拱墅区车辆电池温度风险分析数据

本分析数据的应用场景是拱墅区车辆电池温度安全风险评估。采集了电池内6个测温点的实时温度监测数据，通过对上述数据进行均值、极差、标准差、最大温升率等描述性统计分析，可获知电池在运行中的关键温度信息，实现对电池内温度异常及潜在风险的实时监控，洞察电池系统的热行为特性、安全状态及老化轨迹，为相关用户或智能系统的电池安全、健康管理提供决策支持。1. 数据来源
采集了拱墅区车辆电池内6个测温点各自的实时温度监测数据。
2. 数据处理
对采集的拱墅区车辆的电池温度实时数据进行描述性统计分析，得到电池各个测量点的温度均值、极差、标准差、最大温升率实时统计结果，根据上述统计结果建立车辆电池温度安全风险监控模型，共分为低风险、中风险、高风险、极高风险四个等级。具体统计指标的意义介绍如下：
均值用于反映电池系统整体热状态；极差用于衡量电池散热均匀性，>5℃存在散热设计缺陷风险；标准差用于量化温度分布的离散程度，>3℃预示热失控风险升高；最大温升率用于捕捉热失控早期信号，>1℃/s为危险阈值。
车辆电池温度安全风险监控模型等级划分依据如下所示：
①极差<=5℃，标准差<=3℃，最大温升率<=1℃/s，全部满足为低风险；
②极差>5℃，标准差>3℃，最大温升率>1℃/s，只满足其中一个为中风险；
③极差>5℃，标准差>3℃，最大温升率>1℃/s，只满足其中两个为高风险；
④极差>5℃，标准差>3℃，最大温升率>1℃/s，全部满足为极高风险。
3. 数据应用
通过拱墅区车辆电池温度实时统计分析结果建立的车辆电池温度安全风险监控模型，能够实现对电池内温度异常及潜在风险的实时监控，洞察电池系统的热行为特性、安全状态及老化轨迹，为相关用户或智能系统的电池安全、健康管理提供决策支持。

This analytical dataset is applied for temperature safety risk assessment of vehicle batteries in Gongshu District. Real-time temperature monitoring data from six temperature measurement points inside the batteries were collected. Through descriptive statistical analysis including mean, range, standard deviation and maximum temperature rise rate on the aforementioned data, key temperature information of the battery during operation can be obtained, enabling real-time monitoring of temperature anomalies and potential risks inside the battery, gaining insights into the thermal behavior characteristics, safety status and aging trajectory of the battery system, and providing decision support for battery safety and health management of relevant users or intelligent systems.

1. Data Source
Real-time temperature monitoring data from six individual temperature measurement points within the vehicle batteries in Gongshu District were collected.

2. Data Processing
Descriptive statistical analysis was conducted on the collected real-time battery temperature data of vehicles in Gongshu District, to obtain real-time statistical results of temperature mean, range, standard deviation and maximum temperature rise rate for each measurement point of the battery. A vehicle battery temperature safety risk monitoring model was established based on the above statistical results, which is divided into four risk levels: low risk, medium risk, high risk and extremely high risk.

Specific meanings of the statistical indicators are introduced as follows:
The mean value is used to reflect the overall thermal state of the battery system; the range is used to measure the heat dissipation uniformity of the battery, with a risk of heat dissipation design defects when the range exceeds 5℃; the standard deviation is used to quantify the dispersion degree of temperature distribution, with an increased risk of thermal runaway when the standard deviation exceeds 3℃; the maximum temperature rise rate is used to capture early signals of thermal runaway, with a dangerous threshold of >1℃/s.

The grading criteria of the vehicle battery temperature safety risk monitoring model are as follows:
① Low risk: All of the following conditions are met: range ≤5℃, standard deviation ≤3℃, maximum temperature rise rate ≤1℃/s;
② Medium risk: Only one of the following conditions is met: range >5℃, standard deviation >3℃, maximum temperature rise rate >1℃/s;
③ High risk: Only two of the following conditions are met: range >5℃, standard deviation >3℃, maximum temperature rise rate >1℃/s;
④ Extremely high risk: All of the following conditions are met: range >5℃, standard deviation >3℃, maximum temperature rise rate >1℃/s.

3. Data Application
The vehicle battery temperature safety risk monitoring model established based on the real-time statistical analysis results of vehicle battery temperatures in Gongshu District can realize real-time monitoring of temperature anomalies and potential risks inside the battery, gain insights into the thermal behavior characteristics, safety status and aging trajectory of the battery system, and provide decision support for battery safety and health management of relevant users or intelligent systems.

搜集汇总

背景与挑战

背景概述

该数据集包含9651条拱墅区车辆电池温度监测数据，通过分析6个测温点的实时温度，计算均值、极差、标准差和最大温升率等指标，构建风险监控模型以评估电池安全风险等级，应用于电池温度异常实时监控和健康管理决策支持。