A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Abstract

Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

“…Therefore, the method based on the intercept frequency is readily applicable to batteries of different chemistries, dimensions, and storage capacities. However, the intercept frequency can be considerably influenced by interfering currents flowing through the battery pack under operating conditions 101–104 . This can make the result inaccurate.…”

“…However, the intercept frequency can be considerably influenced by interfering currents flowing through the battery pack under operating conditions. [101][102][103][104] This can make the result inaccurate. On this basis, a new method based on other frequencies (the imaginary part of the LIB impedance is equal to a non-zero constant) was developed.…”

A novel Drosophila‐back propagation method for the lithium‐ion battery state of charge estimation adaptive to complex working conditions

“…Data‐driven methods are mainly based on neural networks or machine learning without the need to know the internal structure of the lithium‐ion battery and the initial SOC. At present, there are newer methods such as recurrent neural network (RNN), long short‐term memory neural networks (LSTM), convolutional neural network (CNN), support vector machines (SVM), and more classic and commonly used neural network algorithms such as BP neural networks, Elman neural networks and so on 16 . Xiao et al 17 used the RNN‐GRU algorithm, the RNN can use historical information from the past to help understand current signals.…”

An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery

“…At present, scholars have carried out considerable research on SOC estimation. In addition, the methods for SOC estimation can be divided into three categories: direct, model-based, and data-driven approaches [6][7][8]. The direct methods commonly used are Coulomb counting method [9] and open circuit voltage method [10].…”