Linking Calendar and Cycle Ageing in Lithium-Ion Batteries through Consistent Parameterisation of an Electrochemical-Thermal-Degradation Model
Ganesh Madabattula

TL;DR
This paper develops a comprehensive framework to predict capacity fade and degradation modes in lithium-ion batteries under various usage conditions using a coupled electrochemical-thermal model.
Contribution
It introduces a consistent parameterisation method for degradation mechanisms and applies it to predict battery health across diverse conditions using PyBaMM.
Findings
Capacity fade varies from 0.8 to 14 years to reach 75% SoH.
The model captures different fade behaviors: sub-linear, linear, and accelerated.
Simulated degradation datasets are publicly available.
Abstract
Parameterisation of coupled degradation mechanisms in lithium-ion batteries is a major challenge. Interactions between the mechanisms depend on usage conditions: C-rate, rest state-of-charge (SoC), depth-of-discharge (DoD) and temperature. This work presents a framework to consistently parameterise key degradation modes--solid-electrolyte interphase (SEI) growth, lithium plating, and active material loss in both electrodes--using insights derived from degradation mode analysis data. The work predicts capacity fade trajectories of a NMC-based lithium-ion cell under both calendar and combined calendar-cyclic ageing, using a P2D electrochemical-thermal-degradation model. The work predicts state-of-health (SoH), remaining-useful-life (RUL) and internal degradation modes of the cell--under 81 combinations of temperature (10C, 25C, 40C), C-rate (0.1 C, 0.3 C and 1.0 C), rest SoC…
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