Statistical physics-based model of mechanical degradation in lithium ion batteries

Probing the Mechanically Stable Solid Electrolyte Interphase and the Implications in Design Strategies

The inevitable volume expansion of secondary battery anodes during cycling imposes forces on the solid electrolyte interphase (SEI). The battery performance is closely related to the capability of SEI to maintain intact under the cyclic loading conditions, which basically boils down to the mechanical properties of SEI. The volatile and complex nature of SEI as well as its nanoscale thickness and environmental sensitivity make the interpretation of its mechanical behavior many roadblocks. Widely varied approaches are adopted to investigate the mechanical properties of SEI, and diverse opinions are generated. The lack of consensus at both technical and theoretical levels has hindered the development of effective design strategies to maximize the mechanical stability of SEIs. Here, the essential and desirable mechanical properties of SEI, the available mechanical characterization methods, and important issues meriting attention for higher test accuracy are outlined. Previous attempts to optimize battery performance by tuning SEI mechanical properties are also scrutinized, inconsistencies in these efforts are elucidated, and the underlying causes are explored. Finally, a set of research protocols is proposed to accelerate the achievement of superior battery cycling performance by improving the mechanical stability of SEI.

Decentralized versus Clustered Microgrids: An Energy Systems Study for Reliable Off-Grid Electrification of Small Islands

Philippine off-grid islands are mostly electrified by diesel generators, resulting in costly electricity that is interrupted by fuel supply disruptions. The archipelagic nature of the country also impedes off-grid electrification due to the high capital cost of grid extension. Transitioning from diesel-only systems to hybrid renewable energy systems and interconnecting the island microgrids can solve these problems while promoting cleaner energy production. In this work, a comparative study on decentralized and clustered hybrid renewable energy system microgrids in the Polillo group of islands in the Philippines, using HOMER Pro, was performed. Microgrids comprising solar photovoltaics, lithium-ion battery energy storage, and diesel generators were designed on each island. Clustered systems encompassing multiple islands in the island group were simulated by also considering the least-cost interconnection paths. The techno-economics of each decentralized or clustered system and the four-island system were evaluated based on the levelized cost of electricity (LCOE). Reliability was assessed using the change in LCOE upon the failure of a component and during weather disturbances. Transitioning from diesel-only systems to hybrid systems reduces generation costs by an average of 42.01% and increases the renewable energy share to 80%. Interconnecting the hybrid systems results in an average increase of 2.34% in generation costs due to the cost of submarine cables but improves system reliability and reduces the optimum solar photovoltaic and lithium-ion storage installations by 6.66% and 8.71%, respectively. This research serves as a framework for the interconnection pre-feasibility analysis of other small off-grid islands.

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively.