Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics.

ABSTRACT: Photovoltaics supply a growing share of power to the electric grid worldwide. To mitigate resource intermittency issues, these systems are increasingly being paired with electrochemical energy storage devices, e.g., Li-ion batteries, for which ensuring long and safe operation is critical. However, in this operation framework, secondary Li-ion batteries undergo sporadic usage, which prevents the application of standard diagnostic methods. Here, we propose a diagnostic methodology that uses machine learning algorithms trained directly on data obtained from photovoltaic charging of Li-ion batteries. The training is carried out on synthetic voltage data at various degradation conditions calculated from clear sky model irradiance data. The method is validated using synthetic voltage responses c

SUBMITTER: Dubarry M 

PROVIDER: S-EPMC10229535 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature