Lithium-ion battery state-of-charge estimation based on long-short-term memory neural network and square-root cubature Kalman filter

Abstract

Due to the widespread use of Li-ion batteries in electric vehicles, battery management systems for monitoring the status and ensuring the safe operation of Li-ion batteries have been extensively studied. Online monitoring of the state of charge (SOC) is crucial for lithium-ion batteries, but achieving precise SOC estimation is a difficult task due to battery dynamics and the influence of factors such as current, temperature, and operating conditions on SOC variability. This paper introduces a novel approach that combines a Long Short-Term Memory (LSTM) network with a square-root cubature Kalman filter (SRCKF) to address this challenge. To tackle the issue at hand, the proposed methodology employs a two-step approach. Initially, the LSTM network is utilized to capture the complex relationship between the state of charge (SOC) and the measured variables, including current, voltage, and temperature. Subsequently, the output of the LSTM network is subjected to smoothing using the SRCKF technique, leading to precise and consistent SOC estimation. A notable advantage of this method is its ability to simplify the arduous task of parameter tuning for the LSTM network, eliminating the need for constructing a battery model. The experimental results demonstrate that the maximum error in estimating the state of charge (SOC) using this particular method is constrained within the 5% threshold. Compared with using a separate LSTM method at different temperatures, by using the combination method, the root mean square error and maximum error of SOC estimation are greatly reduced.