Forecasting Li-ion battery State of Charge using Long-Short-Term-Memory network

Estimating the state of charge (SOC) for lithium-ion batteries (LIB) has become a highly desirable task, but also critical, especially as electrified vehicles become more common. However, due to the non-linear behaviour of these batteries, accurately estimating SOC remains a challenge. As a result, traditional theory-based methods are often being replaced by data-driven approaches, thanks to the greater availability of battery data and advances in artificial intelligence. Recurrent neural networks (RNNs), in particular, are promising methods to be exploited, because they can capture temporal dependencies and predict SOC without a battery model. Long short term memory (LSTM), a specific type of RNN, can accurately predict SOC values in real-time and forecast future SOC values within different time horizons.