Directory profiles · Battery Business Directory
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Sources and links on page 462 (16)
Links embedded in the original report PDF, including citations and other references. These destinations have not been independently reviewed. Links open in a new tab.
- https://battgenie.life/wp-content/uploads/2025/11/batt-daas-whitepaper-2025-26.pdf
- https://www.nrel.gov/news/detail/program/2025/artificial-intelligence-models-improve-efficiency-of-battery-diagnostics
- https://www.mdpi.com/1996-1073/17/24/6326
- https://pmc.ncbi.nlm.nih.gov/articles/PMC11779878/
- https://www.researchgate.net/publication/388443416_TimeGAN-based_Diversified_Synthetic_Data_Generation_Following_BERT-based_Model_for_EV_Battery_SOC_Prediction_A_State-of-the-Art_Approach
- https://arxiv.org/pdf/2503.12258
- https://www.researchgate.net/publication/372810363_Controlled_physics-informed_data_generation_for_deep_learning-based_remaining_useful_life_prediction_under_unseen_operation_conditions
- https://research.ibm.com/publications/forging-time-series-with-language-a-large-language-model-approach-to-synthetic-data-generation
- https://www.batteryarchive.org/
- http://batterydata.energy.gov/
- https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac
- https://calce.umd.edu/battery-data
- https://www.nrel.gov/transportation/battery-failure
- https://big-map.github.io/BattINFO/index.html
- https://batterydataalliance.energy/
- https://github.com/NREL/ampworks
Text on page 462
Availability of Open Data Repositories and Synthetic Data Generation Accelerate AI/ML Driven Applications The availability of open datasets / repositories and efforts into standardization are accelerating several AI / ML applications of battery research as the demand for energy storage accelerates. In addition to lab data, increased diversity of available field data is crucial for advancing battery management studies in real-world environments.
Institutional Repositories
The “Lab-to-Field” Gap & Synthetic Data Generation
Battery Archive Battery DataHub Open Datasets Oxford Battery Degradation Dataset CALCE - Experimental Test Data NREL Battery Failure Databank Data Standardization & Tools
The limited availability of large-scale, high-quality field data is a primary bottleneck for BMS development. This table outlines some latest developments in synthetic data generation that aim to address this: Architecture
Methodology & Innovation
Lab-to-Field Impact
TimeGAN / RCGAN
Uses Recurrent Conditional GANs to generate multivariate time-series conditioned on auxiliary variables (e.g., Temperature, SOC).
Synthesizes scarce "edge case" data (e.g., extreme cold profiles) to robustify models against unseen field conditions.
CPI-GAN
Embeds "Five Basic Physics Constraints" (e.g., Monotonicity, Energy Conservation) directly into the generator's loss function.
Prevents "physical hallucinations," ensuring generated degradation paths obey thermodynamic laws.
LLMs (SDForger)
"Textualizes" voltage signals, treating time-series forecasting as a natural language token prediction task.
Captures long-range temporal dependencies and enables multimodal analysis (integrating maintenance logs with voltage data).
BattINFO LF Energy Battery Data Alliance
Example AI / ML Applications PINNs for Battery Diagnostics: Physics-Informed Neural Networks (PINNs) are nearly 1,000 times faster than traditional physics models, making them ideal for real-time diagnostics. Mamba Model: a more efficient alternative to Transformers, for early Remaining Useful Life (RUL) prediction (trained on CALCE CS2 dataset).
Ampworks Source: BattGenie, PINNs, NREL, Liang Y, Zhao S., Energies 2024, Liu H, Li C et al., Nat Commun, 2025, Mohanty, Prasanta & Jena et al., IEEE Transactions on Industry Applications, 2025, Myisha A. Chowdhury, Gift Modekwe, and Qiugang Lu, 2025, Xiong, Jiawei & Fink et al., Mechanical Systems and Signal Processing, 2023, Cécile Rousseau, Tobia Boschi, et al. NeurIPS, 2025
Extracted from the PDF; charts and images may read imperfectly.
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