upon the current state-of-art, this review identifies common trends, practical challenges, and significant research opportunities in the domain of predictive maintenance for power converters. The analysis covers a broad spectrum of approaches used for parameter identification, feature engineering, fault detection, and remaining useful life estimation (RUL). This article not only provides a comprehensive survey of recent methodologies but also highlights future trends, serving as a resource for researchers and practitioners involved in the development of predictive maintenance strategies for power converters

Electrochemical Noise Analysis applied to new generation Li-ion batteries

International audienceThis work aims to assess the possibility for Electrochemical Noise Analysis (ENA) to be used as a diagnosis method for new generation Li-ion cells (LNMO, LTO, TNO). Since it is only based on electrical fluctuations generated by the system, this method has the undisputed advantage of being non-invasive, operando and low cost. Plus, the implementation of this method into the current systems would not require any additional hardware, since cell voltage measurements is mandatory in a battery pack. The latter point is critical for economical and practical concerns. Nevertheless, the very low level of voltage fluctuations (<μV) provided by the batteries renders a proper measurement challenging. That may explain why the literature is still poor on this application. In this work, an ultra low noise instrumentation has been purposely designed and realized to extract the voltage noise signature of four Li-ion coin cells with different electrode materials. The noise of the developed conditioning circuit has been characterized, and demonstrates the ability to perform measurements with an ultra-low background noise compatible with the targeted requirements for the ENA application. The estimation of the power spectral density of the signal allows a statistical description of the cells voltage fluctuations under different operating conditions. It is shown that the noise signature has a 1/f flicker nature, regardless the cell type. The noise level apparently depends on electrode materials (figure 1). It is also found out that the voltage noise intensity is proportional to the DC current load (figure 2). Finally, overcharging of a Li/LNMO cell seems to induce sharp transient in the voltage signal. This work demonstrates the high sensitivity of the ENA method to operating conditions, and thus its great potential for new generation Li-ion batteries diagnosis

Electrochemical Noise Analysis applied to new generation Li-ion batteries

International audienceThis work aims to assess the possibility for Electrochemical Noise Analysis (ENA) to be used as a diagnosis method for new generation Li-ion cells (LNMO, LTO, TNO). Since it is only based on electrical fluctuations generated by the system, this method has the undisputed advantage of being non-invasive, operando and low cost. Plus, the implementation of this method into the current systems would not require any additional hardware, since cell voltage measurements is mandatory in a battery pack. The latter point is critical for economical and practical concerns. Nevertheless, the very low level of voltage fluctuations (<μV) provided by the batteries renders a proper measurement challenging. That may explain why the literature is still poor on this application. In this work, an ultra low noise instrumentation has been purposely designed and realized to extract the voltage noise signature of four Li-ion coin cells with different electrode materials. The noise of the developed conditioning circuit has been characterized, and demonstrates the ability to perform measurements with an ultra-low background noise compatible with the targeted requirements for the ENA application. The estimation of the power spectral density of the signal allows a statistical description of the cells voltage fluctuations under different operating conditions. It is shown that the noise signature has a 1/f flicker nature, regardless the cell type. The noise level apparently depends on electrode materials (figure 1). It is also found out that the voltage noise intensity is proportional to the DC current load (figure 2). Finally, overcharging of a Li/LNMO cell seems to induce sharp transient in the voltage signal. This work demonstrates the high sensitivity of the ENA method to operating conditions, and thus its great potential for new generation Li-ion batteries diagnosis