Lithium-ion batteries (LIBs) inevitably experience performance decay over their lifetime, including both capacity and power loss. This degradation reduces operational reliability, may pose safety risks under demanding operating conditions, and can increase warranty liability for manufacturers and system operators. Consequently, the development of reliable tools for predictive maintenance, battery health monitoring, and lifetime prognosis has become a strategic priority.

The primary drivers of LIB performance degradation are mechanical and electrochemical processes. During battery operation, lithium ions intercalate and deintercalate within the active material particles that form the electrode microstructure. Regions with higher lithium concentration experience greater deformation than those with lower concentration, leading to differential strain diffusion-induced stresses and mechanical degradation. In addition, electrochemical side reactions, such as the growth of the solid electrolyte interphase (SEI) and lithium plating, continuously consume lithium ions. These mechanisms result in capacity fade, increased resistance, and irreversible battery swelling, a phenomenon that is often overlooked in the existing literature.

This contribution presents POLIDEMO [1], an innovative battery degradation software designed for reliability and ease of use. POLIDEMO employs a multi-physics framework that combines physics-based modeling with advanced optimization techniques and experimental data to deliver high-fidelity battery lifetime predictions across diverse operating scenarios. The approach accurately calibrates cell-specific parameters while making physics-based models more accessible to researchers and engineers.

POLIDEMO overcomes key limitations of traditional physics-based approaches by significantly reducing computational cost, improving the accuracy of parameter estimation, and providing a more comprehensive representation of degradation mechanisms. The key strengths of POLIDEMO are the prediction of the knee point in the capacity fade curve and the irreversible swelling, which is highly relevant to the mechanical design of the battery pack.

POLIDEMO is validated using aging tests performed on different battery chemistries and operating conditions, with an efficient parametrization over a limited set of data. Thus, POLIDEMO represents a valuable tool for enhancing lithium-ion battery lifetime modeling in both academic and industrial contexts.

[1] F. Pistorio, D. Clerici, and A. Somà, “POLIDEMO: An electrochemical-mechanical framework for modeling lithium-ion batteries degradation,” Applied Energy, vol. 404, p. 126744, 2026.