Lithium-ion battery prognostics through reinforcement learning based on entropy measures

Abstract

Lithium-ion is a progressive battery technology that has vastly been used in different electrical systems. Failure in the battery can lead to failure in the entire system where the battery is embedded and cause irreversible damage. To avoid the probable damages, research is actively conducted, and data-driven methods are proposed based on prognostics and health management (PHM) systems. PHM can use multiple time-scale data and stored information from battery capacities over cycles to determine the battery state of health (SOH) and its remaining useful life (RUL). This results in battery safety, stability, reliability, and longer lifetime. In this paper, we propose different data-driven approaches to battery prognostics that rely on: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA) and Reinforcement Learning (RL) based on the Permutation Entropy of battery voltage sequences at each cycle since they take into account the vital information from the past data and result in high accuracy

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