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Differential voltage analysis (DVA) is a conventional approach for estimating capacity degradation in batteries. During charging, a graphite electrode goes through several phase transitions observed as plateaus in the voltage response. The transitions between these plateaus emerge as observable peaks in the differential voltage. The DVA method utilizes these peaks for estimating cell degradation. Unfortunately, at higher C-rates (above C/2) the peaks flatten and become unobservable. In this work, we show that, unlike the differential voltage, the peaks in the 2nd derivative of the expansion with respect to capacity remain observable up to 1C and thus make possible diagnostic algorithms at these charging rates. To understand why that is the case, we have developed an electrochemical and expansion model suitable for model-based estimation. In particular, we demonstrate that the single particle modeling methodology is not able to capture the peak smoothing effect, therefore a multi-particle approach for the graphite electrode is needed. Additionally, model parameters are identified using experimental data from a 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.

Estimation of electrode state of health (eSOH) is essential to understanding battery degradation status in detail. This is accomplished by considering electrode capacity and a utilization range as eSOH parameters. In this paper, we propose a novel combination of two eSOH estimation approaches (i.e. voltage fitting and differential voltage analysis). By utilizing peak information in the differential voltage curve, the proposed method can separate individual electrode's contributions from the full-cell voltage. This separation allows the proposed method to identify changes in the positive electrode half-cell potential curve and to calibrate the aged half-cell potential function by refitting the coefficient of the basis functions. A comparison between the conventional voltage fitting method and the proposed method for an aged NMC/graphite cell shows that the proposed method can significantly reduce the voltage fit error while matching the location of distinct peaks in the differential voltage curve that the conventional method fails to do.

Lithium-ion batteries cell thickness changes as they degrade. These changes in thickness consist of a reversible intercalation-induced expansion and an irreversible expansion. In this work, we study the cell expansion evolution under variety of conditions such as temperature, charging rate, depth of discharge, and pressure. A specialized fixture was used to keep the cells at a constant pressure during cycling, while measuring the thickness change both within a cycle and the cumulative growth over many cycles. The changes in positive and negative electrode capacity and stoichiometric range can be diagnosed from the evolution of the reversible expansion. The changes in the reversible expansion if combined with the voltage, lead to a higher-confidence estimation of cell health parameters important for lifetime prediction and adaptive battery management such as asymmetric charge/discharge power limits. This study raises the importance of monitoring the expansion for enabling advanced and more-informed health diagnostics of lithium-ion batteries.

Advanced battery management system, which leverages an in-depth understanding of the battery state of health, can improve efficiently and safely. To this end, we introduce the electrode-level battery state of health (eSOH) estimation problem with open-circuit voltage (OCV) data. In real-world applications, collecting the full-range OCV data is difficult since the battery is not deeply discharged. When data is limited, the estimation accuracy deteriorates. In this article, we quantify the uncertainty of the electrode parameter estimation with partial data based on the Cramer-Rao bound and confidence interval. By introducing a voltage constraint in the estimation problem, the positive electrode parameters can be estimated with sufficient accuracy over a wide range of state of charge. However, the estimation accuracy of the negative electrode parameters is more sensitive to the depth of discharge. The proposed framework can be used as a guideline for selecting proper data windows and understanding the impact on parameter estimation.

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