# Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

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## Abstract

**:**

## 1. Introduction

## 2. Diagnosis and Prognosis Method

_{i}) is the data probability density in the ith region, and n is the number of regions.

_{ave}denotes the average Shannon entropies, and σ

_{E}denotes the standard deviation of entropy.

## 3. Data Acquisition Platform

## 4. The Thermal Fault Prognosis Analysis and Discussion

#### 4.1. Thermal Management Schematic

#### 4.2. The Fault Prognosis of Over-Temperature

_{b}. Boxplots can reflect the center and spread scope of the data distribution. By drawing the boxplots of multiple sets of data on the same coordinates, the distribution difference is clearly displayed. The structure diagram of the boxplot is shown in Figure 7. The boxplot requires the statistical concept of quartiles, which means the position numbers of three segmentation points. Q1 denotes the lower quartile, which is equal to the number of 25% of all values. Q2 is the median, which is equal to the number of 50% of all values. Q3 is the upper quartile, which is equal to the number of 75% of all values. The abnormal coefficient A

_{b}is the median of the boxplot in this paper.

_{b}of Probe 1 is much greater than that of Probe 9 and the others. By defining certain detection thresholds as A

_{b}= 1 and A

_{b}= 1.2, the over-temperature fault alarm can be avoided if the abnormal temperature is detected in advance by this method. Actually, for the purpose of accurate over-temperature fault prognosis, much more monitoring data were derived from NSMC-EV. The evaluation strategy of the abnormal temperature was obtained by the trial-and-error method through a large number of analytical results, which is feasible, reliable, and can accurately forecast both the time and location of over-temperature faults. Thus, this method can effectively prevent the over-temperature fault by detecting the abnormal temperature in real-time.

#### 4.3. The Fault Prognosis of Temperature Difference

_{b}< 1, which is consistent with the temperature curves in Figure 9. Thus, all of the probes have a safe temperature status and no abnormal temperature can be detected in the first 3 h.

_{b}> 1, which is consistent with the temperature curves in Figure 9. Thus, abnormal temperature of Probe 11 can be detected in the first 6 h. From Figure 13a, Probe 11 has a distinct abnormal fluctuation and is easier to detect. Figure 13b demonstrates that the median position of Probe 11 is higher compared to those of other probes and the abnormal coefficient A

_{b}> 1. The results show excellent consistency with the previous temperature curves in Figure 9. The excessive TD fault of Vehicle 2 occurred after it traveled more than 9 h. Therefore, the proposed prognosis method can detect the abnormal probe in real-time and identify the fault location in advance.

_{b}> 1. However, the excessive TD fault can be avoided if the abnormal temperature is detected in advance. Actually, for accurate excessive TD fault prognosis, much more monitoring data were retrieved from NSMC-EV and analyzed, which reveals the proposed method is feasible, reliable, and stable to accurately predict the time and location of excessive TD faults within a battery pack. Thus, this method can effectively prevent the excessive TD fault by detecting the abnormal temperature in real-time.

#### 4.4. The Security Management Strategy and Discussion

_{b}= 1 and A

_{b}= 1.2, the cell with abnormal temperature can be detected before the thermal faults occur, which has vital significance for the future prognosis and safety management of the battery fault, especially for the prevention of thermal runaway. The prognosis strategy of the thermal fault can be obtained through analyzing much more monitoring data retrieved from NSMC-EV using the trial-and-error method. The prognosis strategy flowchart of the thermal fault is shown in Figure 16.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 4.**General schematic of BTMS using air and liquid. (

**a**) Three air BTM methods; (

**b**) Three liquid BTM methods.

**Figure 11.**The abnormal coefficient and boxplot at the first 3 h of Vehicle 2. (

**a**) The abnormal coefficient curves; (

**b**) Boxplot of the abnormal coefficient.

**Figure 12.**The abnormal coefficient and boxplot at the first 6 h of Vehicle 2. (

**a**) The abnormal coefficient curves; (

**b**) Boxplot of the abnormal coefficient.

**Figure 13.**The abnormal coefficient at the first 9 h of Vehicle 2. (

**a**) The abnormal coefficient curves; (

**b**) Boxplot of the abnormal coefficient.

**Figure 15.**The abnormal coefficient and boxplot at the first 3 h of Vehicle 2. (

**a**) The abnormal coefficient curves; (

**b**) Boxplot of the abnormal coefficient.

Order Number | Monitoring Object | Monitoring Purpose |
---|---|---|

1 | Battery voltage | To confirm whether there is a value beyond the range. |

2 | Cell voltage | The low voltage will lead to insufficient capacity, and the high voltage will cause high temperature, gas precipitation, water losses, and grid corrosion of the battery. |

3 | Battery temperature | To identify potential problems and optimize the vehicle operation and cycle life of the battery. Once beyond the maximum value means that there is a potential thermal runaway and it requires human intervention. |

4 | Ambient temperature | Too high an ambient temperature will shorten battery life and too low an ambient temperature will lead to battery capacity decline. |

5 | Temperature difference | Large temperature difference is because of the inconsistency of the battery, which will cause endurance deterioration. |

6 | Charge and discharge current | Provide the health state information of the battery to users, which can be used to indicate the operating state and the integrity of the battery connection. |

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**MDPI and ACS Style**

Hong, J.; Wang, Z.; Liu, P.
Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. *Energies* **2017**, *10*, 919.
https://doi.org/10.3390/en10070919

**AMA Style**

Hong J, Wang Z, Liu P.
Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. *Energies*. 2017; 10(7):919.
https://doi.org/10.3390/en10070919

**Chicago/Turabian Style**

Hong, Jichao, Zhenpo Wang, and Peng Liu.
2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles" *Energies* 10, no. 7: 919.
https://doi.org/10.3390/en10070919