# Recognition and Diagnosis Method of Accelerated Aging of Lithium-Ion Battery Based on Logistic Regression

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## Abstract

**:**

## 1. Introduction

## 2. Accelerated Aging Characterization Parameters and Change Characteristics of Lithium Batteries

#### 2.1. Curve Matching Method

#### 2.2. Characteristic Parameters in DV Curve

#### 2.3. Characteristic Parameters in IC Curve

#### 2.4. Correlation Analysis between the Change of Characteristic Parameters and the Inflection Point of Accelerated Aging

## 3. Recognition Model of the Inflection Point of Accelerating Aging of Battery

#### 3.1. Logistic Regression and Related Theoretical Basis

#### 3.2. Model Building Method Based on Logistic Regression

## 4. Experimental Results and Model Verification

## 5. Conclusions

- (1)
- In view of the fact that it is difficult to obtain the complete charging curve of the battery, the DTW algorithm is used to realize the matching analysis of the curve, and the battery characteristic curve in the range of 15–75% SOC is compared with the complete characteristic curve of the new battery, and the accelerated aging of the battery is compared. Before and after the change of the battery characteristic curve in this interval, it is necessary to extract the characteristic parameters that have obvious changes before and after the accelerated aging of the battery. Finally, a characteristic parameter system including the distance between the DV curve (40–60% SOC), the minimum valley value of the DV curve bottom, the main peak area of the IC curve, and the difference sequence of the main peak area is established.
- (2)
- As the lithium-ion battery ages, the DV curve moves upward, the distance between the curves expands, and the main peak area of the IC curve decreases. When accelerating aging of the battery occurs, the capacity decay rate increases, and an obvious inflection point appears, and the corresponding characteristic parameters also change significantly. The bottom of the DV curve and the distance between the DV curve are significantly increased, while the main peak of the IC curve is rapidly reduced.
- (3)
- Use logistic regression to train the characteristic parameter set and establish a judgment model for accelerating battery aging. Extract the required characteristic parameters in the characteristic curve of each cycle of the battery to be tested, and input them into the established model in sequence according to the number of cycles. The model output 0 means that the battery has not undergone accelerated aging, and the model output 1 means that the battery has accelerated aging failure.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

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Distance between DV Curves | The Bottom of the DV Curve | Area of the Main Peak of the IC Curve | Difference Sequence of Main Peak Area of IC Curve | Lable |
---|---|---|---|---|

… | … | … | … | … |

3.70 × 10^{−6} | 7.26 × 10^{−5} | 49.6 | 0 | 0 |

6.90 × 10^{−6} | 7.27 × 10^{−5} | 49.0 | 0.6 | 0 |

… | … | … | … | … |

2.34 × 10^{−4} | 8.58 × 10^{−5} | 42.5 | 7.1 | 1 |

3.97 × 10^{−4} | 9.16 × 10^{−5} | 41.0 | 8.6 | 1 |

… | … | … | … | … |

Distance between DV Curves | The Bottom of the DV Curve | Area of the Main Peak of the IC Curve | Difference Sequence of Main Peak Area of IC Curve | Lable |
---|---|---|---|---|

3.70 × 10^{−6} | 7.26 × 10^{−5} | 49.6 | 0 | 0 |

6.90 × 10^{−6} | 7.27 × 10^{−5} | 49.0 | 0.6 | 0 |

1.00 × 10^{−5} | 7.36 × 10^{−5} | 48.5 | 1.1 | 0 |

2.9 × 10^{−5} | 7.56 × 10^{−5} | 48.0 | 1.6 | 0 |

2.54 × 10^{−5} | 7.81 × 10^{−5} | 47.3 | 2.3 | 0 |

7.87 × 10^{−5} | 8.07 × 10^{−5} | 46.7 | 2.9 | 0 |

… | …. | … | … | … |

2.34 × 10^{−4} | 8.58 × 10^{−5} | 41.6 | 8 | 1 |

6.59 × 10^{−4} | 9.47 × 10^{−5} | 41 | 8.6 | 1 |

… | … | … | … | … |

Cycles | Distance between DV Curves | The Bottom of the DV Curve | Area of the Main Peak of the IC Curve | Difference Sequence of Main Peak Area of IC Curve | Lable |
---|---|---|---|---|---|

… | … | … | … | … | … |

200 | 4.66 × 10^{−6} | 7.14 × 10^{−5} | 49.0 | 0 | 0 |

400 | 1.14 × 10^{−5} | 7.29 × 10^{−5} | 48.4 | 0.6 | 0 |

600 | 2.91 × 10^{−5} | 7.34 × 10^{−5} | 47.7 | 1.3 | 0 |

… | … | … | … | … | … |

1400 | 1.47 × 10^{−4} | 7.70 × 10^{−5} | 45.2 | 3.8 | 0 |

1600 | 4.97 × 10^{−4} | 8.29 × 10^{−5} | 41.7 | 7.3 | 1 |

1800 | 1.38 × 10^{−3} | 9.13 × 10^{−5} | 40.2 | 8.8 | 1 |

… | … | … | … | … | … |

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**MDPI and ACS Style**

Zou, N.; Zhang, C.; Wang, Y.; Zhang, L.
Recognition and Diagnosis Method of Accelerated Aging of Lithium-Ion Battery Based on Logistic Regression. *World Electr. Veh. J.* **2021**, *12*, 143.
https://doi.org/10.3390/wevj12030143

**AMA Style**

Zou N, Zhang C, Wang Y, Zhang L.
Recognition and Diagnosis Method of Accelerated Aging of Lithium-Ion Battery Based on Logistic Regression. *World Electric Vehicle Journal*. 2021; 12(3):143.
https://doi.org/10.3390/wevj12030143

**Chicago/Turabian Style**

Zou, Naipeng, Caiping Zhang, Yubin Wang, and Linjing Zhang.
2021. "Recognition and Diagnosis Method of Accelerated Aging of Lithium-Ion Battery Based on Logistic Regression" *World Electric Vehicle Journal* 12, no. 3: 143.
https://doi.org/10.3390/wevj12030143