CN-121995261-A - Battery fault early warning method, battery management system and vehicle

CN121995261ACN 121995261 ACN121995261 ACN 121995261ACN-121995261-A

Abstract

The invention discloses a battery fault early warning method, a battery management system and a vehicle, wherein the battery fault early warning method comprises the steps of obtaining a consistency key characteristic value of each electric core in a battery pack in each complete charge-discharge cycle of the battery pack, and calculating the deviation degree of the consistency key characteristic value of each electric core from a health baseline, wherein the health baseline is determined based on the statistical distribution of the consistency key characteristic values; the method comprises the steps of obtaining a track of the deviation degree of each cell, which changes sequentially along with time, as a deviation track, carrying out time sequence analysis on the deviation track of each cell to obtain dynamic early warning characteristic information of each cell, and carrying out early warning processing when the dynamic early warning characteristic information meets early warning conditions. The method provided by the invention realizes the dynamic evolution characteristic identification of the consistency abnormality of each battery cell in the battery pack, is beneficial to identifying potential abnormality before the further development of battery degradation, and improves the timeliness of early warning.

Inventors

YANG JING
QIN ZHIDONG
YAN KANGKANG
ZHANG YANCHAO

Assignees

北京卡文新能源汽车有限公司

Dates

Publication Date: 20260508
Application Date: 20260119

Claims (10)

1. A battery fault warning method, comprising: In each complete charge-discharge cycle of a battery pack, obtaining a consistency key characteristic value of each electric core in the battery pack, and calculating the deviation degree of the consistency key characteristic value of each electric core from a health baseline, wherein the health baseline is determined based on the statistical distribution of the consistency key characteristic values; obtaining a track of the deviation of each cell, which changes sequentially with time, as a deviation track; Performing time sequence analysis on the deviation tracks of each battery cell to obtain dynamic early warning characteristic information of each battery cell; and when the dynamic early warning characteristic information meets the early warning condition, early warning processing is carried out.
2. The battery fault pre-warning method according to claim 1, wherein the consistency key characteristic value includes at least one of a charge end voltage value, a voltage change rate of a preset battery state of charge interval, a rest voltage drop value, and a maximum temperature rise value in one complete charge and discharge cycle.
3. The battery fault pre-warning method according to claim 1, characterized in that the battery fault pre-warning method further comprises: Obtaining the consistency key characteristic value of each electric core in the charge-discharge cycles for a plurality of times to be used as a health cluster; Calculating an average value and a standard deviation value of each consistency key characteristic value in the health cluster; establishing the health baseline based on the mean and the standard deviation.
4. The battery fault warning method of claim 3, further comprising updating the health baseline at preset time intervals.
5. The battery fault pre-warning method according to claim 1, wherein the calculating the degree of deviation of the consistency key feature value of each cell from a health baseline comprises: And obtaining the deviation degree based on the consistency key characteristic value, the average value corresponding to the consistency key characteristic value and the standard deviation value corresponding to the consistency key characteristic value.
6. The battery fault pre-warning method of claim 1, wherein the dynamic pre-warning feature information includes at least one of an absolute value of a degree of deviation and a slope of the deviated trajectory; the early warning conditions comprise at least one of a first early warning condition and a second early warning condition; The first early warning condition comprises that the absolute value of the deviation degree of the battery cell exceeds a deviation degree threshold value in a plurality of continuous charge-discharge cycles; the second early warning condition comprises that the slope of the deviation track of the battery cell continuously changes to be a positive value along with time.
7. The battery fault pre-warning method according to claim 1, wherein when the dynamic pre-warning feature information satisfies a pre-warning condition, performing pre-warning processing includes: And executing a hierarchical early warning strategy according to the condition that the dynamic early warning characteristic information meets the early warning condition.
8. The battery fault pre-warning method according to any one of claims 1 to 7 further comprising collecting the voltage, temperature and total battery current of each of the cells in the battery pack and storing as historical data with a time tag to extract the consistency key characteristic value of each of the cells from the historical data during the charge-discharge cycle.
9. A battery management system, comprising: the plurality of sensors are used for collecting the total current of the battery pack and the voltage and current of each battery cell; A battery manager connected to a plurality of the sensors for implementing the battery fault warning method of any one of claims 1 to 8.
10. A vehicle comprising a battery pack and the battery management system of claim 9, the battery management system being coupled to the battery pack, the battery pack comprising a plurality of series-parallel connected cells.

Description

Battery fault early warning method, battery management system and vehicle Technical Field The present invention relates to the field of battery management technologies, and in particular, to a battery fault early warning method, a battery management system, and a vehicle. Background Currently, the monitoring of Battery pack inconsistency by a Battery Management System (BMS) MANAGEMENT SYSTEM mainly relies on the instantaneous values of parameters such as voltage and temperature, and judges in combination with a preset fixed threshold, for example, when the voltage difference or the temperature of the Battery cells exceeds a set threshold, a corresponding fault alarm is triggered. However, this static judgment method based on the comparison of the instantaneous parameter with the fixed threshold only focuses on whether the parameter is out of limit at a certain moment, and the abnormal variation trend below the threshold cannot be perceived. In the stage that the parameters of the battery core do not exceed the threshold value but show continuous deterioration trend, the system is difficult to sense abnormality in time, and the optimal time for early warning of potential faults is easy to be missed. Disclosure of Invention The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, an object of the present invention is to provide a battery fault early warning method, which implements dynamic evolution feature identification of consistency abnormality of each battery cell in a battery pack, is beneficial to identifying potential abnormality before further development of battery degradation, and improves early warning timeliness. A second object of the present invention is to provide a battery management system. A third object of the invention is to propose a vehicle. In order to achieve the above objective, the battery fault early warning method according to the embodiment of the first aspect of the present invention includes obtaining a consistency key feature value of each cell in a battery pack in each complete charge-discharge cycle of the battery pack, calculating a deviation degree of the consistency key feature value of each cell from a health baseline, wherein the health baseline is determined based on a statistical distribution of the consistency key feature value, obtaining a trajectory of the deviation degree of each cell that changes sequentially with time as a deviation trajectory, performing timing analysis on the deviation trajectory of each cell to obtain dynamic early warning feature information of each cell, and performing early warning processing when the dynamic early warning feature information meets an early warning condition. According to the battery fault early warning method provided by the embodiment of the invention, in each complete charge-discharge cycle, the consistency key characteristic value is firstly extracted for each battery cell in the battery pack, and the consistency key characteristic value is the characteristic quantity capable of comprehensively reflecting the electrochemical performance and the working state of the battery cell in one cycle, so that the health state and the performance level of the battery cell in the cycle can be represented more stably. Meanwhile, a health baseline is established based on the statistical distribution of all the cell consistency key characteristic values, and the health baseline reflects the overall consistency level and the normal fluctuation range of the battery pack in the current operation stage. And then, comparing the consistency key characteristic value of each cell with a health baseline and calculating the deviation degree of the consistency key characteristic value, and quantifying the deviation degree of each cell relative to the overall state of the battery pack, wherein the deviation degree can directly reflect whether the cell starts to deviate from the overall cluster or not and generate potential inconsistent degradation, so that slight inconsistent which is difficult to visually perceive is converted into statistically significant deviation information. And then, carrying out time sequence analysis on the deviation track to extract dynamic early warning characteristic information, so that the system can identify potential abnormality only according to deviation trend and evolution characteristic before obvious out-of-limit of the consistency key characteristic value does not occur. Therefore, the identification of the abnormal dynamic evolution characteristics of the consistency of each battery cell in the battery pack is realized through the logic chain formed by the characteristic value, the deviation degree, the deviation track and the time sequence analysis, and compared with the prior art which only judges based on the instantaneous parameters and the fixed threshold value, the sprouting stage of the battery degradation can be captured earlier, so that early