State of Charge (SoC) Accurate Estimation Using Different Models of LSTM

Accurately estimating the State of Charge (SoC) is essential for optimal battery charge control and predicting the operational range of electric vehicles. The precision of SoC estimation directly influences these vehicles’ range and safety. However, achieving accurate SoC estimation is challenging due to environmental variations, temperature changes, and electromagnetic interference. Numerous technologies rely on Machine Learning (ML) and Artificial Neural Networks (ANN). The proposed model employs two or more cascaded Long Short-Term Memory (LSTM) networks, which have effectively reduced the Mean Square Error (MSE). Additionally, other models such as Nonlinear Auto Regressive models with exogenous input neural networks (NARX) combined with LSTM, and standard LSTM models have been simulated. In this research a model has been presented with reduced Root Mean Square Error (RMSE) compared to a LSTM by 78% and has reduced the RMSE compared to NARX with LSTM by 47%.