A novel positional encoded attention-based Long short-term memory network for state of charge estimation of lithium-ion battery

Abstract

Accurate estimation of the state of charge (SOC) in battery management systems is critical to the global electrification revolution in various fields. This article introduces a novel long short-term memory (LSTM) network enhanced with positional encoding and a time-step internal attention mechanism for accurate SOC estimation. The proposed method adeptly utilizes positional encoding to transform one-dimensional battery data sequences into a multi-dimensional space through a series of sinusoidal functions parameterized by a diverse frequency spectrum, capturing unique temporal characteristics and relationships inherent in battery data. The enhanced data is then processed by the time-step internal attention, which adeptly discerns interdependencies among input variables at each specific time interval. This dynamic adjustment captures evolving relationships, ensuring a transformed data representation. This enriched data is subsequently processed by the LSTM network, addressing long-term correlations and temporal dynamics. The model was trained and tested on a public dataset that includes the data from different working conditions and temperatures. Primary training followed a leave-one-out (LOO) approach focusing on working conditions, with further validation across temperature variations. Empirical analysis demonstrated that the proposed method surpassing baseline methods, attaining the optimal root mean square error and mean absolute error of 0.91 % and 0.68 % respectively.