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Physical-based interpretation of lithium-ion battery automotive-related ageing

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Despite its fully commercial development status, lithium-ion battery (LIB) degradation understanding and diagnostics is still not fully consolidated, involving a number of interconnected mechanisms whose effect combines into a complexly mixed performance fade. State-of-health (SoH), while lacking a harmonized definition, is usually referred as a mere fade of capacity, lacking physical background and any possibility to distinguish between different degradation mechanisms. In such scenario, residual lifetime reliable estimation becomes an extremely challenging task.
In this work, degradation related to automotive-specific application have been investigated by applying several appositely developed dynamic load profiles (driving cycles, such as WLTP) to commercial battery samples, replicating real-world and accelerated aging, under laboratory-controlled environment, for several hundreds of equivalent full cycles (EFCs). The contribution of calendar aging has been appositely characterized to enable its discretization from the overall fading. A multi-measurement diagnostic protocol combining discharge test, electrochemical impedance spectroscopy (EIS) and voltage relaxation, previously developed to maximize the sensitivity of the pseudo-2D Doyle-Fuller-Newman (P2D-DFN) model parameters, was periodically applied to thoroughly characterize cells aging modes. Also, several online fast diagnostics have been developed and performed to track battery performance fading during actual real-world operation. Hence, a previously developed P2D-DFN model, implemented with heat transfer, has been applied to interpret the periodic aging diagnostics by means of an optimized stepwise calibration algorithm, translating battery performance fade into a variation of their key physical parameters.
Such physical-based approach enables the interpretation of battery aging, distinguishing the underlying degradation mechanisms and identifying and quantifying the impact of operation-specific aging stressors, such as average C-rate, State of Charge (SOC), depth of discharge (DOD) and temperature. The methodology also permits to validate of the physical consistency of accelerated stress cycles with respect to real-world driving cycles and permitting to identify fast experimental diagnostics which may enable a physically-sound online tracking of battery state of health during its operation. As an example, high-SOC and high-speed WLTP cycling, featuring quite different operation lead to 85% residual capacity in ∼300 and ∼400 EFC respectively. While thermodynamic print recorded by means of a slow discharge permit to highlight different values of loss of lithium inventory (LLI) and of active material (LAM), which is highlighted by the multi-measurement protocol particularly in the EIS. The effects on the physical parameters, which will require an apposite methodology implementation with respect to the BoL for fading simulation, is already preliminarily able to a enable physically-sound diagnostics.

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