Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Engineering science >> Automatics, Remote handling
2 proposition(s).

Machine learning for smart management of next generation batteries with reconfigurable architecture

Département de l'Electricité et de l'Hydrogène pour les Transports (LITEN)

Laboratoire Electronique avancée, Energie et Puissance

01-10-2019

SL-DRT-19-0379

vincent.heiries@cea.fr

Located within the MINATEC campus in Grenoble, CEA-Leti's main mission is to create innovation and transfer it to the industry, by generating research results that prepare for medium and long-term industrial exploitations, positioning its research between academic research and industrial R&D. Within LETI/DSYS, the Sensor Systems, Electronics for Energy Department's mission is to design and manufacture innovative systems to meet the needs of industrial innovation in a wide variety of fields, from automotive to sports and construction. The skills that are involved range from electronics to physics, electromagnetism, magnetostatics, signal processing and applied mathematics. The PhD thesis will take place in the SSCE Service, in the Laboratory of Advanced Electronics and Electronics for Power (LETI/DSIS/SSCE/L2EP). L2EP develops solutions for the interface and the energy management into systems. The laboratory focuses on innovative electronic systems for managing Li-ion (Lithium-ion) battery packs for electric vehicles. From electric transportation to smart power grids, leisure and industry, the battery usage is growing very fast and seems to have a bright future. Although having benefited from major advances in recent years, batteries still suffer from certain limitations, notably in terms of energy density, lifetime and sometimes safety. In this context, the patented reconfigurable switching cell battery architecture proposed and developed in the L2EP laboratory represents a major innovation in this field and allows us to go beyond some of these limitations. Today, batteries composed of a fixed series of cells through which the same current is flowing. These systems are thus limited by the weakest of the cells connected in series. The major advantage of the reconfigurable battery architecture developed is that each cell in a battery pack can be controlled individually and dynamically. This innovative architecture is then able to offer new functionalities (reconstruction of a sinusoidal signal, isolation, dynamic loading of the cells according to their health conditions, etc.). This architecture allows a complete real-time reconfiguration of the battery topology. In addition, thanks to this innovation, it is possible to avoid the components usually essential for a battery system implemented in an AC application: the charger and the inverter. The saving in cost, volume and weight of the system is then very significant. The first objective of this thesis is the development of advanced estimation algorithms for SoX indicators (SoC: State of Charge; SoH: State of Health, SoE: State of Energy, SoP: State of Power) of batteries based on an optimal use of the new potentialities offered by the reconfigurable switched cell architecture. Indeed, this architecture brings new functionalities opening the field to the implementation of new algorithms within the "Battery Management System". Currently, there is an abundant scientific literature on SoX battery estimators. These studies show varying results in terms of accuracy and robustness. The reliable and accurate evaluation of variables such as impedance and cell capacity remains a challenge to date and often requires a heavy and expensive characterization campaign in lab. Thanks to the new reconfigurable battery architecture, these characterization could be done on-line, and significant improvement can be achieved regarding the embedded SoX indicators estimators. In particular, the estimation of cell capacity could be greatly improved by adjusting the estimator, made possible by a controlled load-discharge of some cells individually; the evaluation of the online impedance can be optimized by an active identification process applied to a cell. It is even possible to carry out an online readjustment of the "Open Circuit Voltage (OCV)" characteristic according to the "state of charge (SoC)". SoX estimation algorithms such as Bayesian observers and Machine Learning will take full advantage of these features and could deliver unrivaled performance. The second objective of the thesis is to propose an algorithm based on the estimates described above that optimally exploits the energy of each and all the cells in the battery to increase the autonomy of the system while maximizing its lifetime.

Adaptative frequency tuning electronic systems for broadband vibration energy harvesting

Département Systèmes

Laboratoire Autonomie et Intégration des Capteurs

01-10-2019

SL-DRT-19-0436

pierre.gasnier@cea.fr

Energy harvesting is a theme whose goal is to supply power to communicating Wireless Sensor Nodes (WSN) by replacing their electrical power source (battery, cables) or by increasing their energy autonomy. Vibration energy harvesting, in particular, makes it possible to exploit the mechanical energy of an environment and convert it into electricity in order to supply the WSN. The proposed PhD thesis will focus on the use of the piezoelectric transduction to convert vibration energy into electricity. One of the major drawbacks of these harvesters is their frequency selectivity: the use of mechanical resonators amplifies ambient vibrations, but the harvested power drastically drops when the harvester and the environment are no longer tuned in frequency, which degrades the operability of the system and therefore its reliability. For the adoption of this type of system by industry, one of the main major barriers is therefore this frequency selectivity. This can be solved by means of so-called "broadband" harvesters and/or with the ability to be dynamically tuned by an electronic system. Indeed, coupled to an intelligent electronics, a "strongly coupled" harvester can see its mechanical behaviour modified (change in its stiffness for example) which makes it possible to 1) follow the evolution of the input frequency (a motor whose rotation frequency slows down, ...) and/or 2) compensate for its own intrinsic properties (its resonance frequency that decreases with temperature, ageing...). The core of the proposed work therefore focuses on electronics and power management circuits that adapt the mechanical behaviour of such harvesters according to the input vibration. The CEA and the Savoie Mont-Blanc University (SYMME Laboratory) have already proposed high-performance techniques to carry out this frequency tuning. However, the automatic adjustment part of these circuits has not been investigated. The objective of the thesis is to propose, dimension, simulate, realize and test innovative electronic architectures allowing automatic tuning and maximum power point tracking of a piezoelectric harvester. After a state of the art study on frequency adjustment means and techniques, a system study and electromechanical simulations will have to be carried out, which will make it possible to select the relevant implementations (Full analog, or mixed digital-analog). Particular care will be taken to ensure that the proposed circuit is low power and takes up little space, since the ultimate goal is to build a circuit that is autonomous in terms of energy and consumes a negligible proportion of the harvested electrical energy. At the end of the Phd work, the selected architecture(s) will then be proposed to the integrated circuit design department for miniaturization. A complete demonstrator (harvester + tuning technique + adjustment circuit) is targeted at the end of this thesis.

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