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The article explains why solid-state batteries are gaining attention, detailing their advantages over conventional lithium-ion batteries and the massive industry investment from companies like CATL, BYD, LG, and Samsung.
Introduces a deep learning surrogate pipeline based on Swin3D Transformer to predict spatiotemporal discharge dynamics in lithium-ion batteries, significantly reducing computational cost while improving accuracy over point cloud baselines.
Proposes RoSIP-Batt, a physics-guided multi-task transformer for joint estimation of battery State of Health and Remaining Useful Life, achieving state-of-the-art accuracy on NASA, MIT-Stanford, and HUST datasets.
Proposes RoSIP-Batt, a Transformer-based model for joint State of Health and Remaining Useful Life prediction of lithium-ion batteries, using dynamic loss balancing and rotary position embeddings, achieving state-of-the-art results on multiple datasets.
This paper presents BattVAE-GP, a hybrid physics-probabilistic framework that combines a Variational Autoencoder with a sparse multitask Gaussian Process to generate and interpolate long-horizon battery degradation trajectories with uncertainty estimates, enabling efficient surrogate modeling for lithium-ion battery health prediction.
This paper introduces IonSense-QKG, a metadata framework that enriches public lithium-ion battery datasets with quantum-readiness fields and a weighted Quantum Readiness Score to rank datasets for near-term hybrid quantum-classical machine learning.
Despite decades of hype, solid-state batteries remain unready for mass production, but semi-solid-state (gel) batteries are now entering the market, offering improved safety and longevity for power banks and e-bikes at a slightly higher cost.
This paper introduces MagBridge-Battery, a synthetic dataset of 6,760 magnetic-field signatures for Li-ion battery state-of-health diagnostics, combining real magnetic morphology with real degradation labels to bridge the gap in public magnetic-sensing battery data.
This paper introduces C2L-Net, a data-driven model for efficient and accurate state-of-charge estimation of lithium-ion batteries using short historical windows.