Feature Engineered Dataset from HPPC Files for Lithium-Ion Battery Cells

This dataset contains time-series data from Hybrid Pulse Power Cycle (HPPC) tests of lithium-ion battery cells, specifically designed for evaluating cell performance and degradation over multiple cycles. The dataset originally published by (Popp et al., 2024) includes information from both charge and discharge tests for each cell, with additional engineered features derived from the raw data. The cells in this dataset were evaluated under different States of Health (SoH), providing insights into performance across various degradation levels.

Dataset Overview

The dataset comprises charge and discharge cycles from 256 individual cells. Each test involves the following procedures:

1. Cells were equilibrated at 25 °C inside a thermal chamber to ensure a stable temperature before testing.
2. Cells were charged using constant current/constant voltage (CC/CV) mode with a charging rate of C/2 and a cutoff current of 98 mA.
3. After a resting period to allow thermal stabilization, the cells were discharged at a rate of C/5 until the voltage dropped to 2.5 V, capturing the full discharge capacity.
4. A second resting period was conducted until the cells reached thermal equilibrium.
5. HPPC cycles were performed at multiple stages of the charge and discharge cycles to assess cell performance at different State of Charge (SOC) levels.

The dataset also includes a micro HPPC cycle performed at every 10% SOC decrement, starting from 100% SOC down to 10% SOC, with a final HPPC test at 0% SOC. Cells were tested using a combination of charge and discharge pulses to simulate real-world usage patterns. All cycles were recorded at 100 Hz for standard charge/discharge cycles and 1 kHz for the HPPC tests, allowing for high-resolution analysis of the cell behavior.

Data Features

The dataset includes the following features recorded by (Popp. et al., 2024) during the tests:

• Time: Timestamp for each data point.
• ClimaTemp: Temperature recorded from the climate chamber.
• I: Current applied to the cell (in Amps).
• Itarget: Setpoint for the applied current (in Amps).
• P: Power output of the cell (in Watts).
• Q: Total charge accumulated in the cell (in Amp-seconds).
• Qneg: Negative charge accumulated (in Amp-seconds).
• Qpos: Positive charge accumulated (in Amp-seconds).
• Temp_Cell: Temperature measured at the cell (in °C).
• U: Cell voltage (in Volts).

In addition to these core features, engineered features have been included to facilitate analysis of battery performance and degradation trends:

• Cumulative_Cycles: Running count of cycles completed by the cell.
• Avg_Voltage: The average voltage of the cell up to the current cycle.
• Capacity_Fade_Rate: The rate of capacity degradation over time.
• Avg_Temperature: Average temperature experienced by the cell up to the current cycle.
• Temp_Variation: Maximum difference in cell temperature over time.
• High_Temp_Flag: Binary flag indicating whether the cell temperature exceeded 40°C.
• Internal_Resistance: Internal resistance of the cell, calculated from voltage and current data.
• Power_Consumption_Rate: Rate at which power is consumed by the cell.
• Energy_Efficiency: Efficiency of energy storage, calculated from the ratio of positive to negative charge.
• Rolling_Avg_Voltage: Rolling average of the cell voltage over recent cycles.
• Std_Dev_Voltage: Standard deviation of cell voltage across cycles.
• Max_Voltage: Maximum voltage observed during the test.
• Min_Voltage: Minimum voltage observed during the test.
• Dynamic_Resistance: Resistance change calculated from voltage and current differentials.
• Impedance: Impedance calculated from voltage and current changes.
• Temp_Coefficient: Rate of change in power with respect to temperature.
• Thermal_Runaway_Risk: Flag indicating the potential for thermal runaway conditions.
• Effective_Capacity: Net charge retained by the cell after each cycle.
• Energy_Throughput: Total energy delivered by the cell over time, calculated from power and time.

This dataset is suitable for research into lithium-ion battery degradation, performance modeling, and State of Health prediction. The engineered features provide a robust foundation for predictive modeling, including the estimation of Remaining Useful Life (RUL) and optimization of battery usage in practical applications.

The dataset has been structured for easy integration with machine learning workflows, with all features provided in CSV format for each test cycle.

Citation:

Popp, A., Spaeth, U., & Schmuelling, B. (2024). Samsung INR21700-50E Capacity and HPPC tests (V1.0) [Data set]. 2024 IEEE Transportation Electrification Conference and Expo (ITEC), Rosemont, IL, USA. Zenodo. https://doi.org/10.5281/zenodo.10891871