Aging Characterization of Li-ion Batteries

Abstract

The depleting nature of fossil energy resources accompanied by increasing demand for energy and their influence on increasing the climate change rates and greenhouse gas emissions motivated the governments and decision-makers to adopt energy sources that are less harmful to the environment and community. Hence, exploiting the potential power of sunlight, water, wind, and underground energy was the cornerstone player in achieving cleaner and more sustainable energy systems. Unfortunately, it is almost impossible to rely on these resources to match the energy demand because of their erratic behavior. Thus, efficient storage systems are needed to enhance reliability and increase the dependency on these energy production systems by storing excess energy and releasing it when needed. In the essence of this development, lithium-ion rechargeable batteries stand out to be a practical solution for different energy uses, starting from small appliances, transportation, and even grid applications. Although at different levels of utilization, all types of Lithium batteries experience the problem of aging and capacity degradation. LiBs aging is a unique and complicated phenomenon influenced by the interdependency between different internal and external factors. Also, the degradation rates, modes, and mechanisms are affected by the battery's design, production process, and application field. Hence, it has become indispensable to simulate the battery functionality accurately to indicate and fix the possible failures in advance. Different approaches were employed for tracing and estimating the capacity fade. Some models rely on an offline investigation of the battery cell(s), while others succeed in scoring high estimation results while the battery is in operation. Data-driven model (DDM) is an example of an online model that doesn't need a comprehensive understanding of the battery components and the chemical reactions inside. Hence, a specific machine learning type of DDM approach called the long-short term memory algorithm (LSTM) was utilized in this thesis. LSTM excels in solving a time-dependent problem; hence, it is adopted here as battery aging is an accumulated problem affected by its previous state. Herein, a Lithium titanate oxide (LTO) pouch battery cell was employed and subjected to experimental characterization under two different C-rates at a temperature of 25 oC. The test results were then treated to extract specific health indicators that are studied in the literature. The estimation model was built in a python programming language with the help of data manipulation, machine learning, and visualization libraries. The model's outputs were then evaluated using metrics like mean absolute error (MSE), and root mean squared error (RMSE).