# Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction

^{*}

## Abstract

**:**

## 1. Introduction

- A specific indirect HI is extracted from the charge monitoring data. A correlation analysis is used to show that these indirect HIs accurately reflect the capacity. Therefore, complicated measurements or elaborate calculations are no longer needed.
- The combination of soft-sensing and GRU NN with sliding window produces a model capable of both accurate state-of-health estimation and reliable long-term RUL prediction using historical data sets.
- Dropout and early stopping methods were also used to prevent overfitting.
- The effectiveness of the method is validated and verified by the real-world NASA data set.

## 2. Gated Recurrent Unit Neural Network

## 3. Data Preparation

#### 3.1. Test Data

#### 3.2. Health Indicator Extraction

#### 3.3. Correlation Analysis

## 4. Algorithm and Approach

#### 4.1. General Algorithm

#### 4.2. SOH Estimation Framework

#### 4.3. Approach

#### 4.3.1. Data Set Selection

#### 4.3.2. Hyperparameter Optimization

## 5. Results and Discussion

#### 5.1. Evaluation Parameters

#### 5.2. SOH Results Analysis

#### 5.3. RUL Results Analysis

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 7.**The SOH estimation result at starting point 0.3 for batteries (

**a**) Bat. 5; (

**b**) Bat. 6; (

**c**) Bat. 7; (

**d**) Bat. 18.

**Figure 8.**The RUL prediction results at different starting points for batteries (

**a**) Bat. 5; (

**b**) Bat. 6; (

**c**) Bat. 7; (

**d**) Bat. 18.

Correlation between CCCT and Capacity | Bat. 5 | Bat. 6 | Bat. 7 | Bat. 18 |
---|---|---|---|---|

Spearman: | 0.993 | 0.996 | 0.992 | 0.975 |

Pearson: | 0.997 | 0.993 | 0.990 | 0.986 |

Test | Train | Val |
---|---|---|

Bat. 5 | Bat. 7 and 18 | Bat. 6 |

Bat. 6 | Bat. 5 and 18 | Bat. 7 |

Bat. 7 | Bat. 6 and 18 | Bat. 5 |

Bat. 18 | Bat. 6 and 7 | Bat. 5 |

Description | Parameter |
---|---|

Sequence length | 10 |

Learning rate | 9 × 10^{−4} |

Number of Epochs | 100 |

Batch size | 16 |

Optimizer | Adam |

Loss | Mean Square Error |

Battery | RMSE | MAE | R2 |
---|---|---|---|

Bat. 5 | 0.0060 | 0.0041 | 0.993 |

Bat. 6 | 0.0103 | 0.0065 | 0.984 |

Bat. 7 | 0.0056 | 0.0037 | 0.991 |

Bat. 18 | 0.0121 | 0.0089 | 0.925 |

Battery | Starting Point | Real RUL | Pred. RUL | AE RUL |
---|---|---|---|---|

Bat. 5 | 0.3 | 75 | 75 | 0 |

0.5 | 42 | 40 | −2 | |

0.7 | 8 | 6 | −2 | |

Bat. 6 | 0.3 | 50 | 54 | 4 |

0.5 0.7 | 17 - | 12 - | −5 - | |

Bat. 7 | 0.3 | 115 | 120 | 5 |

0.5 | 82 | 85 | 3 | |

0.7 | 48 | 56 | 8 | |

Bat.18 | 0.3 | 47 | - | - |

0.5 0.7 | 14 - | 25 - | 11 - |

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**MDPI and ACS Style**

Hell, S.M.; Kim, C.D.
Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction. *Batteries* **2022**, *8*, 192.
https://doi.org/10.3390/batteries8100192

**AMA Style**

Hell SM, Kim CD.
Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction. *Batteries*. 2022; 8(10):192.
https://doi.org/10.3390/batteries8100192

**Chicago/Turabian Style**

Hell, Sebastian Matthias, and Chong Dae Kim.
2022. "Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction" *Batteries* 8, no. 10: 192.
https://doi.org/10.3390/batteries8100192