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

1. Overview — scope, provenance, and the present public subset
2. Design Space — five varied porous-electrode design parameters
3. Performance Simulations — protocols and termination conditions
4. Data Tables and Relationships — schemas, cardinalities, and join keys
5. Metric and Scene Catalogue — all 28 metrics and exact scene identifiers
6. Format and Loading — Parquet layout and loading examples
7. 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

Design parameter	Exact key in variables	Range or values	Number of values	Unit
Cathode single-sided coating thickness after calendering	cathode_single_face_thickness_post_rolling	[30, 65]	20	μm
Cathode compaction density after calendering	cathode_compaction_density_post_rolling	[3.3, 3.7]	20	g/cm³
Anode compaction density after calendering	anode_compaction_density_post_rolling	[1.3, 1.75]	20	g/cm³
Cathode particle diameter D50	cathode_uniform_diameter	[3, 8]	3	μm
Anode particle diameter D50	anode_uniform_diameter	[8, 16]	3	μm

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:

Termination condition	Trigger
Temperature cutoff	Cell temperature reaches 80 °C
Upper voltage cutoff	Terminal voltage reaches 4.2 V
Lower voltage cutoff	Terminal voltage reaches 3.0 V
Lithium-plating cutoff	Potential at the separator–anode interface is ≤ 0 V

Data Tables and Relationships

Table	Rows	Columns	Description
cell_designs	72,000	5	One cell design completed through material-balance calculations
performance_results	1,728,000	7	One requested operating condition and its summary result
simulation_traces	1,722,137	11	Detailed simulation settings and electrical, thermal, and polarisation data

1. cell_designs: Cell-Design Table

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

Field	Description
cell_designs.id	Unique identifier for the cell design. It links the design to the other two tables.
cell_designs.variables	Five varied design parameters: cathode coating thickness, cathode compaction density, anode compaction density, cathode particle D50, and anode particle D50.
cell_designs.mod_data	Inputs 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_input	Solver inputs for the design, including geometry, electrode properties, and solver-control parameters.
cell_designs.additional	Termination conditions and other information, including upper and lower voltage limits, the 80 °C temperature cutoff, and material thermal and kinetic parameters.

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.

Field	Description
performance_results.id	Unique identifier for the performance task. It links the task to its detailed simulation record.
performance_results.scene	Operating-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.status	Task 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.additional	Records 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_input	Contains three fixed Bruggeman coefficients for the anode, separator, and cathode.
performance_results.result	Contains the performance result. Its structure depends on the operating-condition category. A failed calculation is represented by the literal \N.
performance_results.project_id	Identifier of the associated design. It corresponds to cell_designs.id.

The principal result fields are:

Performance category	Result field	Unit
Discharge capacity and energy	performance_results.result.capacity, performance_results.result.energy	Ah, Wh
60 s continuous discharge power	performance_results.result.continuous_discharge_power	W
10 s peak discharge power	performance_results.result.peak_discharge_power	W
5 s recharge power	performance_results.result.recharge_power	W
20%–80% SOC fast-charge time	performance_results.result.fast_charge_time	min
Direct-current resistance	performance_results.result.dcr	mΩ

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.

Field	Description
simulation_traces.id	Unique identifier for the detailed simulation record.
simulation_traces.order	Sequence number within the simulation workflow. It is not the row number of the complete table.
simulation_traces.sub_order	Subsequence number within the workflow or search process.
simulation_traces.step	Simulation-category and stage identifier, such as a capacity-and-energy condition or a fast-charge stage.
simulation_traces.condition	Initial SOC, ambient temperature, convective heat-transfer boundary, charge or discharge mode, control value, and stage-termination conditions.
simulation_traces.status	Completion 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.result	Curves for current, voltage, electrode potentials, temperature, heat generation, and polarisation decomposition. It also contains high-temperature and error flags.
simulation_traces.result_fields	Names and ordering of entries in simulation_traces.result. This field identifies the corresponding curves and flags.
simulation_traces.additional	Summary 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_id	Identifier of the associated design. It corresponds to cell_designs.id.
simulation_traces.task_id	Identifier of the associated performance task. It corresponds to performance_results.id.

4. Relationships Among the Three Tables

Relationship	Join fields	Cardinality
Cell design → performance result	cell_designs.id ↔ performance_results.project_id	Every cell design corresponds to exactly 24 performance tasks.
Performance result → simulation trace	performance_results.id ↔ simulation_traces.task_id	Each completed task corresponds to one detailed record. Failed fast-charge tasks have no detailed record.
Cell design → simulation trace	cell_designs.id ↔ simulation_traces.project_id	A design has 24 trace records if every task completed. It has fewer than 24 when one or more fast-charge tasks failed.

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

No. / scene	Operating condition, simulation protocol, and result
1 capacity_energy_0001	25 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · capacity (Ah)
2 capacity_energy_0002	45 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · capacity (Ah)
3 capacity_energy_0003	25 °C · full charge · 1C constant-current discharge to the cutoff voltage · capacity (Ah)
4 capacity_energy_0004	45 °C · full charge · 1C constant-current discharge to the cutoff voltage · capacity (Ah)

Discharge Energy

No. / scene	Operating condition, simulation protocol, and result
5 capacity_energy_0001	25 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · energy (Wh)
6 capacity_energy_0002	45 °C · full charge · 1/3C constant-current discharge to the cutoff voltage · energy (Wh)
7 capacity_energy_0003	25 °C · full charge · 1C constant-current discharge to the cutoff voltage · energy (Wh)
8 capacity_energy_0004	45 °C · full charge · 1C constant-current discharge to the cutoff voltage · energy (Wh)

Continuous Discharge Power

No. / scene	Operating condition, simulation protocol, and result
9 continuous_discharge_power_0001	25 °C · 20% SOC · constant-power discharge · 60 s target · power (W)
10 continuous_discharge_power_0002	25 °C · 50% SOC · constant-power discharge · 60 s target · power (W)
11 continuous_discharge_power_0003	25 °C · 80% SOC · constant-power discharge · 60 s target · power (W)
12 continuous_discharge_power_0004	45 °C · 20% SOC · constant-power discharge · 60 s target · power (W)
13 continuous_discharge_power_0005	45 °C · 50% SOC · constant-power discharge · 60 s target · power (W)
14 continuous_discharge_power_0006	45 °C · 80% SOC · constant-power discharge · 60 s target · power (W)

Peak Discharge Power

No. / scene	Operating condition, simulation protocol, and result
15 peak_discharge_power_0001	25 °C · 20% SOC · constant-power discharge · 10 s target · power (W)
16 peak_discharge_power_0002	25 °C · 50% SOC · constant-power discharge · 10 s target · power (W)
17 peak_discharge_power_0003	25 °C · 80% SOC · constant-power discharge · 10 s target · power (W)
18 peak_discharge_power_0004	45 °C · 20% SOC · constant-power discharge · 10 s target · power (W)
19 peak_discharge_power_0005	45 °C · 50% SOC · constant-power discharge · 10 s target · power (W)
20 peak_discharge_power_0006	45 °C · 80% SOC · constant-power discharge · 10 s target · power (W)

Recharge Power

No. / scene	Operating condition, simulation protocol, and result
21 recharge_power_0001	−10 °C · 90% SOC · constant-power charging · 5 s target · power (W)
22 recharge_power_0002	25 °C · 90% SOC · constant-power charging · 5 s target · power (W)
23 recharge_power_0003	45 °C · 90% SOC · constant-power charging · 5 s target · power (W)

Fast-Charge Time

No. / scene	Operating condition, simulation protocol, and result
24 fast_charge_0001	−10 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)
25 fast_charge_0002	25 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)
26 fast_charge_0003	45 °C · 20%–80% SOC · 12-stage constant-current charging (5% SOC per stage) · time (min)

Direct-Current Resistance

No. / scene	Operating condition, simulation protocol, and result
27 direct_current_resistance_0001	25 °C · 80% SOC · 3C constant-current discharge pulse · 10 s · DCR (mΩ)
28 direct_current_resistance_0002	25 °C · 50% SOC · 2C constant-current discharge pulse · 10 s · DCR (mΩ)

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:

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:

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.