The SiCWell Dataset contains data of battery electric vehicle lithium-ion batteries for modeling and diagnosis purposes. In this experiment, automotive-grade lithium-ion pouch bag cells are cycled with current profiles plausible for electric vehicles. 

The analysis of current ripples in electric vehicles and the corresponding aging experiments of the battery cells result in a dataset, which is composed of the following parts: 



Cell Aging Scenarios

The battery cells are cycled in groups of three cells in series. The scenarios for each cell are the following:

  • Ka01, Ka02: Calendar test 35°C 80% SoC
  • Ka03, Ka04: Calendar test 35°C 45% SoC
  • Ka05, Ka06: Calendar test 45°C 80% SoC
  • Ka07, Ka08: Calendar test 45°C 20% SoC
  • Ka09, Ka10: Calendar test 45°C 45% SoC
  • Ka11, Ka12: Calendar test 45°C 60% SoC
  • DC01, DC02, DC03: DC cycling
  • AC01, AC02, AC03: Sinusoidal cycling 10 kHz, 12.5 A
  • AC04, AC05, AC06: Sinusoidal cycling 10 kHz, 25.0 A
  • AC07, AC08, AC09: Sinusoidal cycling 10 kHz, 6.25 A
  • AC10, AC11, AC12: Sinusoidal cycling 40 kHz, 12.5 A
  • AC13, AC14, AC15: Sinusoidal cycling 20 kHz, 12.5 A
  • AC16, AC17, AC18: Sinusoidal cycling 40 kHz, 6.25 A
  • AC19, AC20, AC21: Artificial ripple cycling
  • AC22, AC23, AC24: Realistic ripple cycling
  • AC25, AC26, AC27: Realistic ripple cycling

Current Ripple Evaluation

The evaluation results of current ripples in a battery-electric vehicle are stored in the “current_ripple_evaluation” directory. It contains the following files:

  • input_sWLTP.csv/input_UDDS.csv: The speed, torque, and power of the sWLTP and UDDS cycles at every second of the simulated battery-electric vehicle.
  • cycler_sWLTP.csv/cycler_UDDS.csv: The compressed current values for every second of the sWLTP and UDDS cycles. More details about the compression can be found at [1].
  • parameters.csv: The parameters for simulation of the battery-electric vehicle and the drivetrain.
  • sWLTP.h5/UDDS.h5: The simulated current and voltage of the battery-electric vehicle in the time-domain sampled with 500 kHz. Every second has its operating point, which is simulated for a second.


Cell Cycles

The raw current, voltage, and temperature measurements of the cycled battery cells. The results are stored in the “cell_cycling_*” directory with the following files:


  • cell_cycling_sinusoidal/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the sinusoidal cycling experiments.
  • cell_cycling_artificial_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the artifical ripple cycling experiments.
  • cell_cycling_realistic_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the realistic ripple cycling experiments.

The measurements have been taken periodically by an external 2MHz measurement system. Each .hdf5 file contains the measurements of a specific cycle number of the experiment. In the sinusoidal and artificial ripple tests, a measurement has been taken for every 1 % State-of-Charge. In the realistic ripple tests, a measurement has been taken every 5 seconds. Every measurement has a duration of 100 ms and a sampling rate of 2 MS/s.

Each measurement is a group in the hdf5 file, with the voltage and current as 1d 32bit floating-point arrays. Each measurement also has a Unix UTC timestamp of the time of the measurement, the cell temperature, capacity, and resistance[10s] stored as attributes. The capacity and resistance are synchronized with the checkups and interpolated linearly over the number of cycles.


Cell Checkups

The periodic checkups of the battery cells are composed of capacity, internal resistance, EIS, OCV, and qOCV measurements. Measurements that take longer, such as EIS and OCV, are not taken at every checkup. The results are stored in the “cell_checkups” directory with the following files:


  • Overview.csv: List of every checkup of every battery cell. For every checkup, the date, number of cycles, capacity, 10s resistance, and references to the more detailed checkup files are stored.
  • EIS/[cell id]_CheckUp[checkup id]_[date]_EIS.csv: The results of the electrochemical impedance spectroscopy from 0.001 to 50,000 Hz using an EIS-meter.
  • OCV/[cell id]_CheckUp[checkup id]_[date]_OCV.csv: Results of the open-circuit voltage measurement between 0 and 100 % State of Charge in 5 % steps.
  • qOCV/[cell id]_CheckUp[checkup id]_[date]_qOCV.csv: Results of the quasi-open-circuit voltage measurement between 0 and 100 % State of Charge in 1 % steps. It contains the quasi-open-circuit voltage, 1s resistance, and 10s resistance.
  • OCV_raw/[cell id]_CheckUp[checkup id]_[date]_OCV_raw.csv: Raw current, voltage, and temperature values of the OCV measurement.
  • qOCV_raw/[cell id]_CheckUp[checkup id]_[date]_qOCV_raw.csv: Raw current, voltage, and temperature values of the qOCV measurement.
  • Capacity_raw/[cell id]_CheckUp[checkup id]_[date]_Cap_raw.csv: Raw current, voltage, and temperature values of the capacity measurement.

The attached txt file “cir_bis.txt” is the netlist of the SPICE subcircuit of an EMI filter for dc or single-phase ac applications, valid up to 100 MHz. It can be used in a SPICE-compatible solver (e.g., LTSPICE) for frequency-domain or time-domain simulations. It is a passive black-box macromodel obtained as described in the following paper: S. Negri, G. Spadacini, F. Grassi, and S. A.


This document provides the data for the case studies of the work “Priority Chronological Time-Period Clustering for Generation and Transmission Expansion Planning Problems with Long-Term Dynamics”.



The dataset contains temperature measurements taken with an 8x8 infrared array (Panasonic Grid-EYE) over a period of three weeks during 2018 in Bucharest, Romania, in an educational facility.


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The dataset is supplementary material for the research article 'Techno-economic assessment of grid-level battery energy storage supporting distributed photovoltaic power' published in IEEE Access in October 2021. The dataset corresponds to the annual timeseries at 1-minute resolution (525,600 steps) of the per-unit profiles used for the electric load and the per-unit power output of 8 PV systems.


Clean energy resources, like wind, have a stochastic nature, which involves uncertainties in the power system. Introducing energy storage systems (ESS) to the network can compensate for the uncertainty in wind plant output and allow the plant to participate in ancillary service markets. Advance in compressed air energy storage system (CAES) technologies and their fast response make them suitable for ancillary services.


The attached file include the data used in the case study of paper "An Adjustable Robust Optimization Approach for the Expansion Planning of a Virtual Power Plant".


There is an industry gap for publicly available electric utility infrastructure imagery.  The Electric Power Research Institute (EPRI) is filling this gap to support public and private sector AI innovation.  This dataset consists of ~30,000 images of overhead Distribution infrastructure.  These images have been anonymized, reviewed, and .exif image-data scrubbed.  These images are unlabeled and do not contain annotations.  EPRI intends to label these data to support its own research activities.  As these labels are created, EPRI will periodically update this dataset with those data.


These images are not labeled or annotated.  However, as these images are labeled, EPRI will update this dataset periodically.  If you have annotations you'd like to contribute, please send them, with a description of your labeling approach, to


Also, if you see anything in the imagery that looks concerning, please send the image and image number