Prognostics and Health Management (PHM) is a crucial approach for battery systems, aiming to predict and manage system health to prevent failures and address potential issues. Accurate estimation ofbattery capacity is essential for proactive maintenance and replacement strategies, preventing unexpected failures, and optimising performance. Measurable data like voltage, current, and temperature profiles provide valuable insights into battery behaviour and performance. Accurate estimation of the state of health (SoH) is crucial for effective battery management and maintenance, enabling informed decisions on battery replacement, optimising battery lifespan, and maximising performance and longevity. This chapter uses various neural network models, such as Feed forward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to analyse battery capacity. Experiments were conducted on NASA-randomised datasets. The experimental results proved that the proposed models are better compared to the existing state-of-the-art methods in terms of accuracy, precision, and recall.
S et al. (Mon,) studied this question.