Remaining Useful Life (RUL) Estimation for Lithium-Ion Battery Using Extreme Learning Machine

Abstract

Study proposes a new method for predicting the remaining useful life (RUL) of lithium-ion batteries using the Extreme Learning Machine (ELM) algorithm by employing advanced feature extraction techniques to identify critical parameters influencing battery degradation. The model captures intricate patterns in battery health, enabling precise RUL prediction by analyzing voltage, current, temperature, and capacity data. A correlation investigation executed in this paper emphasizes the importance of designated variables, for example, discharge capacity dwindle and inner resistance, in identifying lifecycle of the battery. The ELM is an advanced machine learning algorithm, which has high speed for problem-solving capability, and is used to design a vigorous prognostic approach. Experiment-based results analysis validates that the suggested method accomplishes higher correctness and computational speed compared to conventional approaches. The algorithm's enactment is authenticated via several performance matrices (i.e., AE, MAPE and R²), validating model’s reliable enactment in concrete actual circumstances. Presented study delivers an appreciated algorithm for BLI health diagnosis, augmenting the consistency and reliability of BLI in EVs, RES, and further uses. The outcomes emphasize the impending of AI/ML methods in evolving analytical maintenance for ESS.