Li-ion Battery Aging Datasets

This data set has been collected from a custom built battery prognostics testbed at the NASA Ames Prognostics Center of Excellence (PCoE). Li-ion batteries were run through 3 different operational profiles (charge, discharge and Electrochemical Impedance Spectroscopy) at different temperatures. Discharges were carried out at different current load levels until the battery voltage fell to preset voltage thresholds. Some of these thresholds were lower than that recommended by the OEM (2.7 V) in order to induce deep discharge aging effects. Repeated charge and discharge cycles result in accelerated aging of the batteries. The experiments were stopped when the batteries reached the end-of-life (EOL) criteria of 30% fade in rated capacity (from 2 Ah to 1.4 Ah).

**Data Acquisition:**

The testbed comprises:

* Commercially available Li-ion 18650 sized rechargeable batteries,
* Programmable 4-channel DC electronic load,
* Programmable 4-channel DC power supply,
* Voltmeter, ammeter and thermocouple sensor suite,
* Custom EIS equipment,
* Environmental chamber to impose various operational conditions,
* PXI chassis based DAQ and experiment control, and

MATLAB based experiment control, data acquisition and prognostics algorithm evaluation setup (appx. data acquisition rate is 10Hz).

**Parameter Description:**

Data Structure:

cycle: top level structure array containing the charge, discharge and impedance operations

type: operation type, can be charge, discharge or impedance

ambient_temperature: ambient temperature (degree C)

time: the date and time of the start of the cycle, in MATLAB date vector format

data: data structure containing the measurements

for charge the fields are:

Voltage_measured: Battery terminal voltage (Volts)

Current_measured: Battery output current (Amps)

Temperature_measured: Battery temperature (degree C)

Current_charge: Current measured at charger (Amps)

Voltage_charge: Voltage measured at charger (Volts)

Time: Time vector for the cycle (secs)

for discharge the fields are:

Voltage_measured: Battery terminal voltage (Volts)

Current_measured: Battery output current (Amps)

Temperature_measured: Battery temperature (degree C)

Current_charge: Current measured at load (Amps)

Voltage_charge: Voltage measured at load (Volts)

Time: Time vector for the cycle (secs)

Capacity: Battery capacity (Ahr) for discharge till 2.7V

for impedance the fields are:

Sense_current: Current in sense branch (Amps)

Battery_current: Current in battery branch (Amps)

Current_ratio: Ratio of the above currents

Battery_impedance: Battery impedance (Ohms) computed from raw data

Rectified_impedance: Calibrated and smoothed battery impedance (Ohms)

Re: Estimated electrolyte resistance (Ohms)

Rct: Estimated charge transfer resistance (Ohms)

**Intended Use:**

The data sets can serve for a variety of purposes. Because these are essentially a large number of Run-to-Failure time series, the data can be set for development of prognostic algorithms. In particular, due to the differences in depth-of-discharge (DOD), the duration of rest periods and intrinsic variability, no two cells have the same state-of-life (SOL) at the same cycle index. The aim is to be able to manage this uncertainty, which is representative of actual usage, and make reliable predictions of Remaining Useful Life (RUL) in both the End-of-Discharge (EOD) and End-of-Life (EOL) contexts.

本数据集源于美国国家航空航天局艾姆斯预测性维护卓越中心（PCoE）的自建电池预测性测试平台。锂离子电池在不同温度下经历了3种不同的操作模式（充电、放电及电化学阻抗谱）的测试。放电过程在多种电流负载水平下进行，直至电池电压降至预设的电压阈值。其中部分阈值低于原始设备制造商（OEM）推荐值（2.7V），旨在引发深度放电老化效应。重复的充放电循环加速了电池的老化。实验在电池达到30%额定容量衰减（从2Ah降至1.4Ah）的寿命终结（EOL）标准时终止。

**数据采集：**

测试平台包括以下设备：
* 商业可用的锂离子18650尺寸可充电电池，
* 可编程4通道直流电子负载，
* 可编程4通道直流电源，
* 电压表、电流表和热电偶传感器套件，
* 定制的电化学阻抗谱设备，
* 用于施加各种操作条件的环境室，
* 基于 PXI 机箱的数据采集和实验控制系统，以及

基于 MATLAB 的实验控制、数据采集和预测性算法评估设置（数据采集率约为10Hz）。

**参数描述：**

数据结构：
* cycle：包含充电、放电和阻抗操作的顶层结构数组，
* type：操作类型，可以是充电、放电或阻抗，
* ambient_temperature：环境温度（摄氏度），
* time：循环开始的日期和时间，以 MATLAB 日期向量格式表示，
* data：包含测量的数据结构，具体如下：
* 充电：
* Voltage_measured：电池端电压（伏特），
* Current_measured：电池输出电流（安培），
* Temperature_measured：电池温度（摄氏度），
* Current_charge：充电器测量的电流（安培），
* Voltage_charge：充电器测量的电压（伏特），
* Time：循环的时间向量（秒），
* 放电：
* Voltage_measured：电池端电压（伏特），
* Current_measured：电池输出电流（安培），
* Temperature_measured：电池温度（摄氏度），
* Current_charge：负载测量的电流（安培），
* Voltage_charge：负载测量的电压（伏特），
* Time：循环的时间向量（秒），
* Capacity：从2.7V放电至放电容量的电池容量（安时），
* 阻抗：
* Sense_current：感应支路中的电流（安培），
* Battery_current：电池支路中的电流（安培），
* Current_ratio：上述电流的比率，
* Battery_impedance：从原始数据计算出的电池阻抗（欧姆），
* Rectified_impedance：校准并平滑后的电池阻抗（欧姆），
* Re：估计的电解液电阻（欧姆），
* Rct：估计的电荷转移电阻（欧姆）。

**预期用途：**

数据集可用于多种目的。由于这些数据集本质上是一系列运行至故障的时间序列，因此可用于预测性算法的开发。特别是，由于放电深度（DOD）、休息时间长度和内在变异性不同，同一循环索引下没有两个电池具有相同的寿命状态（SOL）。目标是管理这种不确定性，它代表了实际使用的典型情况，并在放电结束（EOD）和寿命终结（EOL）情境下可靠地预测剩余使用寿命（RUL）。