The invention belongs to the technical field of automobile battery detection, and particularly relates to a temperature anomaly probe identification algorithm based on a probability mutation rule. Thealgorithm comprises the following steps of a data acquisition step: acquiring temperature data of a probe by taking a vehicle as a unit; an analysis and screening step: performing statistical analysis on temperature data; analyzing cumulative temperature frequency distribution smaller than a preset temperature value in a cumulative frequency mode, screening out a corresponding temperature value when the cumulative temperature frequency is smaller than X, and recording the temperature value as a low-temperature threshold value; analyzing cumulative temperature frequency distribution greater than a preset temperature value in the cumulative frequency mode, screening out a corresponding temperature value when the cumulative temperature frequency is less than X, and recording the temperaturevalue as a high-temperature threshold value; and a probe inspection step: carrying out statistical analysis on the data of a single probe. By using the algorithm, an effective utilization rate of battery data can be improved, and batteries with an abnormal temperature can be quickly identified and positioned.
本发明属于汽车电池检测技术领域,尤其涉及一种基于概率突变法则的温度异常探针的识别算法,包括:数据获取步骤,以车辆为单元,获取探针的温度数据;分析筛选步骤,对温度数据进行统计分析;用累计频率的方式分析小于预设温度值的累计温度频率分布,筛选出累计温度频率小于X时对应的温度值,并将其记录为低温阈值;用累计频率的方式分析大于预设温度值的累计温度频率分布,筛选出累计温度频率小于X时对应的温度值,并将其记录为高温阈值;探针检验步骤,对单个探针的数据进行统计分析。使用本算法,能够提高电池数据的有效利用率,快速对温度异常的电池进行识别及定位。
Temperature anomaly probe identification algorithm based on probability mutation rule
一种基于概率突变法则的温度异常探针的识别算法
2020-10-30
Patent
Elektronische Ressource
Chinesisch
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