电池剩余使用寿命预测数据

基于收集的锂电池充放电数据，包括放电时间(min)、温度波动(°C)、SOH(%)、容量(Ah)等关键指标，我们进行了全面的统计分析。通过这些数据，计算并深入研究了容量衰减率(%)和预测RUL(循环)等重要参数，为锂电池的健康管理和剩余使用寿命预测提供了科学依据。电池剩余使用寿命预测数据在电动两轮车电池的换电站运营、租赁服务、回收再利用和客户服务等多个领域都具有重要的应用价值。通过持续分析大量电池使用数据，不断提升在电池性能预测、故障诊断、使用优化和产品开发等方面的能力。电池剩余使用寿命预测数据使租赁平台能够为用户创造更大价值，推动行业技术进步，并支持可持续发展。通过电池剩余使用寿命预测数据应用，不仅提升了在锂电池全生命周期管理方面的专业能力，还为整个新能源行业的技术进步和可持续发展做出了积极贡献。1.数据采集：锂电池的运行数据，包括充放电时间(min)、温度波动(°C)、SOH(%)、容量(Ah)、容量衰减率(%)和预测RUL(循环）等。2.数据处理：通过算法计算可知，容量衰减率 (%) = [(初始容量 - 当前容量) / 初始容量] * 100；电池健康评分=0.30*(容量/满容量)+0.30*(SOH/100
)+ 0.20*(100-容量衰减率)/100 + 0.10*(1−∣放电时间−标准时间∣/标准时间)+0.10*(1-|温度波动-1|/10))，其中满容量为30，标准时间为40；预测RUL (循环) = f(电池健康评分, 历史数据)，其中f是基于支持向量机回归的函数。3.数据应用：电池剩余使用寿命预测数据，能够为客户提供更精确的电池寿命估计，优化电池资产管理，并在竞争激烈的新能源市场中保持技术领先优势。通过提高电池使用效率和延长电池寿命，为整个电池行业的可持续发展做出了重要贡献。

Based on the collected lithium-ion battery charge-discharge data including key indicators such as discharge time (min), temperature fluctuation (°C), SOH (%), and capacity (Ah), we conducted a comprehensive statistical analysis. Using this dataset, we calculated and thoroughly investigated critical parameters including capacity decay rate (%) and predicted RUL (cycle count), providing scientific support for lithium-ion battery health management and remaining useful life (RUL) prediction.

The battery RUL prediction data holds significant application value across multiple fields such as battery swapping station operations, rental services, recycling and reuse, and customer service for electric two-wheeler batteries. By continuously analyzing large volumes of battery usage data, we continuously enhance our capabilities in battery performance prediction, fault diagnosis, usage optimization, and product development.

This battery RUL prediction data enables rental platforms to create greater value for users, promote technological advancement in the industry, and support sustainable development. Through the application of battery RUL prediction data, we have not only improved our professional capabilities in the full lifecycle management of lithium-ion batteries, but also made positive contributions to the technological progress and sustainable development of the entire new energy industry.

1. Data Collection: Operational data of lithium-ion batteries includes charge-discharge time (min), temperature fluctuation (°C), SOH (%), capacity (Ah), capacity decay rate (%), and predicted RUL (cycle count).

2. Data Processing: Calculated via algorithms, the relevant formulas are as follows:
- Capacity decay rate (%) = [(Initial capacity - Current capacity) / Initial capacity] * 100;
- Battery health score = 0.30*(Capacity / Full capacity) + 0.30*(SOH / 100) + 0.20*(100 - Capacity decay rate)/100 + 0.10*(1−|Discharge time - Standard time|/Standard time) + 0.10*(1 - |Temperature fluctuation - 1|/10)
where the full capacity is 30 Ah and the standard time is 40 min;
- Predicted RUL (cycle count) = f(Battery health score, Historical data), where f is a function based on support vector machine regression.

3. Data Application: The battery remaining useful life prediction data can provide customers with more accurate battery life estimates, optimize battery asset management, and maintain a technological leading position in the highly competitive new energy market. By improving battery usage efficiency and extending battery lifespan, it has made important contributions to the sustainable development of the entire battery industry.

搜集汇总

数据集介绍

特点

电池剩余使用寿命预测数据集包含507条锂电池充放电数据，关键指标包括放电时间、温度波动、SOH、容量、容量衰减率和预测RUL，应用于锂电池健康管理和剩余使用寿命预测，支持电动两轮车电池的换电站运营、租赁服务、回收再利用和客户服务等多个领域。