Lithium-ion batteries have emerged as a prevalent power source in a variety of industries, including the electric vehicle sector due to their higher energy density and low self-discharge. With the use and passage of time, batteries degrade and eventually die, endangering the integrity of the objects they power. The ability to accurately predict the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for optimizing their utilization and ensuring their safe operation. For this purpose, a deep learning-based approach trained on the widely used Oxford battery degradation dataset with the help of Generative Adversarial Networks (GANs) has been implemented. The designed network consists of a Long-Short-Term Memory (LSTM) architecture with the implementation of a stratification strategy and a custom loss function. The illustrative results show that the suggested approach can produce adaptable and reliable predictions of the RUL.
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