The student Iker Lopetegui Tapia obtained an EXCELLENT CUM LAUDE grade with mention International Doctorate
The student Iker Lopetegui Tapia obtained an EXCELLENT CUM LAUDE grade with mention International Doctorate
The student Iker Lopetegui Tapia obtained an EXCELLENT CUM LAUDE grade with mention International Doctorate
- Thesis title: Mitigating Lithium-Ion Battery Aging: Physics-Based State Estimation, Aging Prediction, and Degradation-aware Control Strategies
Court:
- Presidency: Gregory Plett (University of Colorado Colorado Springs)
- Vocal: Scott Trimboli (University of Colorado Colorado Springs)
- Vocal: David Anseán González (Universidad de Oviedo)
- Vocal: Elixabete Ayerbe Olano (CIDETEC)
- Secretary: Erik Garayalde Perez (Mondragon Unibertsitatea)
Abstract:
Lithium-ion batteries are the most widely adopted energy storage system (ESS) nowadays. Due to their high gravimetric and volumetric energy density, and their low self-discharge rate, they are the best option for many applications, such as consumer electronics or electric vehicles (EVs). Yet, this technology still needs improvements to meet the demands of the energy transition. The relatively fast degradation that these batteries suffer is one of the main concerns with this ESS, and alongside the safety issues associated to it, the need for optimized and safe management is urgent.
This thesis aims to improve the management of lithium-ion batteries using advanced control algorithms based on physical knowledge to mitigate battery aging. For that, simplified and reduced-order physics-based models (PBMs) are employed, such as the P2D and SPMe models, which are believed to give relevant insight about the physicochemical phenomena happening inside batteries. Lithium-ion battery aging is analyzed, and PBMs are used to develop a reliable degradation model that could be used to develop advanced degradation-aware control algorithms. For that, a new parameterization approach is proposed and tested experimentally, which could be used to significantly reduce the number of experiments to obtain an accurate aging model. To estimate the physical states of the battery through battery lifetime, new state-of-charge (SOC) and state-of-health (SOH) estimation algorithms are developed, improving current battery diagnosis algorithms by providing accurate estimates of electrode-level degradation and internal variables. Lastly, combining the knowledge provided by the state and parameter estimator, and the predictions of the degradation model, a new fast charging control strategy is proposed based on a nonlinear model predictive control (NMPC) algorithm, resulting in faster charging and reduced aging.
The developed aging modeling approach, state and parameter estimation, and control strategy, demonstrate that PBMs can improve current empirical approaches, and help in the advance of new optimized battery algorithms; for battery aging prediction purposes, for battery health diagnosis, and for improved degradation-aware control strategies.