Investigating and Classifying the Level of Thermal Runaway-Induced Failure Energy Storage Systems in Photovoltaics - Shamsmohammadi, Nikta (Journal of Computational and Cognitive Engineering, 2023)
In a photovoltaic system, the electricity generated by a solar panel can depend on the amount of sunlight available at any given time. Due to the nonlinear behavior of this energy, the storage component of the system is critical. As a result, many solar panel systems are equipped with batteries. However, changes in environmental and other conditions can cause damage to these batteries. This study explores novel technologies for detecting thermal runaway failures in lithium-ion batteries. Specifically, the study employs image processing techniques to detect structural failures and applies deep learning techniques for automatic classification. Thermal damage to a battery can result in irreparable harm, making design and construction considerations crucial. Through image analysis, any internal changes in the battery can be transformed into a measurable variable, providing a reliable indication of potential failure. The study compares the current series with previous ones to highlight the structural differences. Results confirm that the proposed approach has significant potential for detecting and estimating internal variations during production. Overall, the proposed method can serve as a valuable tool for drafting and implementing a comprehensive plan to address early problems in the battery. Received: 8 September 2023 | Revised: 14 November 2023 | Accepted: 21 November 2023 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in National Renewable Energy Laboratory (NREL) at https://www.nrel.gov/transportation/battery-failure.html