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Datasets:
bsebench-org
/
yao-tu-berlin-2024-raw

Tasks:
Time Series Forecasting
Tabular Regression
Languages:
English
Size:
1B<n<10B
Tags:
battery
state-of-charge
state-of-health
lithium-ion
NMC
LG-INR21700
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 5, saw 16

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 246, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4196, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 5, saw 16

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yao-tu-berlin-2024-raw

Tier 1 raw mirror of the Lithium-Ion Battery Drive Cycle Dataset by Jiaqi Yao (TU Berlin, 2024), hosted under the BSEBench organization.

This dataset addresses a major gap identified in the BSEBench V1.0 selection survey : authentic dynamic-driving telemetry on modern NMC cells with multi-temperature coverage at 10 Hz sampling. The 12 drive cycles span automotive (LA92, US06, FTP-72/75, HWFET) AND heavy-duty (HHDDT, OCTBC, PDTCB, PDMHC, CSHVC) profiles, providing exceptional regime diversity for filter robustness testing.

Status

🚧 Upload pending — placeholder card. The structured dataset (5.1 GB, 140 files in 0_raw + 1_split + 2_preprocessed layout) will be uploaded preserving the original directory hierarchy.

What this is

Three brand-new LG INR21700 M50LT lithium-ion battery cells (4.93 Ah nominal NMC) cycled at 5 ambient temperatures (5/15/25/35/45 °C) using 12 different drive cycle profiles. Total 60+ profile-cell-temperature combinations.

Citation (verbatim) :

Yao, Jiaqi (2024). Lithium-Ion Battery Drive Cycle Dataset. Technische Universität Berlin.

Cell specifications

Property Value
Manufacturer LG Chem
Model INR21700 M50LT
Form factor 21700 cylindrical
Cathode chemistry NMC
Nominal capacity 4.93 Ah
Number of cells 3 (NMC063, NMC068, NMC069)
Temperatures 5, 15, 25, 35, 45 °C
Sampling rate 10 Hz during dynamic cycles, 1 Hz during relaxation

Cycle assignment

Cell Drive cycles
NMC063 BCDC, LA92, HHDDT, IM
NMC068 CSHVC, FTP-72, FTP-75, HWFET
NMC069 US06, PDTCB, PDMHC, OCTBC

Test plan (per cell × temperature)

  1. Relaxation 1 min (1 Hz)
  2. CC charge 1C until 4.2 V (10 Hz)
  3. CV charge 4.2 V until I < 0.02C (10 Hz)
  4. Relaxation 2 h (1 Hz)
  5. Repeated drive cycle until V < 2.5 V (10 Hz)
  6. Repeat steps 2-5 for all 4 assigned cycles

Regenerative power above 90% SOC is trimmed to prevent overcharge. At 5 °C, regen power is multiplied by 0.7 to simulate real-world cold behavior.

Folder structure (preserved in Tier 1 mirror) 

0_raw/             Raw exports from BaSyTec CTS cycler (capacity tests + full procedures)
1_split/           Drive cycle data split into individual profiles with reference SOC
2_preprocessed/    Preprocessed at strict 10 Hz (plug-and-play)
Readme.md          Original Yao README

The 2_preprocessed folder contains 12 CSVs per temperature (60 total), each with columns : Time, Voltage, Current, Temperature, SOC (Coulomb-counted reference)

Why "raw mirror" tier

BSEBench dual-tier strategy :

  • Tier 1 (this repository) — original 5.1 GB structured archive preserved, SHA-256 verified
  • Tier 2 — see bsebench-org/yao-tu-berlin-2024 for harmonized BSEBench-canonical Parquet (V1.1)

Original source

Distributed by Jiaqi Yao at TU Berlin (jiaqi.yao97@gmail.com / jiaqi.yao@tu-berlin.de). License : CC-BY-4.0 with citation requirement.

License

Creative Commons Attribution 4.0 International (CC-BY-4.0) — citation requested.

How to use 

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    "bsebench-org/yao-tu-berlin-2024-raw",
    repo_type="dataset",
)
# Use the 2_preprocessed folder for plug-and-play 10 Hz data

Citation 

@dataset{yao2024drivecycle,
  author       = {Yao, Jiaqi},
  title        = {{Lithium-Ion Battery Drive Cycle Dataset}},
  year         = {2024},
  publisher    = {Technische Universit{\"a}t Berlin},
  note         = {3 LG INR21700 M50LT NMC cells, 5 temperatures, 12 drive cycles, 10 Hz}
}

@misc{bsebench2026,
  author = {Akir, Oussama and BSEBench Contributors},
  title  = {{BSEBench}: an open-source benchmark for battery state-estimation filters},
  year   = {2026},
  url    = {https://bsebench.org},
}

Provenance manifest

bsebench-org/bsebench-datasets/manifests/yao_tu_berlin_2024.yaml.
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