Advanced Diagnostics for Lithium-ion Batteries: Decoding the Information in Electrode Swelling

Lithium-ion batteries exhibit mechanical expansion and contraction during cycling, consisting of a reversible intercalation-induced expansion and an irreversible expansion as the battery ages. Prior experimental studies have shown that mechanical expansion contains valuable information that correlates strongly with cell aging. However, a number of fundamental questions remain on the usability of the expansion measurement in practice. For example, it is necessary to determine whether the expansion measurements provide information that can help the estimation of the electrode state of health (eSOH), given limits on data availability and sensor noise in the field. Furthermore, the viability of using expansion for cell diagnostics also needs more investigation considering the broad range of aging conditions in real-world applications. This dissertation focuses on the experimental and modeling study of the expansion measurements during aging in order to assess its ability in helping battery diagnostics. To this end, mechanistic voltage and expansion models based on the underlying physics of phase transitions are developed. For the first time, the identifiability of eSOH parameters is explored by incorporating the expansion/force measurement. It is shown that the expansion measurements enhance the estimation of eSOH parameters, especially with a limited data window, since it has a better signal-to-noise ratio compared to the voltage. Moreover, the increased identifiability is closely related to the phase transitions in the electrodes. A long-term experimental aging study of the expansion of the graphite/NMC pouch cells is conducted under a variety of conditions such as temperature, charging rate, and depth of discharge. The goals here are to validate the results of the identifiability analysis and record the reversible and irreversible expansion correlated with capacity loss for informing the electrochemical models. Firstly, the advantages of the expansion concerning the eSOH identifiability are confirmed. Secondly, the results of the expansion evolution reveal that the features in the reversible expansion are an excellent indicator of health and, specifically, capacity retention. The expansion feature is robust to charge conditions. Namely, it is mostly insensitive to the hysteresis effects of the various initial state of charge, and it is detectable at higher C-rates up to 1C. Additionally, the expansion feature occurs near the half-charged point and therefore diagnostics can be performed more often during naturalistic use cases. Thus, the expansion measurement facilitates more frequent capacity checks in the field. Furthermore, an electrochemical and expansion model suitable for model-based estimation is developed. In particular, a multi-particle modeling approach for the graphite electrode is considered. It is demonstrated that the new model is able to capture the peak smoothing effect observed in the differential voltage at higher C-rates above C/2. Model parameters are identified using experimental data from the graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates. Finally, a fast-charging method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CVησT (VEST), is developed. The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. The capabilities of the new CC-CVησT charging are demonstrated using the physics-based model developed in this dissertation.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169953/1/pmohtat_1.pd

A Non-intrusive Approach for Physics-constrained Learning with Application to Fuel Cell Modeling

A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physical models. In contrast to parameter calibration, this work seeks corrections to the structure of the model by a) inferring augmentation fields that are consistent with the underlying model, and b) transforming these fields into corrective model forms. The proposed approach couples the inference and learning steps in a weak sense via an alternating optimization approach. This coupling ensures that the augmentation fields remain learnable and maintain consistent functional relationships with local modeled quantities across the training dataset. An iterative solution procedure is presented in this paper, removing the need to embed the augmentation function during the inference process. This framework is used to infer an augmentation introduced within a Polymer electrolyte membrane fuel cell (PEMFC) model using a small amount of training data (from only 14 training cases.) These training cases belong to a dataset consisting of high-fidelity simulation data obtained from a high-fidelity model of a first generation Toyota Mirai. All cases in this dataset are characterized by different inflow and outflow conditions on the same geometry. When tested on 1224 different configurations, the inferred augmentation significantly improves the predictive accuracy for a wide range of physical conditions. Predictions and available data for the current density distribution are also compared to demonstrate the predictive capability of the model for quantities of interest which were not involved in the inference process. The results demonstrate that the weakly-coupled IIML framework offers sophisticated and robust model augmentation capabilities without requiring extensive changes to the numerical solver

Optimizing Partial Power Processing for Second-Use Battery Energy Storage Systems

Repurposing automotive batteries to second-use battery energy storage systems (2-BESS) may have environmental and economic benefits. The challenge with second-use batteries is the uncertainty and diversity of the expected packs in terms of their chemistry, capacity and remaining useful life. This paper introduces a new strategy to optimize 2-BESS performance despite the diversity or heterogeneity of individual batteries while reducing the cost of power conversion. In this paper, the statistical distribution of the power heterogeneity in the supply of batteries is considered when optimizing the choice of power converters and designing the power flow within the battery energy storage system (BESS) to maximize battery utilization. By leveraging a new lite-sparse hierarchical partial power processing (LS-HiPPP) approach, we show a hierarchy in partial power processing (PPP) partitions power converters to a) significantly reduce converter ratings, b) process less power to achieve high system efficiency with lower cost (lower efficiency) converters, and c) take advantage of economies of scale by requiring only a minimal number of sets of identical converters. The results demonstrate that LS-HiPPP architectures offer the best tradeoff between battery utilization and converter cost and had higher system efficiency than conventional partial power processing (C-PPP) in all cases