Welcome to our repository of open-source datasets and resources in the fields of battery monitoring and modeling! This platform serves as a comprehensive hub for researchers, engineers, and enthusiasts to access high-quality datasets, tools, and articles that empower advancements in battery technologies.
We extend our gratitude to the original publishers and contributors of these datasets, whose efforts and generosity enable the broader community to build upon their work. Explore and contribute to foster collaboration and innovation in the energy storage domain.
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Battery Failure Databank
- Related Articles:
- Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data
- The battery failure databank: Insights from an open-access database of thermal runaway behaviors of Li-ion cells and a resource for benchmarking risks
- Decoupling of heat generated from ejected and non-ejected contents of 18650-format lithium-ion cells using statistical methods
- Modelling and experiments to identify high-risk failure scenarios for testing the safety of lithium-ion cells
- Characterising thermal runaway within lithium-ion cells by inducing and monitoring internal short circuits
- Identifying the Cause of Rupture of Li-Ion Batteries during Thermal Runaway
- Tracking Internal Temperature and Structural Dynamics during Nail Penetration of Lithium-Ion Cells
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
19. A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles
The dataset for this paper is currently unavailable.
- Link: Code Repository
- Related Articles:
- Description: Twelve datasets containing charge and discharge cycles of Li–LixNi0.8Mn0.1Co0.1O2 (NMC 811) cells, under various formulations, configurations, and testing conditions. This dataset supports nondestructive evaluation methods and lithium inventory tracking as a battery monitoring technique.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
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).
- Link: Download Dataset
- Related Articles:
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.
- Link: Download Dataset
- Related Articles:
34. Source data: Revealing how internal sensors in a smart battery impact the local graphite lithiation mechanism
- Link: Download Dataset
- Related Articles:
35. Source data: Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions
- Link: Download Dataset
- Related Articles:
- Link: Download Dataset
- Related Articles:
- Link: Download Dataset
- Related Articles:
- Link: Download Dataset
- Related Articles:
Links to external sites