A highly effective and robust structure-based LSTM with feature-vector tuning framework for high-accuracy SOC estimation in EV

This paper describes a new dual long short-term memory (LSTM) model for accurate estimation of the state of charge (SOC) of lithium–ion batteries in electric vehicles. The proposed network has highly effective and robust structure combining a mainstream ( m –) LSTM and gradient ( g –) LSTM in parallel, which can capture both data-temporal dependency and variability in battery's time-series. The g –LSTM possessing a gradient function consists of very few unit-cells corresponding to about 3 % of m –LSTM cells, and helps prevent the decrease of SOC accuracy caused by sudden changes of current and voltage during charging and discharging. Experimental results show that due to the gradient-tuning effect of feature vectors, the proposed model offers an innovative approach to predicting the SOC patterns with extraordinary precision, resulting in remarkably improved accuracy, on average 12.02 % higher than that of the vanilla LSTM. Further, the proposed dual LSTM demonstrates a fast convergence speed in the training process, and achieves highly accurate SOC estimation, even on unexpected data. Consequently, the computationally efficient and effective g –LSTM collaboration provides a highly robust and strong LSTM network structure to accurately estimate battery SOC, which helps maintain stable performance. • A dual LSTM network has a robust structure combining m –LSTM and g –LSTM in parallel. • Each captures data-temporal dependencies and variabilities in battery's time-series. • A more reliable and accurate SOC estimation can be achieved, even on unexpected data. • The g -LSTM collaboration also allows a fast convergence speed in the training process. • This model leads to remarkably improved accuracy 12.02 % higher than general LSTM.