# Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

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## Abstract

**:**

## 1. Introduction

## 2. Study of SOC Estimation Method

#### 2.1. Lithium-Ion Battery Working Principle

#### 2.2. SOC Estimation Based on BP Algorithm

#### 2.2.1. Model Building

#### 2.2.2. Model Simulation

#### 2.3. SOC Estimation Based on PSO-BP Algorithm

#### 2.3.1. Model Building

#### 2.3.2. Model Simulation

#### 2.4. SOC Estimation Based on LSTM Algorithm

#### 2.4.1. Principle of LSTM Algorithm

- (1)
- LSTM forward calculation process

- (2)
- LSTM reverse calculation process

#### 2.4.2. Model Building

#### 2.4.3. Model Simulation

## 3. Contrast and Analysis

## 4. SOC Estimation Based on PSO-LSTM Algorithm

#### 4.1. Principle of PSO-LSTM Model

#### 4.2. Model Building

#### 4.3. Model Simulation

#### 4.4. Analysis of the Results of LSTM and PSO-LSTM for DST Condition

#### 4.5. Analysis of the Results of LSTM and PSO-LSTM for US06 Condition

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Result analysis of BP neural network model. (

**a**) Comparison of SOC estimation results of BP neural network models; (

**b**) BP neural network model SOC error results.

**Figure 5.**Result analysis of PSO-BP neural network model. (

**a**) PSO-BP neural network model prediction simulation; (

**b**) Comparison of the errors of the two neural network models.

**Figure 8.**Result analysis of LSTM neural network model. (

**a**) LSTM estimation results for constant flow condition; (

**b**) Estimation error results of LSTM for constant flow condition.

**Figure 9.**Comparative analysis of model training results. (

**a**) Comparison of loss values of different models; (

**b**) Comparison of testing accuracy of different models.

**Figure 10.**Comparison of results between different neural network models. (

**a**) Comparison of estimates between different neural network models; (

**b**) Comparison of errors between different neural network models.

**Figure 12.**Simulation comparison of LSTM and PSO-LSTM model. (

**a**) PSO-LSTM estimation results for constant flow condition; (

**b**) Estimation error results of PSO-LSTM for constant flow condition.

**Figure 15.**Simulation results of LSTM for DST condition. (

**a**) LSTM estimation results for DST condition; (

**b**) Estimation error results of LSTM for DST condition.

**Figure 16.**Simulation results of PSO-LSTM for DST condition. (

**a**) PSO-LSTM estimation results for DST condition; (

**b**) Estimation error results of PSO-LSTM for DST condition.

**Figure 18.**Simulation results of LSTM for US06 condition. (

**a**) LSTM estimation results for US06 condition; (

**b**) Estimation error results of LSTM for US06 condition.

**Figure 19.**Simulation results of PSO-LSTM for US06 condition. (

**a**) PSO-LSTM estimation results for US06 condition; (

**b**) Estimation error results of PSO-LSTM for US06 condition.

Type | Nominal Voltage | Nominal Capacity | Upper/Lower Cut-Off Voltage | Nominal Continuous Discharge Current |
---|---|---|---|---|

18650 | 3.7 V | 2200 mAh | 4.20 V/2.75 V | 0.2 C |

Model | RMSE | MAE | ME |
---|---|---|---|

BP | 0.0545 | 0.0386 | 0.0457 |

PSO-BP | 0.0432 | 0.0218 | 0.0265 |

LSTM | 0.0350 | 0.0162 | 0.0184 |

PSO-LSTM | 0.0115 | 0.0112 | 0.0147 |

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## Share and Cite

**MDPI and ACS Style**

Zhang, C.; Xu, X.; Li, Y.; Huang, J.; Li, C.; Sun, W.
Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network. *World Electr. Veh. J.* **2023**, *14*, 275.
https://doi.org/10.3390/wevj14100275

**AMA Style**

Zhang C, Xu X, Li Y, Huang J, Li C, Sun W.
Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network. *World Electric Vehicle Journal*. 2023; 14(10):275.
https://doi.org/10.3390/wevj14100275

**Chicago/Turabian Style**

Zhang, Chuanwei, Xusheng Xu, Yikun Li, Jing Huang, Chenxi Li, and Weixin Sun.
2023. "Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network" *World Electric Vehicle Journal* 14, no. 10: 275.
https://doi.org/10.3390/wevj14100275