循环深度（DOD）对铅酸蓄电池容量的影响分析数据

本数据聚焦于分析循环深度（DOD）对铅酸蓄电池容量的影响，揭示了放电深度与电池寿命衰减之间的定量关系，为公司（作为电池制造商）及外部相关方提供了关键的使用策略优化依据，具有重要的应用价值。具体体现在以下方面：
1.优化电池使用策略设计：公司可通过分析不同DOD对容量的影响，建立科学的放电深度控制方案，在满足使用需求的同时显著延缓电池容量衰减，从而提升电池在储能、备用电源等场景的经济性表现。
2.指导寿命预测模型开发：本数据可为电池管理系统开发商、运维服务商及科研机构提供参考，支持其开展寿命预测算法优化、预防性维护策略制定、循环寿命加速测试方法研究等工作，推动铅酸蓄电池应用向精细化、智能化管理方向发展。​​1.数据采集：
实时记录不同循环深度（DOD）下的铅酸蓄电池容量测试数据，包括测试样品编号、测试时间、循环深度（DOD）/%、电池容量/Ah等字段。
2.数据预处理：
（1）对采集的数据进行去噪处理，确保数据准确性。
（2）将历史采集的数据（包含本次采集）进行聚合，形成数据集X，并针对数据集X中的电池容量字段，计算出其平均值。
3.计算线性回归斜率a和截距b：
（1）基于数据集X（以循环深度（DOD）为自变量、电池容量为因变量），运用SLOPE函数，基于最小二乘法原理确定斜率a，运用INTERCEPT函数确定截距b。
（2）斜率a表示单位循环深度变化对电池容量的影响程度，截距b表示基准循环深度下铅酸蓄电池的容量值。
4.结果运用：
（1）计算比例系数k：k=|a/电池容量平均值|×100%。
（2）若k≥5%，则判定为"高影响"，若2%≤k＜5%，则判定为"中影响"，若k＜2%，则判定为"低影响"。

This dataset focuses on analyzing the impact of Depth of Discharge (DOD) on the capacity of lead-acid batteries, revealing the quantitative relationship between depth of discharge and battery lifespan degradation. It provides a key basis for optimizing usage strategies for the company (as a battery manufacturer) and external stakeholders, holding significant application value, which is reflected in the following aspects:
1. Optimizing battery usage strategy design: The company can analyze the impact of different DOD levels on battery capacity, establish a scientific depth of discharge control scheme, significantly delay battery capacity degradation while meeting usage requirements, thereby improving the economic performance of batteries in scenarios such as energy storage and backup power.
2. Guiding the development of lifespan prediction models: This dataset can provide references for battery management system (BMS) developers, operation and maintenance service providers, and research institutions, supporting them to carry out work such as optimizing lifespan prediction algorithms, formulating preventive maintenance strategies, and researching cyclic lifespan accelerated test methods, promoting the application of lead-acid batteries towards refined and intelligent management.

1. Data Collection:
Real-time recorded test data of lead-acid battery capacity under different depths of discharge (DOD), including fields such as test sample ID, test time, depth of discharge (DOD)/%, and battery capacity / Ah.
2. Data Preprocessing:
(1) Perform denoising processing on the collected data to ensure data accuracy.
(2) Aggregate the historically collected data (including this collection) to form dataset X, and calculate the average value of the battery capacity field in dataset X.
3. Calculation of linear regression slope a and intercept b:
(1) Based on dataset X (with depth of discharge (DOD) as the independent variable and battery capacity as the dependent variable), use the SLOPE function to determine the slope a based on the principle of least squares, and use the INTERCEPT function to determine the intercept b.
(2) The slope a represents the degree of impact of a unit change in depth of discharge on battery capacity, and the intercept b represents the capacity value of lead-acid batteries under the reference depth of discharge.
4. Result Application:
(1) Calculate the proportional coefficient k: k = |a / average battery capacity| × 100%.
(2) If k ≥ 5%, it is categorized as "High Impact"; if 2% ≤ k < 5%, it is categorized as "Medium Impact"; if k < 2%, it is categorized as "Low Impact".