This article focuses on state-of-charge (SoC) estimation for a lithium-ion battery modeled using a recently developed nonlinear double-capacitor representation that has been shown to be highly accurate. The measurement equation of the model has two nonlinear functions, one of them being significant hysteresis in voltage as a function of the SoC. The hysteresis term is modeled using a physically intuitive modified Preisach representation consisting of a series of hysterons which get switched on or off to produce the hysteresis phenomenon. The proposed model for the hysteresis term is not differentiable but is Lipschitz bounded. A custom nonlinear observer is designed for this class of nonlinear systems using Lyapunov analysis to guarantee asymptotic stability. The observer design procedure consists of satisfying a linear matrix inequality on a set of vertices of a convex function. The developed observer is evaluated using experimental battery data. Experimental data is also used to determine the weights of the hysterons in the data-based modified Preisach model. The nonlinear observer performs well, yielding accurate SoC estimates in the presence of hysteresis. The nonlinear observer is also compared with the extended Kalman filter and the unscented Kalman filter, and is seen to provide superior performance for the same application due to its guaranteed global convergence properties. A broad technical contribution of this article is the development of an observer design method to handle the presence of hysteresis in nonlinear systems.

Predicting changes in cell resistance and thickness as the battery ages can be as important as capacity fade in informing vehicle resale value, pack replacement schedules, and viability for repurposing before eventual recycling. Three well-known degradation mechanisms, namely SEI growth, lithium plating, and electrode particle fracture due to reversible expansion and contraction, are revisited and tuned using capacity fade data from cells experiencing plating and having significant loss of anode active material as the dominant degradation mechanism. Therefore, lifetime prediction achieved in this work cannot rely on SEI degradation that can be tuned mostly with calendar aging data. More importantly, our model uses capacity-predictive irreversible SEI growth, net growth in plating, and accumulation of particle fracture as the cells cycle to predict the irreversible increases in cell thickness through a single set of tuned parameters. Last but not least, the capacity-predictive degradation can also predict the increased resistance through another single set of parameters. These two sets of scaling parameters achieve for the first time a consistently tuned model of degradation mechanisms for lifetime prediction of changes in battery capacity, resistance, and thickness as the battery ages. The model prediction has been validated in conditions not used for parameter tuning.

This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within a charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model and validated against experimental data. To demonstrate how current and state of charge (SOC) imbalance leads to cell degradation, we developed a successive update scheme in which the intercycle imbalance dynamics update the intracycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis.

Detecting internal short circuits (ISCs) in a single cell connected in parallel with others is challenging because unmeasured internal currents can obscure measurable indicators such as charge loss and voltage drop from the faulty cell. In this work, we propose a new method for detecting ISCs based on the interacting multiple model (IMM) estimation technique, which can provide a probability for the occurrence of an ISC and simultaneously estimate the short-circuit resistance, indicating the severity of the ISC. The IMM relies on dynamic electrothermal models of parallel cells (nP), both for the healthy mode and short-circuit mode. The IMM technique is combined with unscented Kalman filters (UKFs) to detect internal short circuits and estimate the short-circuit resistance across various synthetic data sets that are corrupted by Gaussian noise for different values of ISC resistance. Fifty short-circuit scenarios were simulated in which one cell in a 46 P cell group underwent an ISC during a drive cycle. The short-circuit resistances ranged from 0.5 to 100 Ω, tested at ten different states of charge (SOCs). Our simulation outputs included busbar voltage, input current, and cell temperatures, which were then corrupted by Gaussian noise. Our IMM successfully detected and estimated the ISC in all fifty cases, with temperature rise remaining below 6 °C before detection, well before the onset of thermal runaway conditions.