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

PhD : selection by topics

Engineering science >> Physical chemistry and electrochemistry
2 proposition(s).

Methodology development for lifetime prediction of batteries for transportation to improve the durability simulation

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

Laboratoire Analyse électrochimique et Post mortem

01-10-2019

SL-DRT-19-0546

sylvie.genies@cea.fr

The reduction of the human impact on the environment require reducing the CO2 emission. The electrisation of transportation participate to this objective and is now possible by the recent advance in the Li-ion battery technology. Initially developed for the nomad devices (labtop, smart phone and other electronic devices), research effort in this field was increase the energy density, cyclability and safety issue. Now this technology is mature to be used as power sources of car and other vehicles. Ageing and safety issue were two key factors for a large deployment of this technology. Up to now, research activities was concentrated on the batteries performances. Degradation mechanisms were study in particular conditions to understand the electrochemical reaction responsible to the loose of performances [1]. If the degradation mechanisms is well known in specific condition (low temperature, high current, etc?), very few study in the literature have been study the battery ageing in real conditions. The ageing laws in the field of battery were established from empirical data or in the objective of improve the performances in a development process. This type of research does not taking account to the real usage of the battery [2-5]. Therefore, the simulation of battery ageing is based on either empirical or semi-empirical models. Therefore, the simulation of battery ageing is based on either empirical or semi-empirical models. Multiphysical modelling approaches allow a better understanding of battery behaviour by more accurately modelling their behaviour. The objective of this thesis is to develop a methodology to study the durability of battery for lifetime prediction related to usage in the field of transportation. This work will make it possible to better identify the laws of ageing for the modelling of these complex systems. In addition, the development of representative test protocols will also make it possible to feed the modelling with a better understanding of the phenomena involved. This methodology will be based on the lifetime prediction developed in other mature field and can be divide in 3 phases [6]: 1- A risk analysis to identify the stress factor, there level. 2- The development of accelerate test to reproduce stress factor for evaluate the sensitivity of the system and analysis the degradation process 3- From these accelerate tests and the analysis of real operate conditions of battery, an ageing law and lifetime prediction can be established related to real case of application. This thesis proposes to apply this methodology to the field of batteries for mobility, as the field has a sufficient level of maturity. It will be based on both experimental studies of accelerated ageing and the identification of degradation mechanisms through structural analyses. A comparison with the results of the literature and the feedback of real-life experiences in vehicles will make it possible to identify and quantify stress factors related to real-life uses and to validate the mechanisms. The determination and the validation of the ageing law and lifetime prediction will also compare to the simulation results. By coupling of experiment and simulation, the goal of this thesis is also to improve and validate the battery's modelling and simulation in a deeper correlation to usage of battery.

field effect nanoionic synaptic transistors for neuromorphic applications

Département Composants Silicium (LETI)

Laboratoire Micro-Batteries Embarquées

01-09-2019

SL-DRT-19-0631

sami.oukassi@cea.fr

Neuromorphic computing represents an innovative technology that can perform intelligent and energy-efficient computation, whereas construction of neuromorphic systems requires biorealistic synaptic elements with rich dynamics that can be tuned based on a robust mechanism. As a result, there exists a tremendous upsurge of research interests on building neuromorphic systems, especially by exploiting the scalability and functionality of emerging devices (memristors, Reram). Recently, there is a growing interest on 3-terminal synaptic architectures (memtransistors), whose additional input terminal and modified device configuration have proven favorable for achieving complicated synaptic functions. Today, ion gated transistors appear as one of the most promising candidates, due to their low consumption, scalability and integration. They rely on the use of an ion conductor as a gate dielectric to drive or attract ions to/from the channel. Xxx. In this context, the objective of this PhD is to investigate novel solid-state nanoionic transistors as a synaptic device for neuromorphic applications. The main objective of the PhD is to investigate the potential synaptic behavior that can be achieved in field effect nanoionic transistors. To this aim various materials and device architectures will be characterized: channel with (i) ionic conductor or (ii) ionic conductor/host material bilayer. On these structures, synaptic behavior features will be quantified in terms of: linearity, symmetry, energy consumption etc?modelling could be used to analyze the physical effects taking place in the devices: ion drift and accumulation in the channel bulk or interfaces will be described and simulated. Based on the obtained results, a benchmark among the various tested technologies will be proposed. Then, the link between device stack (materials, thicknesses?) and synaptic capabilities will be clarified. The objective is to propose new device stacks (new elements, multilayers?) to optimize the device performances.

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