Abstract:
Battery technology plays a key factor in the advancement of electric cars and mobile
robots. Efficient battery management, particularly accurate estimation of the state of
charge (SOC), is of paramount importance in both theoretical research and practical
applications. However, SOC estimation for batteries is a challenging task due to their
inherent time-varying and non-linear characteristics.
In this study, we delve into the complex nature of lithium cells and explore the potential
of neural networks for SOC prediction. Specifically, we compare three different neural
network architectures using the LG HG2 dataset, which provides valuable insights into
lithium cell behavior.
By analyzing the theory of neural networks and conducting comprehensive experiments,
we aim to improve the accuracy of SOC estimation. Our focus is on understanding
the intricate relationship between input variables and SOC, and harnessing the power of
neural networks to capture and model this relationship effectively.
The results of our study not only contribute to the field of battery management systems
but also shed light on the broader understanding of SOC estimation in the context of
lithium cells. By evaluating the performance of different neural network architectures
and analyzing the obtained results, we gain valuable insights into optimizing battery
performance and enhancing the overall efficiency of electric vehicles and mobile robots.
