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This repository curates open-source datasets and resources in battery monitoring and modelling. It aims to help researchers and engineers quickly find datasets for state estimation, degradation analysis, and thermal–electrochemical modelling, and to support reproducible benchmarking across studies.

We gratefully acknowledge the original publishers and contributors of these datasets. Please cite the original sources and follow the licence terms when using the data.

Battery_dataset_Shiyun Liu

Available Datasets

1. Multi-year field measurements of home storage systems and their use in capacity estimation

The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders.

2. Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation

This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.

All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.

Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.

3. Battery Failure Databank

The Battery Failure Databank features data collected from hundreds of abuse tests conducted on commercial lithium-ion batteries. Methods of abuse include nail penetration, thermal abuse, and internal short-circuiting.

This databank provides the heat output from cells undergoing thermal runaway, the breakdown of heat from the cell casing and its ejected contents, as well as the mass of the cells before and after thermal runaway and the quantity of mass ejected from the cell. The databank also organizes the stored data for understanding test-to-test variability for each cell type and trigger mechanism combination.

Additionally, most tests feature associated high-speed X-ray radiography videos for review alongside the data.

4. Second-life lithium-ion battery aging dataset based on grid storage cycling

This dataset accompanies the data article "Second-life lithium-ion battery aging dataset based on grid storage cycling" and contains second-life experimental data collected at Stanford Energy Control Lab for six NMC cells cycled using residential and commercial synthetic duty cycles. The data is shared in a .zip format. Please refer to the publication accompanying the dataset to get further details.

5. Dataset of lithium-ion battery formation and structured aging cycling data

The dataset includes raw formation data and structured aging cycling data processed by TRI BEEP. The BEEP structured data features interpolated cycling data with a manageable data size and cycle summaries. This dataset enables insights into the role of electrode utilization in extending battery cycle life.

6. Full factorial design of experiments dataset for parallel-connected lithium-ion cells

This dataset includes experimental data from lithium-ion battery parallel-connected modules. Conducted at the Stanford Energy Control Laboratory, it employs a comprehensive full factorial design of experiments (DOE) on ladder-configured parallel strings. It investigates 54 test conditions, varying by temperature, cell-to-cell interconnection resistance, cell chemistry, and aging levels. Measurements include individual cell current and temperature distributions, enabling detailed analyses of cell-to-cell variations and their impact on module performance. Cell characterization data for NCA and NMC cells are also included.

7. Lithium-ion battery aging dataset based on electric vehicle real-driving profiles

This dataset contains aging data of lithium-ion battery cells subjected to a typical electric vehicle discharge profile and periodic diagnostic tests. Collected over 28 months at Stanford Energy Control Laboratory, it includes tests for INR21700-M50T cells using Urban Dynamometer Driving Schedule (UDDS) discharge and Constant Current (CC)-Constant Voltage (CV) charging protocols. The dataset monitors capacity, Hybrid Pulse Power Characterization (HPPC), and Electrochemical Impedance Spectroscopy (EIS) to characterize battery aging under real-world driving conditions.

8. Experimental data of lithium-ion batteries under galvanostatic discharge tests at different C-rates and temperatures

This dataset contains experimental data for three lithium-ion batteries tested under galvanostatic discharge at various C-rates and operational temperatures. Using the Arbin system, the dataset provides detailed measurements of voltage, current, and battery skin temperature, with ambient temperature controlled via a thermal chamber. It enables analyses of battery performance under varying charge-discharge rates and temperature conditions.

9. Increasing generalization capability of battery health estimation using continual learning approach

These datasets contain data from different battery types (pouch and prismatic) aged under various temperatures and loading profiles. The data includes partial Q curves during charging, capacities, and state-of-health (SOH) curves normalized for verification. The dataset also incorporates information from non-accelerated battery degradation experiments, with detailed raw data for Dataset 5 provided for further analysis.

10. EV field datasets (Tsinghua)

Three real-world, large-scale electric vehicle datasets collected from 464 EVs of 3 different types, including over 1.2 million charging snippets. The dataset provides insights into real-world electric vehicle charging behaviors and patterns.

11. Experimental degradation study of a commercial lithium-ion battery

This dataset contains data from the aging of 196 commercial lithium-ion cells with silicon-doped graphite anode and nickel-rich NCA cathode. The cells were subjected to a wide range of calendar and cyclic aging conditions, with periodic check-ups performed at a controlled temperature of 20°C. The dataset reveals insights into the effects of aging conditions on battery degradation, providing a comprehensive view of aging patterns.

12. Attention towards chemistry agnostic and explainable battery lifetime prediction

This dataset contains pre-trained model weights from the ARCANA framework, including models trained and fine-tuned on different battery chemistries, such as lithium-ion and sodium-ion cells. It provides resources for explainable battery lifetime prediction across various chemistries.

13. Underlying dataset for battery pack degradation

This dataset contains raw and processed data, as well as analysis codes, used to investigate aging in parallel-connected lithium-ion battery packs under thermal gradients. The dataset supports research into the degradation behaviors of battery packs and the effects of thermal gradients.

14. NASA Battery Data Set

A dataset of lithium-ion battery experiments, including charging and discharging at different temperatures. It also records impedance as a damage criterion, providing data for studying battery prognostics and health management.

15. Randomized Battery Usage

This dataset features batteries cycled with randomly generated current profiles. Reference charging and discharging cycles are performed periodically to provide benchmarks for battery state of health.

16. HIRF Battery Data Set

This dataset contains battery data collected from experiments on the Edge 540 Aircraft in a HIRF (High-Intensity Radiated Field) Chamber, providing insights into battery performance under unique testing conditions.

17. Small Satellite Power Simulation

This dataset includes data from simulated experiments on small satellite BP930 batteries using the MACCOR system. It provides valuable insights into the battery performance for small satellite missions.

18. Accelerated Battery Life Testing

This dataset includes accelerated lifecycle data for Li-ion battery packs composed of two 18650 cells. It covers various loading conditions, including constant and random loading levels, as well as second-life battery pack cycling.

19. A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

The dataset for this paper is currently unavailable.

20. Lithium Inventory Tracking as a Nondestructive Battery Evaluation and Monitoring Method

21. Probability Distributed Equivalent Circuit Model

This dataset supports the development of a physically motivated voltage hysteresis model for lithium-ion batteries. The model leverages probability distributed equivalent circuits to enhance accuracy in performance predictions.

22. GIC/NMC Solar Battery Synthetic Data

This dataset contains more than 700,000 unique voltage vs. capacity curves, generated with slightly varied cell parameters to account for cell-to-cell variations. It includes separate datasets for training and validation.

23. Data-driven Capacity Estimation of Commercial Lithium-Ion Batteries from Voltage Relaxation

Experimental cycling data for three commercial 18650 type batteries (NCA, NCM, and NCM+NCA chemistries). The dataset provides cycling data, impedance measurements, and detailed descriptions of voltage relaxation tests.

24. Impedance-Based Forecasting of Battery Performance Amid Uneven Usage

Dataset of 88 commercial lithium-ion coin cells cycled under multistage constant current charging/discharging. Currents were randomly changed between cycles to simulate realistic usage patterns.

25. Identifying Degradation Patterns of Lithium-Ion Batteries from Impedance Spectroscopy Using Machine Learning

This dataset supports machine learning-based identification of degradation patterns in lithium-ion batteries through impedance spectroscopy analysis.

26. Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation

Dataset supporting the prediction of battery cycle life prior to significant capacity degradation. The data enables early detection and modeling for lithium-ion battery lifecycle management.

27. Physics-Informed Neural Network for Lithium-Ion Battery Degradation Stable Modeling and Prognosis

This dataset includes data for 55 nickel-cobalt-manganese (NCM) 18650 batteries tested under six different charging and discharging strategies. The data supports physics-informed neural network modeling for stable degradation analysis.

28. Real-Time Personalized Health Status Prediction of Lithium-Ion Batteries Using Deep Transfer Learning

A dataset containing 77 LFP/graphite cells tested under identical charge protocols but varying discharge protocols. The data supports real-time health prediction using deep transfer learning techniques.

29. Prognosis of Multivariate Battery State of Performance and Health via Transformers

This dataset contains processed files for reproducing results in multivariate battery state prediction using transformers. It includes datasets for lithium-iron-phosphate fast charging and six cathode chemistries.

30. Battery Aging Modes Across NMC

A dataset of 44 NMC/Gr single-layer pouch cells, including data on cycle-by-cycle capacity, Coulombic efficiency, and end-of-charge/discharge voltages. The dataset also includes code for battery aging mode classification.

31. Predicting Battery End of Life from Solar Off-Grid System Field Data Using Machine Learning

This dataset contains field data from 1027 lead-acid batteries used in solar off-grid systems across sub-Saharan Africa. The data includes performance metrics for batteries used in lighting, phone charging, and small appliances.

32. ISU-ILCC Battery Aging Dataset

The ISU-ILCC battery aging dataset was collected jointly by the System Reliability and Safety Laboratory at Iowa State University (ISU), now the Reliability Engineering and Informatics Laboratory (REIL) at the University of Connecticut, and Iowa Lakes Community College (ILCC). The dataset is designed to study the dependency of battery capacity fade from three stress factors: charge rate, discharge rate, and depth of discharge. The dataset contains cycle aging data from 251 lithium-ion (Li-ion) polymer cells (also called lithium polymer cells) cycled under 63 unique conditions. The current release contains 238 cells; the other 12 cells have not completed the testing (data from those cells will be included in a future release).

33. Dataset: Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell

The dataset contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for almost 600 days under a wide range of operating conditions.

34. Source data: Revealing how internal sensors in a smart battery impact the local graphite lithiation mechanism

35. Source data: Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions

36. Source data: Reuse and recycling pathway of retired-batteries

37. Challenges and opportunities in truck electrification unveiled by big operational data

38. Dataset - Dynamic cycling enhances battery lifetime

The study presents a systematic comparison of dynamic discharge profiles, representative of electric vehicle driving, with constant-current discharge profiles, commonly used in laboratory testing. The results indicate that dynamic discharge profiles can extend the lifetime of lithium-ion batteries significantly. Specifically, under identical average current and voltage conditions, dynamic profiles result in up to a 38% increase in equivalent full cycles at the battery's end-of-life. This enhancement is attributed to the influence of low-frequency current pulses and time-induced aging, as revealed through explainable machine learning. These findings emphasize the necessity of incorporating realistic load profiles in the evaluation of new battery chemistries and designs to better understand aging mechanisms and improve battery performance.

39. Stanford Long Term Calendar Aging Dataset

This dataset contains calendar aging from eight different cell types stored at 2 different SOCs and 4 different temperatures. The raw data contains all the voltage and current data at each diagnostic cycle conducted at regular intervals throughout the storage period for the cells. Cell Types: K2 Energy LFP18650E/K2 Energy LFP18650P/Panasonic NCR18650B/Panasonic NCR18650GA/Sony-Murata US18650VTC6/Tenergy 302030/Ultralife 502030/Ultralife UBP001; Storage SOC Conditions: 50%/100%; Storage Temperatures: 24°C/45°C/60°C/85°C;

40. Battery Capacity Prognostics Dataset for On-Road Electric Vehicles

This dataset provides a comprehensive dataset for analyzing and predicting the capacity degradation of lithium-ion batteries in electric vehicles (EVs). Collected from 20 EVs over approximately 29 months, the dataset includes charging data, labeled capacity values derived from statistical methods, and features optimized through data-driven algorithms.

41. WMG-DIB Data

This dataset provides a rich dataset for understanding battery performance degradation. It includes data from 25 cylindrical lithium-ion cells aged through controlled electrical cycling at 25°C to predefined SOH breakpoints (80%, 85%, 90%, 95%, and 100%). The dataset features reference performance tests (RPT), electrochemical impedance spectroscopy (EIS) measurements across various states of charge (SOC) and temperatures, and key metrics derived from EIS plots.

42. WMG Calendar Ageing Dataset - LGM50 Commercial Cells (39 Storage Conditions)

The dataset presents comprehensive calendar aging data collected from commercial LGM50 lithium-ion cells under controlled storage conditions: Storage Temperatures: 0°C, 25°C, and 45°C; State of Charge (SOC): 13 distinct levels per temperature condition; Test Duration: Two-years per condition (on average). The dataset consists of MATLAB (.mat) files containing cell cycling results from Reference Performance Tests (RPTs) conducted throughout the aging study.

43. Cycle aging data of automotive-grade lithium ion battery cells under realistic and accelerated load conditions

This data set contains cycle aging data from three automotive-grade NMC|Gr pouch cells (used in the VW ID.3) subjected to three different usage patterns over a duration of 3+ years. Please refer to the linked article for cell and measurement specifications. All measurements are provided as .txt files (raw files exported from the battery test system).

44. Retired lithium-ion batteries dataset

45. Synthetic Duty Cycles from Real-World Autonomous Electric Vehicle Driving: Accompanying Data

46. Model-Constrained Deep Learning for Online Fault Diagnosis in Li-ion Batteries over Stochastic Conditions

The authors utilize data uploaded from vehicle onboard Battery Management Systems (BMS) for network design. They have released a real-world vehicle dataset containing 18.2 million valid entries collected from 515 vehicles, which was gathered by the BMS data center on the cloud. The dataset primarily consists of data from three battery manufacturers, which, due to confidentiality restrictions, are referred to as DTI, QAS, and GIS in the paper. In addition to normal operating data, the dataset also includes four types of rare and critical safety failure samples: Thermal runaway, Electrolyte leakage, Internal short circuit, Excessive ageing.

47. Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"

These data were generated from battery cell ageing experiments that included break-in cycles and Reference Performance Tests (RPTs), alternating with performance checks and ageing cycles. Each cell was base-cooled at set temperatures using bespoke rigs, with full experimental details available in linked publications. The repository is structured according to each “Experiment,” containing folders for “Summary Data,” “Processed Timeseries Data,” and “Raw Data.” The “Summary Data” folder holds metrics such as capacity, resistance, degradation mode analysis, and ageing statistics. “Processed Timeseries Data” includes subtest-specific voltage, current, and temperature in .csv files, while “Raw Data” comprises proprietary .mpr and .mpt files. An “experimental_metadata” .xlsx and a Jupyter notebook are provided for straightforward data parsing, analysis, and visualization.

48. Air-cooled lithium-ion battery pack (LIBP) extracted from a plug-in hybrid vehicle (PHEV)

49. Long-Term Sweat Testing Data for Second-Life Batteries

This dataset contains long-term cycling data from repurposed lithium-ion batteries originally used in electric vehicles and redeployed in second-life stationary energy storage applications. It includes data from six distinct use cases that simulate real-world scenarios such as time-of-use tariffs, PV-self consumption, and frequency response services. The raw data was recorded using a Chroma 17020 battery cycler and testing began in 2019. The testing is currently still on going and the repository will be periodically updated. Files were initially stored as large continuous logs and have been processed into individual charge-discharge cycles (in .csv format) for ease of use. The dataset supports research in battery ageing, second-life performance assessment, and state of health modelling.

50. Aging dataset LCO battery with mechanical measurements

Aging dataset of a commercial 22Ah LCO-graphite pouch Li-Po battery. The cycling procedure involves aging steps consisting of 22 aging cycles at 1C CC discharge and C/2 CC-CV charge, with no pauses in between. Periodic RPTs are carried out after each aging step. In particular, two series of RPTs are alternated, referred to as RPT-A and RPT-B, with this pattern: 22 aging cycles -> RPT-A -> 22 aging cycles -> RPT-A + RPT-B -> repeat. The RPT-A consists of three high rate cycles (1C CC discharge and C/2 CC-CV charge) with 1 hour rest. The RPT-B consists of three high rate cycles (1C CC discharge and C/2 CC-CV charge) with 1 hour rest, one low rate cycle (C/20) and the HPPC test. In this way, high rate test cycles are carried out periodically every 25 cycles (22 aging + 3 test), whereas low rate test cycles and HPPC are carried out every 50 cycles. The exact number at which each reference performance test was carried out is reported in the sixth column of the data structure.

In total, 1125 cycles were achieved untill SOH 70%.

The cycling reference performance tests (high rate cycling 1C-C/2, and low rate cycling C/20-C/20) are reported in the MATLAB structure called Aging_Dataset_Cycling. On the other, the data of the HPPC tests are reported in the MATLAB structure called Aging_Dataset_HPPC.

The data structure of cycling reference performance tests is a MATLAB cell organized so that in the first row there are data of RPT-A (high rate cycles), and in the second row the data of RPT-B (low rate cycles). In the first column there are discharge data, in the second column the charge data, in the third column the data recorded in the one hour rest after discharge and in the fourth column the data recorded in the one hour rest after charge. In each element of this 2x4 matrix there is a cell containing the structures referring to each reference performance tests. The different reference performance tests are organized so that in the row there are the reference performance tests carried out at different aging cycles (detailed in the vector in the sixth column of the main data structure) and in the column there are the tests repeated at the same aging cycles for statistical studies. Generally RPT-A tests are repeated three times and RPT-B tests are repeated one times. Then, each cell, e.g. D{1,1}{1,1} contains a structure with the data of that test coded as explained in the bullet list below.

51. Dataset of lithium-ion battery degradation based on a forklift mission profile for state-of-health estimation and lifetime prediction

52. A dataset of over one thousand computed tomography scans of battery cells

1015 commercially available batteries were scanned using a X-ray computed tomography system. These scans were processed into a set of images that capture the major axes of the battery. For cylindrical cells, these are radial and axial views of the battery. For pouch and prismatic cells, these are x-y, x-z, and y-z views of the battery.

53. An ultrasonic and deep-learning dataset for real-time State of Charge estimation in large-format lithium-ion cells

This dataset contains experimentally collected ultrasonic waveforms acquired from large-format lithium-ion pouch cells during controlled charging and discharging procedures. Four ultrasonic sensors were arranged around each cell, enabling multiple bidirectional actuator–receiver transmission paths. For every acquisition point, full raw waveforms were recorded together with the corresponding ground-truth State of Charge (SoC).

The collected signals were processed into two complementary data forms:
(1) time-series CSV files containing the raw waveform amplitudes for each sensor pair, and
(2) waveform-derived image representations suitable for convolutional neural network–based transfer learning. Charging data follow the naming convention SignalA_B_SoC_raw.csv, while discharging data follow SignalA_B_SoCD_raw.csv, where A and B denote the actuator and receiver sensor IDs.

54. Battery Imaging Library

The Battery Imaging Library (BIL) is an open and curated repository of battery imaging datasets spanning multiple imaging modalities and length scales. It includes data from single particles to full battery cells, comprising over 80 scans across 8 modalities and more than 500 billion voxels in total. The collection provides raw, reconstructed, and processed data, supporting multi-scale analysis, method development, and reproducible research in battery science.

55. Lithium-ion battery pack cycling dataset with CC-CV charging and WLTP/constant discharge profiles

This work presents a database of a Lithium-ion battery pack cycling test generated from a custom testbench that simulates dynamic driving conditions based on the WLTP cycle. The current profiles are derived from speed–time data using MATLAB/Simulink and a Tesla Model 3 vehicle model. The dataset includes the time series of cell voltages, currents, surface temperatures, and pack-level resistance across up to 36 cells arranged in three parallel branches. Data is recorded under controlled thermal conditions and stored in an efficient PARQUET format. The system uses a CAN bus architecture and commercial automotive BMS units to replicate in-vehicle communication constraints, enhancing the dataset’s relevance for real-world battery management system development and validation.

56. BEV energy dynamics dataset

This dataset comprises high-resolution (1-second) time-series measurements of actual energy values associated with a battery electric vehicle, including the charger, the battery system, and auxiliary devices. It captures energy behavior across various operational modes—charging, driving, and parking—under diverse operational conditions. By providing a detailed view of energy distribution, this dataset enables a range of analytical and practical applications, such as optimizing smart charging strategies, reducing operational costs, analyzing usage patterns, improving the efficiency of high energy-consuming components, developing AI-based predictive models, and assessing the vehicle’s impact on the power grid. Overall, it offers a rich foundation for advancing research in electric mobility, energy efficiency, and intelligent energy management.

57. Multi-Stage Lithium Ion Battery Aging Study

This dataset presents a large-scale study of lithium-ion battery aging that jointly examines calendar and cycling effects in commercial Samsung INR21700-50E cells. In total, 279 cells were tested under 71 different aging scenarios implemented in two experimental phases. The first phase applied non-model-based experimental designs, including full-factorial and Latin hypercube sampling, to broadly explore degradation trends. The second phase combined model-based parameter-individual optimal experimental design with an additional non-model-based strategy to enable a systematic comparison of design approaches. Beyond its original purpose of assessing the advantages of optimal experimental design for battery aging studies, the dataset supports a wide range of secondary uses, such as data-driven lifetime prediction, parameterization of physics-based and empirical degradation models, and exploratory analyses aimed at revealing previously unobserved aging relationships.

58. Improving Real-World Execution of Optimized Trading Schedules for Large-Scale Battery Storage Systems through Data-Driven Component Parametrization: Dataset

This dataset contains detailed operational data from real-world field trials of a hybrid battery energy storage system in Aachen, Germany. It offers second-by-second measurements of power flows, state of charge at both unit and system level, and grid interaction, alongside the corresponding optimized dispatch schedules. By capturing results from two different test configurations, the dataset allows for direct evaluation of modeling improvements, schedule accuracy, and system efficiency. It is well suited for studies on battery storage optimization, validation of market-based dispatch strategies, and performance analysis of grid-connected energy storage systems.

59. Rate Capability Tests, OCV Measurements, Impedance and Half-Cell Measurements of Sodium-Ion and Lithium-Iron-Phosphate Batteries: Dataset

This dataset contains comprehensive electrochemical characterization data of commercial rechargeable batteries, including two sodium-ion batteries with layered oxide cathodes and one lithium-ion battery with a lithium-iron-phosphate (LFP) cathode. It provides full-cell and half-cell open-circuit voltage (OCV) measurements (pseudo-OCV and GEIS-based OCV), galvanostatic electrochemical impedance spectroscopy (GEIS), direct current resistance (R-DC), and rate capability test results. The data were collected through controlled laboratory experiments and form the experimental basis for a systematic comparison of electrical performance between sodium-ion and lithium-ion technologies. The dataset is well suited for battery modeling, parameter identification, impedance analysis, low-rate and high-rate performance evaluation, and comparative studies of emerging sodium-ion chemistries.

60. Battery failure detection and FPI estimation during cycling-to-failure

61. Electric Vehicle Usage Data Set for Battery Aging Studies

This dataset contains real-world field measurements from 28 portable 24 V lithium iron phosphate (LFP) battery systems, each built with 8 prismatic cells in series (with cell-level voltage sensing, one pack current sensor, and four temperature sensors placed between adjacent cells) and operated for up to 5 years. The systems include active cell balancing, and the recorded time-series data spans from one month to five years per unit, totalling ~133 million rows. Note that all units were returned to the manufacturer due to abnormal/unsatisfactory behaviour, so the dataset is biased toward problematic systems and is not representative of the full population.

62. Mechanistically Guided Residual Learning Dataset for Battery State Monitoring

This dataset accompanies a mechanistically guided residual learning framework for battery state monitoring across the full life span. It combines electrochemical characterisation data and long-term cycling data from both prismatic and pouch lithium-ion cells, together with reproducible Python code used to generate all reported results.

The data include open-circuit voltage (OCV) measurements and cycling trajectories for different cell formats, enabling joint analysis of physics-based features and data-driven residual learning across ageing stages. The accompanying code implements the residual learning framework and supports end-to-end reproduction of figures and state-monitoring results reported in the associated study.

This dataset is suitable for research on battery state estimation, hybrid physics–machine learning methods, feature attribution and interpretability, and full-life degradation modelling.

63. UConn LCO/Gr Battery Fast Charging Dataset

This dataset comprises 76 Powerstream LiR2032 Li-ion coin cells with a lithium cobalt oxide/graphite (LCO/Gr) chemistry, assembled to quantify Coulombic efficiency and long-term degradation under fast-charging conditions.

The data include results from 40 cells tested with SOC-sweep experiments and 36 cells evaluated under benchmark fast-charging protocols. SOC-sweep tests were performed at four target SOH levels (100%, 90%, 80%, 70%) and across 1.5C–3.5C charge rates to characterise how Coulombic efficiency varies with SOC, C-rate, and ageing state. Benchmark tests consist of repeated charge–discharge cycling blocks interleaved with reference performance tests (RPTs) for capacity measurement, continued until end-of-life at 70% remaining capacity, under controlled ambient conditions (~25 °C).

64. Data repository for 'Electrochemical-thermal modelling of high power Li-ion pouch cells'

This data repository provides experimentally measured electrochemical and thermal responses of a large-format lithium-ion pouch cell with a lithium iron phosphate/graphite (LFP/Gr) chemistry, intended for the study of high-power operation. The dataset is based on a series of controlled laboratory experiments combining constant-current charge and discharge tests with pulse-based protocols over a broad range of operating conditions. Measurements were conducted at multiple states of charge and C-rates spanning moderate to highly aggressive regimes (up to 10C), enabling detailed observation of both transient and steady-state behaviour. High-resolution lock-in thermography was employed to capture spatially resolved heat generation alongside electrical responses, allowing joint analysis of thermal dynamics and electrochemical performance. The dataset is designed to support electrochemical–thermal modelling, validation of coupled simulation frameworks, and investigation of power-related limitations relevant to automotive and other high-demand applications.

65. Discovery Learning predicts battery cycle life from minimal experiments

This paper’s primary (new) dataset is an industrial-grade ageing dataset of 123 large-format (73–84 Ah) lithium-ion pouch cells, spanning eight cell types (PA-b1/PA-b2/PA-b3, PB-b1/PB-b2, PC-b1/PC-b2, PD) with varied material–design combinations and cycling protocols: most cells use NMC811 cathodes with graphite anodes, while the PD type uses an NMC9 cathode with a Si–C composite anode (10 wt% Si); cells are cycled under diverse conditions (temperature and charge/discharge C-rates, including constant-current discharge, EV-like dynamic discharge, and multistep charging with a constant-voltage step), and the dataset provides full capacity-retention trajectories and cycle-life labels (defined at 90% capacity retention) with lifetimes spanning roughly 250–1,700 equivalent full cycles, plus high-resolution early-cycle data (first 50 EFCs); repeated tests are organised into 37 groups of near-identical chemistry/design/protocol to capture manufacturing variability at both cell and group level.

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