广州区域电动载具电池健康智能监管数据

随着城市化进程的加快，低速电动载具保有量急剧增加，电池作为电动载具系统的重要组成部分，其健康度的评估与管理显得尤为关键。通过建立电动载具电池健康度评价模型，清晰了解各地区电池的健康状况，从而制定不同的策略，提升电动车电池系统的运行效率。一、提升电池管理与维护的科学性：通过电动载具电池健康度评价模型，可以快速识别出高健康度和需要支持的电池。高健康度的电池可以继续保持现有使用策略，满足电动载具的运行需求并延长使用寿命；需要支持的电池则可以适当调整使用策略、压降维护成本，并优化温度管理系统以提升差异化竞争优势。二、提升健康度较低电池的管理收入：通过分析电池低健康度占比，可以对触发预警的电池采取措施，如推荐定期维护、提供优惠更换计划等策略，吸引用户进行电池管理，提升管理收入。通过电池电动载具电池健康度评价模型，不仅优化了电池的资源配置，还提升了整体运营效率，助力电动载具系统的健康发展。一、算法规则： 1、数据采集：原始数据来自公司业务采集数据，包含电池id、上报时间、电压、电流、SOC、SOH、电芯最高电压、电芯最低电压、最高温度、最低温度、所属地区等原始数据字段。 2、算法规则：通过建立低速电动车电池健康度评价体系，并在满足任一预警条件时发出预警。 ①、soh=SOH（%）/100 ②、电压稳定性=1-（（电芯最高电压-电芯最低电压）/（电压/20）） ③、温度稳定性=1-（（最高温度-最低温度）/理想工作温度范围） ④、SOC利用率=1-|0.5-SOC/100| ⑤、电流负荷率=|实际电流|/额定电流 ⑥、电压偏差=|电压-额定电压|/额定电压 ⑦、综合健康度评分=（w1*soh+w2*电压稳定性+w3*温度稳定性+w4*（1-|0.5-SOC/100|）+w5*（1-电流负荷率）+w6*（1-电压偏差））*地区影响因子 二、预警规则：对每个电池记录进行检查，综合健康度评分及各单项指标，设定预警规则，满足以下任一条件时发出预警： （1）健康度评分预警：如果“健康度评分”低于“健康度评分”的均值减去标准差，添加“低健康度评分”预警。 （2）SOH预警：如果 SOH(%) 低于85%，添加“低SOH”预警。 （3）SOC预警：如果 SOC(%) 低于30%，添加“低SOC”预警。 （4）温度预警：如果 最高温度(°C) 超过35°C 或 最低温度(°C) 低于15°C，添加“温度异常”预警。 （5）电流预警：如果电流的绝对值超过25A，添加“高电流”预警。 （6）额定电流：20A （7）额定电压：51V （8）理想温度范围：10℃ （9）权重系数：[0.3,0.1,0.1,0.2,0.15,0.15] （10）地区影响因子：广州：1.01 （11）健康度评分均值：89.18793692 健康度评分标准差：6.189532261With the acceleration of urbanization, the ownership of low-speed electric vehicles has increased sharply. As a core component of electric vehicle systems, batteries are crucial for health assessment and management. Establishing a battery health evaluation model for electric vehicles helps clearly grasp the battery health status across different regions, formulate targeted strategies, and improve the operational efficiency of electric vehicle battery systems. 1. Enhancing the Scientificity of Battery Management and Maintenance: The battery health evaluation model for electric vehicles can quickly identify batteries with high health status and those requiring support. Batteries with high health status can maintain their current usage strategy to meet the operational needs of electric vehicles and extend their service life; for batteries requiring support, usage strategies can be appropriately adjusted, maintenance costs reduced, and temperature management systems optimized to gain differentiated competitive advantages. 2. Increasing Management Revenue from Low-Health Batteries: By analyzing the proportion of batteries with low health status, measures can be taken for batteries that trigger early warnings, such as recommending regular maintenance and providing preferential replacement plans, to attract users to adopt battery management services and increase management revenue. Through the battery health evaluation model for electric vehicles, we can not only optimize battery resource allocation but also improve overall operational efficiency, contributing to the healthy development of electric vehicle systems. I. Algorithm Rules: 1. Data Collection: The original data is sourced from the company's business collection, including original data fields such as battery ID, reporting time, voltage, current, SOC, SOH, maximum cell voltage, minimum cell voltage, maximum temperature, minimum temperature, and affiliated region. 2. Algorithm Specification: Establish an evaluation system for the health status of low-speed electric vehicle batteries, and issue early warnings when any early warning condition is met. ① soh = SOH (%) / 100 ② Voltage Stability = 1 - ((Maximum Cell Voltage - Minimum Cell Voltage) / (Voltage / 20)) ③ Temperature Stability = 1 - ((Maximum Temperature - Minimum Temperature) / Ideal Operating Temperature Range) ④ SOC Utilization Rate = 1 - |0.5 - SOC / 100| ⑤ Current Load Rate = |Actual Current| / Rated Current ⑥ Voltage Deviation = |Voltage - Rated Voltage| / Rated Voltage ⑦ Comprehensive Health Score = (w1*soh + w2*Voltage Stability + w3*Temperature Stability + w4*(1 - |0.5 - SOC / 100|) + w5*(1 - Current Load Rate) + w6*(1 - Voltage Deviation)) * Regional Impact Factor II. Early Warning Rules: Check each battery record, set early warning rules based on the comprehensive health score and each individual indicator, and issue early warnings when any of the following conditions are met: (1) Health Score Warning: If the "Health Score" is lower than the mean of the "Health Score" minus the standard deviation, add a "Low Health Score" warning. (2) SOH Warning: If SOH (%) is lower than 85%, add a "Low SOH" warning. (3) SOC Warning: If SOC (%) is lower than 30%, add a "Low SOC" warning. (4) Temperature Warning: If the maximum temperature (°C) exceeds 35°C or the minimum temperature (°C) is lower than 15°C, add a "Temperature Abnormality" warning. (5) Current Warning: If the absolute value of current exceeds 25A, add a "High Current" warning. (6) Rated Current: 20A (7) Rated Voltage: 51V (8) Ideal Operating Temperature Range: 10°C (9) Weight Coefficients: [0.3, 0.1, 0.1, 0.2, 0.15, 0.15] (10) Regional Impact Factor: Guangzhou: 1.01 (11) Health Score Mean: 89.18793692; Health Score Standard Deviation: 6.189532261

搜集汇总

背景与挑战

背景概述

该数据集包含502条广州区域电动载具电池的健康监测数据，每日更新，涵盖电压、电流、SOC、SOH等多项指标，用于评估电池健康状况并制定管理策略。数据集通过特定算法计算健康度评分和预警规则，支持电动载具电池的科学管理和维护。