# Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

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## Abstract

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## 1. Introduction

## 2. Overview of SOH

#### 2.1. Define SOH with Battery Internal Resistance

#### 2.2. Define SOH with the Number of Remaining Battery Cycles

#### 2.3. Define SOH with Battery Capacity

## 3. Theoretical Basis of EIS

#### 3.1. Basic Principle of EIS

#### 3.2. Analyze the Principle of Battery Capacity Attenuation from EIS

## 4. Summary of SOH Estimation Based on EIS

#### 4.1. ECM-Based Approach

- (1)
- Based on a single equivalent circuit model:

- (2)
- Based on multiple equivalent circuit models:

Author(s) | Models | F (Hz) for Model | SOH Errors |
---|---|---|---|

Xiong Rui et al. [84] | ECM | 0.02–5000 Hz | Error < 3% |

Matteo Galeotti et al. [85] | ECM | 0.2 Hz–5 kHz | Maximum error = 3.73% |

Wang Xue yuan et al. [86] | ECM | 0.01 Hz–1 kHz | Absolute error < 15% |

Zhang et al. [87] | ECM | 0.1 Hz–5 kHz | Error < 4% |

Pietro Iurilli et al. [88] | ECM | 0.1–1000 Hz | - |

Yang et al. [89] | ECM | 50 mHz–10 kHz | Accuracy increased by 46.38% |

De zhi Li et al. [14] | ECM | 0.02 Hz–20 kHz | RMSE = 0.0497 |

Akram Eddahech et al. [90] | RNN | 0.01 Hz–10 kHz | MSE = 0.462 |

GENG Meng Meng et al. [91] | BP | 10 mHz–10 kHz | MAPE = 1.36%, RMSE = 1.57% |

LIU Jia hao et al. [92] | LSTM | 0.01 Hz–10 kHz | RMSE = 3.981% |

Zhang et al. [93] | GPR | 0.02 Hz–20 kHz | RMSE = 5.03% |

Chun Chang et al. [94] | CS–Elman model | 20 mHz–20 kHz | R^{2} = 99.89% |

T. K. Pradyumna et al. [95] | CNN | 0.1–1 kHz | RMSE = 0.233% |

Li De zh et al. [14] | IPSO-CNN-BiLSTM model | 0.02 Hz–20 kHz | RMSE = 0.0183 |

Chen, X. et al. [96] | SFS-GPR model | 0.02 Hz–20 kHz | RMSE = 0.042 |

#### 4.2. Data-Driven Approach

- (1)
- Based on a single-network mode:

- (2)
- Multi-network model based on automatic feature extraction:

## 5. Conclusions

#### 5.1. Summary

#### 5.2. Outlook

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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SOH Prediction Method | Advantage | Disadvantage |
---|---|---|

Equivalent circuit model method | It can be applied online, and the prediction accuracy is not affected by the initial value; it is easy to converge, and the prediction performance is higher than that of the current integration method and the open-circuit voltage method. | It is difficult to establish an equivalent circuit model for lithium batteries suitable for various operating conditions, and the identification of model parameters is difficult, with a large computational load and limited application range. |

Electrochemical impedance spectroscopy (EIS) method | No need to design complex algorithms, easy to implement, suitable for different types of lithium batteries. | It is unable to be used online and has limited prediction accuracy; it is generally used for calibration of SOH in an offline state. |

Data-driven approach | There is no need to consider the complex electrochemical characteristics of lithium batteries and establish complex circuit models but only to design a reasonable data-driven learning model. | A large amount of lithium battery EIS data is required to ensure that the BMS platform has strong computing power. |

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## Share and Cite

**MDPI and ACS Style**

Sun, X.; Zhang, Y.; Zhang, Y.; Wang, L.; Wang, K.
Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy. *Energies* **2023**, *16*, 5682.
https://doi.org/10.3390/en16155682

**AMA Style**

Sun X, Zhang Y, Zhang Y, Wang L, Wang K.
Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy. *Energies*. 2023; 16(15):5682.
https://doi.org/10.3390/en16155682

**Chicago/Turabian Style**

Sun, Xinwei, Yang Zhang, Yongcheng Zhang, Licheng Wang, and Kai Wang.
2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy" *Energies* 16, no. 15: 5682.
https://doi.org/10.3390/en16155682