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Electrochemical Modeling, Supervision and Control of Lithium-Ion Batteries

par Couto Mendonca, Luis Daniel
Président du jury Bogaerts, Philippe
Promoteur Kinnaert, Michel
Publication Non publié, 2018-12-20

Thèse de doctorat

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Résumé :

This thesis develops an advanced battery monitoring and control system based on the electrochemical principles that govern lithium-ion battery dynamics. This work is motivated by the need of having safer and better energy storage systems for all kind of applications, from small scale portable electronics to large scale renewable energy storage. In this context, lithium-ion batteries have become the enabling technology for energy autonomy in appliances (e.g. mobile phone, electric vehicle) and energy self-consumption in households. However, batteries are oversized and pricey, might be unsafe, are slow to charge and may not equalize the lifetime of the application they are intended to power. This work tackles these different issues.This document first introduces the general context of the battery management problem, as well as the particular issues that arise when modeling, supervising and controlling the battery short-term and long-term operation. Different solutions coming from the literature are reviewed, and several standard tools borrowed from control theory are exposed. Then, starting by well-known contributions in electrochemical modeling, we proceed to develop reduced-order models for the battery operation including degradation mechanisms, that are highly descriptive of the real phenomena taking place. This modeling framework is the cornerstone of all the monitoring and control development that follows.Next, we derive a battery diagnosis system with a twofold objective. First, indicators for internal faults affecting the battery state-of-health are obtained. Secondly, detection and isolation of sensor faults is achieved. Both tasks rely on state observers designed from electrochemical models to perform state estimation and residual generation. Whereas the former solution resorts to system identification techniques for health monitoring, the latter solution exploits fault diagnosis for instrumentation assessment.We then develop a feedback battery charge strategy able to push in performance while accounting for constraints associated to battery degradation. The fast and safe charging capabilities of the proposed approach are ultimately validated through long-term cycling experiments. This approach outperforms widely used commercial charging strategies in terms of both charging speed and degradation.The main contribution of this thesis is the exploitation of first principles models to develop battery management strategies towards improving safety, charging time and lifetime of battery systems without jeopardizing performance. The obtained results show that system and control theory offer opportunities to improve battery operation, aside from the material sciences contributions to this field.