Research on state-of-charge Estimation of Lithium-ion Batteries Based on Improved Sparrow Search Algorithm-BP Neural Network

Abstract

As one of the key parameters of the battery management system (BMS), the accurate estimation of the state of charge (SOC) of lithium-ion batteries is of great significance to the development of electric vehicles. Aiming at the problem that the BP neural network is easy to fall into the local optimum, taking lithium-ion batteries as the research object, a lithium-ion battery SOC estimation method based on the Improved Sparrow Search Algorithm (ISSA) optimized BP neural network is proposed. In order to improve the estimation accuracy, the global optimal solution of the previous generation is introduced into the discoverer's position update strategy, and the simulation experiment is carried out in MATLAB, combined with the test data for analysis. The experimental results show that the improved sparrow search algorithm can better optimize the BP neural network to estimate the state of charge of the lithium-ion battery, and the average error is controlled within 1%. The ISSA-BP model is compared with other models to verify the rationality and accuracy of the model, and provide a reliable basis for monitoring the status of other important batteries.

A multi-strategy improved sparrow search algorithm for mobile robots path planning

“…The SSA algorithm has attracted much attention since it was proposed. Ou et al [17] applied it to the location of the logistics distribution center, Zhang and Han [18] applied SSA to solve the symmetric traveling salesman problem, Zheng and Liu [19] applied SSA to the optimal scheduling of microgrid energy storage, Zhao et al [20] applied it to wireless sensor deployment, and Li et al [21] applied SSA to BP neural network optimization. Jianhua and Zhiheng [22] used a similarity function to measure the dispersion of sparrow populations and developed search rules for sparrow populations under different degrees of dispersion.…”

Influence of Time-Series Length and Hyperparameters on Temporal Convolutional Neural Network Training in Low-Power Battery SOC Estimation

“…Li et al applied a three-dimensional CNN algorithm (3DCNN) for the first time for battery SOC estimation. They further introduced the FCNN algorithm, which improves the accuracy of the SOC estimation by considering the degree of battery aging [26]. Zhang et al proposed a series of intelligent SOC estimation methods using GA and particle swarm optimization (PSO) algorithms to optimize BP based on the Levenberg-Marquardt (L-M) algorithm, demonstrating an accuracy and robustness higher than those of the EKF method [27].…”