Towards decarbonized electric grid and transportation sectors : a modelling, battery-centric study
Keywords: Sustainability.
Energy Resources Engineering.

In the face of ever-increasing effects of anthropogenic climate change, the energy transition towards a fully-decarbonized world is accelerating. Key to this accelerated decarbonized energy transition is the inclusion of energy storage. Across the electric grid and transportation sectors, the predominant technology is the lithium-ion battery (LIB), outperforming other technologies in terms of cost, performance, and technological maturity. LIBs can be used as grid-scale energy storage systems, balancing the intermittent generation of renewable energy resources with the power demand on the electric grid. LIBs are also the most used storage technology for electric vehicles (EVs), and the LIBs from retired EVs can even be repurposed in grid storage ESSs. However, the degradation (capacity fade and resistance increase/power fade) of LIBs must be considered, especially as this degradation is highly application-dependent. As LIBs will become a crucial part of the energy transition, with each system in use for multiple years at a time, it is imperative to understand how they will degrade over usage, in order to ensure that they are deployed prudently. Given their long lifetimes, LIBs are often simulated using models in order to assess their long-term performance. Existing models often use empirical models for aging, and therefore do not encompass the complex processes that underpin LIB operation and degradation. It is critical for both long-term planning and deployment of LIB systems, as well as safe and reliable real-time control and operation, that models adequately incorporate the physics and electrochemistry behind LIB degradation. Regardless of the model chosen, the highly application-dependent degradation trajectories of LIBs mean that any model must consider the LIB usage under the specific application, whether for grid storage or in electric vehicles. Existing experimental aging data comprise cells cycled with standard, overly-simplistic protocols, such as constant-current/constant-voltage profiles, which are not indicative of actual application usage, and therefore using these datasets for model calibration would provide inaccurate estimates of application performance under aging, necessitating the generation of application-specific data for proper LIB model development. To this end, this dissertation aims to "close the loop" between LIB degradation as modelled and studied in the literature and LIB deployment and usage in the field, by developing application-specific datasets and aging models. A methodology to generate synthetic duty cycles from LIB operational data is developed. These synthetic duty cycles are applied in laboratory cycling experiments, to produce application-specific aging datasets for grid storage LIB ESSs and the nascent application of connected/autonomous EVs (C/AEVs). A physics-based model is employed as the basis of an SOC-dependent aging model, which models fundamental processes underpinning LIB degradation and is directly calibrated from data in both the frequency- and time-domain. Altogether, the work presented in this dissertation will help ensure that long-term deployment of LIBs are informed by degradation, by accurately predicting the degradation trajectory under application-specific usage.