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liming-chang/LiBMap-46800-NCM811-Graphite

LiBMap-46800-NCM811-Graphite A Design–Performance Dataset for Cylindrical NCM811/Graphite Lithium-Ion Cells Developed jointly by Tsinghua University and Electroder Ltd. For dataset enquiries: Chang Liming   Email: clm24@mails.tsinghua.edu.cn Contents Overview — scope, provenance, and the present public subset Design Space — five varied porous-electrode design parameters Performance Simulations — protocols and termination conditions Data Tables and… See the full description on the dataset page: https://huggingface.co/datasets/liming-chang/LiBMap-46800-NCM811-Graphite.

sourceHugging Facecc-by-nc-4.0updated 25d agoView on Hugging Face
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<h1 align="center">LiBMap-46800-NCM811-Graphite</h1>

<p align="center"><strong>A Design–Performance Dataset for Cylindrical NCM811/Graphite Lithium-Ion Cells</strong></p>

<p align="center"> Developed jointly by Tsinghua University and Electroder Ltd.<br> For dataset enquiries: Chang Liming&nbsp;&nbsp;&nbsp;Email: <a href="mailto:clm24@mails.tsinghua.edu.cn">clm24@mails.tsinghua.edu.cn</a> </p>

Contents

  1. 1.[Overview](#overview) — scope, provenance, and the present public subset
  2. 2.[Design Space](#design-space) — five varied porous-electrode design parameters
  3. 3.[Performance Simulations](#performance-simulations) — protocols and termination conditions
  4. 4.[Data Tables and Relationships](#data-tables-and-relationships) — schemas, cardinalities, and join keys
  5. 5.[Metric and Scene Catalogue](#complete-metric-and-scene-catalogue) — all 28 metrics and exact scene identifiers
  6. 6.[Format and Loading](#format-and-loading) — Parquet layout and loading examples
  7. 7.[Licence](#licence) — CC BY-NC 4.0 terms

Overview

To address the scarcity of high-quality design–performance data for lithium-ion batteries, we developed what is, to our knowledge, the first large-scale, high-quality dataset designed for battery engineering. The dataset integrates cell format, geometry, material system, kinetic parameters, and porous-electrode design parameters within a unified design space. It also establishes a systematic performance framework based on representative traction-battery and energy-storage applications. Data were generated through material-balance calculations and electrothermal performance simulations on the ElectroderDIGITAL® platform. Large-scale parallel computing produced performance data for approximately 770,000 cell designs.

Each design record contains more than 100 design-related fields describing the cell format, geometry, material formulation, porous electrodes, mechanical components, and thermodynamic and kinetic properties. For the complete LiBMap database, performance was characterised using eight categories comprising 29 metrics: discharge capacity, discharge energy, peak discharge power, continuous discharge power, recharge power, fast-charge time, direct-current resistance, and energy efficiency. The dataset also retains complete electrothermal response data from the simulations, including electrode potentials, terminal voltage, polarisation decomposition, heat generation, and temperature rise. It therefore provides a lithium-ion battery design–performance resource with comprehensive design information, broad performance coverage, and detailed process data.

The present public release is a subset of the complete database. It covers a cylindrical 46800 cell with a single-crystal NCM811 cathode and a graphite anode. This subset contains seven performance categories and 28 metrics; energy efficiency is not included in the present release. It systematically describes relationships between five porous-electrode design parameters and cell performance, comprising 72,000 complete cell designs and their associated detailed performance data.

Design Space

<div align="center">

Design parameterExact key in `variables`Range or valuesNumber of valuesUnit
Cathode single-sided coating thickness after calenderingcathode_single_face_thickness_post_rolling[30, 65]20μm
Cathode compaction density after calenderingcathode_compaction_density_post_rolling[3.3, 3.7]20g/cm³
Anode compaction density after calenderinganode_compaction_density_post_rolling[1.3, 1.75]20g/cm³
Cathode particle diameter D50cathode_uniform_diameter[3, 8]3μm
Anode particle diameter D50anode_uniform_diameter[8, 16]3μm

</div>

Each parameter was sampled at equally spaced values over its specified range, including both endpoints. The Cartesian product of the five discrete parameter sequences produced the complete design space:

20 × 20 × 20 × 3 × 3 = 72,000 distinct cell designs

Performance Simulations

Capacity and energy. The cell was discharged from full charge at constant current under a specified temperature and C-rate. Discharge continued until the lower voltage cutoff was reached. Capacity and energy were obtained from the same simulation.

Discharge power. Constant-power discharge was simulated at a specified state of charge and ambient temperature. The search identified powers for either 60 s continuous discharge or 10 s peak discharge under the maximum-temperature and lower-voltage-cutoff constraints.

Recharge power. Constant-power charging was simulated for 5 s from 90% SOC. The maximum charging power was searched under the lithium-plating boundary and upper voltage cutoff.

Fast charging. The cell was charged from 20% to 80% SOC through 12 constant-current stages. Each stage covered a 5% SOC interval, and the maximum current that avoided lithium plating was searched independently for each stage.

Direct-current resistance. Direct-current resistance was calculated from the voltage response during a constant-current discharge pulse.

Four termination conditions were applied:

<div align="center">

Termination conditionTrigger
Temperature cutoffCell temperature reaches 80 °C
Upper voltage cutoffTerminal voltage reaches 4.2 V
Lower voltage cutoffTerminal voltage reaches 3.0 V
Lithium-plating cutoffPotential at the separator–anode interface is ≤ 0 V

</div>

Data Tables and Relationships

<div align="center">

TableRowsColumnsDescription
cell_designs72,0005One cell design completed through material-balance calculations
performance_results1,728,0007One requested operating condition and its summary result
simulation_traces1,722,13711Detailed simulation settings and electrical, thermal, and polarisation data

</div>

1. cell_designs: Cell-Design Table

Each row contains one porous-electrode parameter combination and the complete cell design obtained through material-balance calculations.

<div align="center">

FieldDescription
cell_designs.idUnique identifier for the cell design. It links the design to the other two tables.
cell_designs.variablesFive varied design parameters: cathode coating thickness, cathode compaction density, anode compaction density, cathode particle D50, and anode particle D50.
cell_designs.mod_dataInputs and outputs of the material-balance calculation, including material composition, structural dimensions, calculated cell capacity, mass, winding count, electrode thicknesses, porosities, and material quantities.
cell_designs.solver_inputSolver inputs for the design, including geometry, electrode properties, and solver-control parameters.
cell_designs.additionalTermination conditions and other information, including upper and lower voltage limits, the 80 °C temperature cutoff, and material thermal and kinetic parameters.

</div>

cell_designs.mod_data.result.cell.capacity is the reference design capacity in Ah. All C-rates in the dataset are defined relative to this capacity.

2. performance_results: Performance-Result Table

Each row represents one simulation task for a cell design under a specified operating condition. It records the requested metric, completion status, and final performance result.

<div align="center">

FieldDescription
performance_results.idUnique identifier for the performance task. It links the task to its detailed simulation record.
performance_results.sceneOperating-condition identifier that distinguishes the metric category and its temperature, SOC, and C-rate combination. For example, capacity_energy_0001 identifies one shared capacity-and-energy discharge condition.
performance_results.statusTask completion status. A value of 2 indicates that the computational workflow ended normally. A value of -201 indicates a failed fast-charge simulation.
performance_results.additionalRecords the power-search or staged fast-charging process. For capacity, energy, and DCR tasks, the literal \N indicates that no such process record exists. It does not indicate task failure.
performance_results.solver_inputContains three fixed Bruggeman coefficients for the anode, separator, and cathode.
performance_results.resultContains the performance result. Its structure depends on the operating-condition category. A failed calculation is represented by the literal \N.
performance_results.project_idIdentifier of the associated design. It corresponds to cell_designs.id.

</div>

The principal result fields are:

<div align="center">

Performance categoryResult fieldUnit
Discharge capacity and energyperformance_results.result.capacity, performance_results.result.energyAh, Wh
60 s continuous discharge powerperformance_results.result.continuous_discharge_powerW
10 s peak discharge powerperformance_results.result.peak_discharge_powerW
5 s recharge powerperformance_results.result.recharge_powerW
20%–80% SOC fast-charge timeperformance_results.result.fast_charge_timemin
Direct-current resistanceperformance_results.result.dcrmΩ

</div>

The three power-result categories also contain performance_results.result.reached. A value of true means that the duration of the saved simulation reached the target duration. A value of false means that the saved duration was shorter than the target, and the corresponding power is retained as an approximate result. The reached flag differs from performance_results.status: normal workflow completion does not necessarily mean that the target duration was reached.

3. simulation_traces: Detailed Simulation-Trace Table

Each row stores the detailed configuration, process data, and outputs for one completed performance task.

<div align="center">

FieldDescription
simulation_traces.idUnique identifier for the detailed simulation record.
simulation_traces.orderSequence number within the simulation workflow. It is not the row number of the complete table.
simulation_traces.sub_orderSubsequence number within the workflow or search process.
simulation_traces.stepSimulation-category and stage identifier, such as a capacity-and-energy condition or a fast-charge stage.
simulation_traces.conditionInitial SOC, ambient temperature, convective heat-transfer boundary, charge or discharge mode, control value, and stage-termination conditions.
simulation_traces.statusCompletion status of the detailed simulation. All currently retained records have a value of 2. Failed fast-charge tasks have no corresponding trace record.
simulation_traces.resultCurves for current, voltage, electrode potentials, temperature, heat generation, and polarisation decomposition. It also contains high-temperature and error flags.
simulation_traces.result_fieldsNames and ordering of entries in simulation_traces.result. This field identifies the corresponding curves and flags.
simulation_traces.additionalSummary of the simulation result and its high-temperature and error flags. For power tasks, it records the power value and actual simulated duration.
simulation_traces.project_idIdentifier of the associated design. It corresponds to cell_designs.id.
simulation_traces.task_idIdentifier of the associated performance task. It corresponds to performance_results.id.

</div>

4. Relationships Among the Three Tables

<div align="center">

RelationshipJoin fieldsCardinality
Cell design → performance resultcell_designs.id ↔ performance_results.project_idEvery cell design corresponds to exactly 24 performance tasks.
Performance result → simulation traceperformance_results.id ↔ simulation_traces.task_idEach completed task corresponds to one detailed record. Failed fast-charge tasks have no detailed record.
Cell design → simulation tracecell_designs.id ↔ simulation_traces.project_idA design has 24 trace records if every task completed. It has fewer than 24 when one or more fast-charge tasks failed.

</div>

The 24 tasks for each design correspond to seven categories containing 28 predefined performance metrics. Discharge capacity and energy under the same condition are obtained from one simulation. The number of tasks is therefore four fewer than the number of metrics. Failed fast-charge tasks have no final result, so not every design has all 28 result values.

Complete Metric and Scene Catalogue

Each scene identifier below is the exact string stored in performance_results.scene.

Discharge Capacity

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
1<br><code>capacity<wbr>energy<wbr>0001</code>25 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · capacity (Ah)
2<br><code>capacity<wbr>energy<wbr>0002</code>45 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · capacity (Ah)
3<br><code>capacity<wbr>energy<wbr>0003</code>25 °C · full charge · 1C constant-current discharge to the cutoff voltage · capacity (Ah)
4<br><code>capacity<wbr>energy<wbr>0004</code>45 °C · full charge · 1C constant-current discharge to the cutoff voltage · capacity (Ah)

</div>

Discharge Energy

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
5<br><code>capacity<wbr>energy<wbr>0001</code>25 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · energy (Wh)
6<br><code>capacity<wbr>energy<wbr>0002</code>45 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · energy (Wh)
7<br><code>capacity<wbr>energy<wbr>0003</code>25 °C · full charge · 1C constant-current discharge to the cutoff voltage · energy (Wh)
8<br><code>capacity<wbr>energy<wbr>0004</code>45 °C · full charge · 1C constant-current discharge to the cutoff voltage · energy (Wh)

</div>

Continuous Discharge Power

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
9<br><code>continuous<wbr>discharge<wbr>power_<wbr>0001</code>25 °C · 20% SOC · constant-power discharge · 60 s target · power (W)
10<br><code>continuous<wbr>discharge<wbr>power_<wbr>0002</code>25 °C · 50% SOC · constant-power discharge · 60 s target · power (W)
11<br><code>continuous<wbr>discharge<wbr>power_<wbr>0003</code>25 °C · 80% SOC · constant-power discharge · 60 s target · power (W)
12<br><code>continuous<wbr>discharge<wbr>power_<wbr>0004</code>45 °C · 20% SOC · constant-power discharge · 60 s target · power (W)
13<br><code>continuous<wbr>discharge<wbr>power_<wbr>0005</code>45 °C · 50% SOC · constant-power discharge · 60 s target · power (W)
14<br><code>continuous<wbr>discharge<wbr>power_<wbr>0006</code>45 °C · 80% SOC · constant-power discharge · 60 s target · power (W)

</div>

Peak Discharge Power

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
15<br><code>peak<wbr>discharge<wbr>power_<wbr>0001</code>25 °C · 20% SOC · constant-power discharge · 10 s target · power (W)
16<br><code>peak<wbr>discharge<wbr>power_<wbr>0002</code>25 °C · 50% SOC · constant-power discharge · 10 s target · power (W)
17<br><code>peak<wbr>discharge<wbr>power_<wbr>0003</code>25 °C · 80% SOC · constant-power discharge · 10 s target · power (W)
18<br><code>peak<wbr>discharge<wbr>power_<wbr>0004</code>45 °C · 20% SOC · constant-power discharge · 10 s target · power (W)
19<br><code>peak<wbr>discharge<wbr>power_<wbr>0005</code>45 °C · 50% SOC · constant-power discharge · 10 s target · power (W)
20<br><code>peak<wbr>discharge<wbr>power_<wbr>0006</code>45 °C · 80% SOC · constant-power discharge · 10 s target · power (W)

</div>

Recharge Power

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
21<br><code>recharge<wbr>power<wbr>0001</code>−10 °C · 90% SOC · constant-power charging · 5 s target · power (W)
22<br><code>recharge<wbr>power<wbr>0002</code>25 °C · 90% SOC · constant-power charging · 5 s target · power (W)
23<br><code>recharge<wbr>power<wbr>0003</code>45 °C · 90% SOC · constant-power charging · 5 s target · power (W)

</div>

Fast-Charge Time

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
24<br><code>fast<wbr>charge<wbr>0001</code>−10 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)
25<br><code>fast<wbr>charge<wbr>0002</code>25 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)
26<br><code>fast<wbr>charge<wbr>0003</code>45 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)

</div>

Direct-Current Resistance

<div align="center">

No. / sceneOperating condition, simulation protocol, and result
27<br><code>direct<wbr>current<wbr>resistance_<wbr>0001</code>25 °C · 80% SOC · 3C constant-current discharge pulse · 10 s · DCR (mΩ)
28<br><code>direct<wbr>current<wbr>resistance_<wbr>0002</code>25 °C · 50% SOC · 2C constant-current discharge pulse · 10 s · DCR (mΩ)

</div>

Format and Loading

Each table is packaged as sharded, ZSTD-compressed Parquet files. Nested objects are retained as complete JSON strings, preserving their original names, ordering, and numeric text. Some strings embedded in these JSON objects retain their original Chinese values; they have not been translated or rewritten. The control columns status, order, and sub_order were converted to int32 only after exact string round-trip validation. All identifiers remain strings. The literal \N, empty strings, and JSON null values remain distinct. SHA-256 digests for every released file are listed in `CHECKSUMS.sha256`.

The following example reads one cell-design record and parses its nested JSON fields:

python
import json
from pathlib import Path

import pyarrow.parquet as pq

release = Path("/path/to/LiBMap-46800-NCM811-Graphite")
part = next(iter(sorted((release / "data/cell_designs").glob("part-*.parquet"))))
design = next(pq.ParquetFile(part).iter_batches(batch_size=1)).to_pylist()[0]

variables = json.loads(design["variables"])
mod_data = json.loads(design["mod_data"])
reference_capacity_ah = mod_data["result"]["cell"]["capacity"]

Each configuration can also be loaded separately with the Hugging Face datasets library:

python
from datasets import load_dataset

repo_id = "liming-chang/LiBMap-46800-NCM811-Graphite"
designs = load_dataset(repo_id, "cell_designs", split="train", streaming=True)

The train split represents the complete table. It is not a predefined machine-learning training set. This dataset provides no benchmark training, validation, or test split. For downstream splitting, all records with the same project_id should remain in the same partition. This grouping prevents information from other operating conditions of the same design from leaking into evaluation data. Additional data are required to assess generalisation to other material systems or cell formats.

Licence

This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0). It may be used for research, education, and other non-commercial purposes. Commercial use is prohibited.