Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance
Abstract
The diagnosis and prognosis of PV-connected batteries are complicated because cells might never experience controlled conditions during operation as both the charge and discharge duty cycles are sporadic. This work presents the application of a new methodology that enables diagnosis without the need for any maintenance cycle. It uses a 1-dimensional convolutional neural network trained on the output from a clear sky irradiance model and validated on the observed irradiances for 720 days of synthetic battery data generated from pyranometer irradiance observations. The analysis was performed from three angles: the impact of sky conditions, degradation composition, and degradation extent. Our results indicate that for days with over 50% clear sky or with an average irradiance over 650 W/m2, diagnosis with an average RMSE of 1.75% is obtainable independent of the composition of the degradation and of its extent.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 29, 2023
- Source ID
- 10.3390/batteries9080395
Entities
People
- Dax Matthews
- Fahim Yasir
- Matthieu Dubarry
- Nahuel Costa
Organizations
- Office of Naval Research
- University of Hawaiʻi at Mānoa
- University of Oviedo