失效和热失控相关
寿命和性能相关
- Calendar aging model for lithium-ion batteries considering the influence of cell characterization
- Degradation diagnostics for lithium ion cells
- Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries
- Lithium-ion battery degradation modelling using universal differential equations: Development of a cost-effective parameterisation methodology
- Lithium-ion battery degradation: how to model it
- Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
- Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries
- Learning the P2D Model for Lithium-Ion Batteries with SOH Detection
- A Multilayer Doyle-Fuller-Newman Model to Optimise the Rate Performance of Bilayer Cathodes in Li Ion Batteries
- A modified Doyle-Fuller-Newman model enables the macroscale physical simulation of dual-ion batteries
- Lithium ion battery degradation: what you need to know
- A Single Particle model with electrolyte and side reactions for degradation of lithium-ion batteries
- Review—“Knees” in Lithium-Ion Battery Aging Trajectories
- Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
- Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells
- Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
- Dynamic double similarity fusion based on ΔQ power law for early-cycle RUL prediction of lithium-ion batteries
- A generic physics-informed machine learning framework for battery remaining useful life prediction using small early-stage lifecycle data
- Degradation Curve Prediction of Lithium-Ion Batteries Based on Knee Point Detection Algorithm and Convolutional Neural Network
- Detection of abnormal SOH estimates in battery field data using statistical learning
- Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning
- State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model
- Nonlinear aging knee-point prediction for lithium-ion batteries faced with different application scenarios
- Transformer Explainer: Interactive Learning of Text-Generative Models
- A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis
- Respecting causality is all you need for training physics-informed neural networks
- From local explanations to global understanding with explainable AI for trees
- Accurate predictions on small data with a tabular foundation model
- A Unified Approach to Interpreting Model Predictions
- Attention Is All You Need
- Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution
- Vision Transformers: State of the Art and Research Challenges
- Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
- A Kernel Approach for PDE Discovery and Operator Learning
- Noise-aware physics-informed machine learning for robust PDE discovery
- Data-driven discovery of partial differential equations
- DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
- Physics-informed learning of governing equations from scarce data