# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 1 Introduction  
With the constant occurrence of energy crisis and environmental pollution in the modern era, the development of natural and efficient energy storage devices is required [1]. Among various energy storage devices, lithium batteries have recently attracted considerable attention due to their advantages, such as high power density, low self-discharge rate, long life cycle, and environmentally friendly characteristics [2]. Lithium batteries have been widely used in various applications, such as electric vehicles, bicycles, and scooters, other means of transportation [3, 4], wireless power tools and vacuum cleaners, and smart phones [5].  
Lithium batteries are a major power source used as an alternative to existing batteries due to their portability and excellent heat resistance, compared with primary batteries and lead storage batteries. However, if not managed properly, their performance may deteriorate, and they may become damaged, which, in severe cases, may cause an explosion [6]. Such problems are related to economy, reliability, and safety. Therefore, accurate measurement and estimation of the State of Charge (SOC) of a battery are important to improve reliability and provide safe service to users [7]. The SOC is an important parameter of a Battery Management System (BMS) [8] and indicates the available capacity of the current battery. Everything else is based on SOC; thus, accuracy and robustness need to be achieved.  
Currently, the electric vehicle field is where the development of lithium batteries and research on battery SOC estimation technology are the most significant. It is very important to estimate the SOC in real time due to the characteristics of the electric vehicle, and it is possible to estimate more accurately and efficiently to estimate the SOC of other lithium battery products. However, it is difficult to estimate accurate real-time SOC due to cost and limited computational power [9].  
Various methods for SOC estimation have been recently proposed. However, most are the SOC that estimate the battery cell. In addition, studies on how to estimate the SOC in real time are scarce. Lithium-ion batteries can supply stronger power to high-power electrical products with a high energy density than a cell-based battery. Therefore, estimating the SOC of a battery pack leads to an efficient and safe use of the battery [10].  
In this paper, a method for real-time SOC estimation of the cells of the lithium battery packs using Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM) is proposed. The relationship between the voltage and SOC was determined after charging and discharging four lithium batteries at both room temperature and high temperature. Subsequently, the SOC of each battery cell was estimated by learning, with each of the four neural networks, using the collected data. Moreover, the performance of the method was evaluated through the simulation. In this paper, SOC estimation was performed in three methods. First, the error rate differences according to the number of inputs were compared; second, the error rate differences according to the temperature variable data used were compared; and finally, the differences in the performance between the MNN and LSTM were compared. Subsequently, the SOC of the lithium battery was estimated in real time using the input value of the learning method obtained with the lowest error rate.