杭州地区低速电动车电池健康度评价模型数据

随着城市化进程的加快，低速电动车保有量急剧增加，电池作为低速电动车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。通过建立低速电动车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：通过低速电动车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，满足电动车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。通过电池低速电动车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，助力低速电动车系统的健康发展。一、数据采集：原始数据来自公司业务采集数据，包含电池id、所属地区、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、最低温度等原始数据字段。 二、算法规则：通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）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。With the accelerated pace of urbanization, the stock of low-speed electric vehicles (LSEVs) has increased dramatically. As a critical component of LSEV systems, the assessment and management of battery state of health (SOH) have become particularly crucial. Establishing a battery SOH evaluation model for LSEVs enables clear insight into the battery health status across different regions, thereby formulating tailored strategies to improve the operational efficiency of electric vehicle battery systems. 1. Enhancing the scientificity of battery management and maintenance: The LSEV battery SOH evaluation model can quickly identify batteries with high SOH and those requiring support. Batteries with high SOH can maintain their current usage strategies to meet operational needs and extend their service life; for batteries requiring support, usage strategies can be appropriately adjusted, maintenance costs reduced, and the temperature management system optimized to enhance differentiated competitive advantages. 2. Increasing management revenue from low-SOH batteries: By analyzing the proportion of batteries with low SOH, measures can be taken for batteries that trigger early warnings, such as recommending regular maintenance and offering preferential replacement plans, to attract users to engage in battery management and increase management revenue. Leveraging the LSEV battery SOH evaluation model not only optimizes battery resource allocation but also improves overall operational efficiency, supporting the healthy development of LSEV systems. I. Data Collection: The raw data comes from the company's business collection dataset, including raw data fields such as battery ID, affiliated region, reporting time, voltage, current, state of charge (SOC), state of health (SOH), maximum cell voltage, minimum cell voltage, maximum temperature, and minimum temperature. II. Algorithm Rules: By establishing a battery SOH evaluation system for LSEVs, early warnings will be issued when any early warning condition is satisfied. 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) 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.

搜集汇总

数据集介绍

特点

该数据集提供了杭州地区低速电动车电池的健康度评价数据，包含501条记录，每季度更新一次。数据涵盖了电池的电压、电流、SOC、SOH等关键指标，以及健康度评分和预警信息，适用于电池健康度评价模型的建立与优化。