Francesca Pistorio

This work provides an overview of the mechanics of lithium-ion batteries, both from the aging and diagnostics perspective. Battery diagnostics based on mechanical measurements exploit the strong correlation between electrode lithiation and its deformation, resulting in macroscopic cell deformation. Macroscopic deformation is then a proxy for lithium concentration, enabling estimation of state of charge (SOC) and degradation indicators such as loss of active material and lithium inventory. The results demonstrate that SOC estimation algorithms based on deformation measurements are more robust than voltage-based methods, which are sensitive to temperature and aging, requiring constant updates of the algorithm parameters. Moreover, the health of the battery can be assessed through the differential expansion method even under high-current operation, providing results consistent with the traditional differential voltage method but applicable to real-world industrial applications. Mechanics plays a crucial role also in battery degradation. This work presents the application of POLIDEMO, an advanced battery aging model that explicitly accounts for mechanical degradation phenomena, providing a physics-based framework describing the coupled electrochemical–mechanical aging processes in lithium-ion batteries. It enables the prediction of key degradation indicators, including capacity fade—capturing the characteristic knee-point behavior—and the irreversible battery thickness increase associated with long-term aging. The model is validated with multiple aging datasets, demonstrating that parameters calibrated under a single operating condition can accurately predict degradation across diverse aging scenarios.

Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be identified via model-based fitting of experimental data. Unlike other parameter-estimation procedures, this study introduces a novel approach that integrates mechanical measurements with electrical data, with a specific application for lithium iron phosphate (LFP) cells. An error analysis—based on the Root Mean Square Error (RMSE) and confidence ellipses—confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. Two scenarios are analyzed: in the first, a teardown of the cell provides direct measurements of electrode thicknesses and the number of layers; in the second, these values are treated as additional unknown parameters. In the teardown case, the electrochemical–mechanical approach achieves significantly lower RMSEs and smaller confidence ellipses, proving its superior accuracy and consistency. In the second scenario, while the RMSE values of electrochemical-mechanical model are similar to those of the purely electrochemical one, the smaller ellipses still indicate better consistency and convergence in the parameter estimates. Furthermore, a sensitivity analysis to initial guesses shows that the electrochemical-mechanical approach is more stable, consistently converging to coherent parameter values and confirming its greater reliability.

Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be identified through model-based fitting of experimental data. Unlike other parameter estimation procedures, this paper introduces a novel approach that integrates mechanical measurements with the electrical data, with a specific application to lithium iron phosphate (LFP) cells. An error analysis – based on the Root Mean Square Error (RMSE) and on the confidence ellipses – confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. Two scenarios are analyzed: in the first, a teardown of the cell provides direct measurements of electrode thicknesses and number of layers; in the second, these values are treated as additional unknown parameters. In the teardown case, the electro-chemo-mechanical model achieves significantly lower RMSEs and smaller confidence ellipses, confirming its superior reliability. In the second, while the RMSE values are similar to those of the purely electrochemical model, the smaller ellipses still indicate better consistency and convergence in the parameter estimates.

In the automotive and working vehicle industry, lithium-ion batteries are a strategic component affecting the design, cost, and performance of vehicles. The electrochemical processes which allow the battery to deliver or store electrical energy involve the interaction of lithium ions with the electrode microstructure, causing the mechanical deformation of the electrode. The deformation of the electrode microstructure has two effects: mechanical degradation and the resulting overall performance decay of the battery, and macroscopic battery deformation. In this work, macroscopic battery deformation originating at the atomic scale is investigated with a multi-physics homogenized model in two steps: first, the composite electrode is modeled with a representative volume element; secondly, the battery is modeled by homogenizing the contribution of the hundreds of composite electrode layers. Then, the impact of the deformation of the single battery on the whole battery module is numerically investigated. The deformation of the single battery computed with the model is validated with experimental measurements quantifying the macroscopic battery deformation during operation. Then, different design solutions for the battery module are investigated to optimize its energetic and volumetric efficiency while maintaining safe levels of battery module deformation.

The characterization of fatigue fracture mechanics in gold-MEMS notched specimens is presented in this work. A test microstructure with a central notched specimen is specifically designed and built to perform on-chip fatigue test. The central specimen undergoes cyclic loading due to the application of alternating voltage. The variation in the microstructure deflection is measured using an optical profilometer and is attributed to the crack growth in the gold material, causing the variation in the specimen stiffness. The occurrence of pull-in condition is used as a fracture detector, then the fracture of the specimen can be recognized without performing scanning electron microscope inspections during the fatigue test. Crack propagation in the test specimen is simulated through a coupled-field electromechanical fracture finite element model and the resulting crack path is compared to the experimental measurments performed with scanning electron microscope analyses. Finally, Paris’ law is applied and the number of cycles to failure is computed by exploiting the results of the fracture model and experimental measurements. Both experimental and numerical results demonstrate that the notch acts as a stress and strain raiser, fostering crack nucleation, and that the linear elastic fracture mechanics theory is still valid to describe crack propagation in micro-size samples.