失效和热失控相关
- The critical importance of stack pressure in batteries
- Strategy and mechanism for external-induced recovery of lithium-ion battery internal short circuit
- Internal short circuits in lithium-ion batteries; origins, detection, and experimental reproduction: A review
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
- 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
- Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning
- A Comprehensive Review on Lithium-Ion Battery Lifetime Prediction and Aging Mechanism Analysis
- Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system
- Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation
- A novel method for state of health estimation of lithium-ion batteries based on fractional-order differential voltage-capacity curve
- A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization
- Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution
- Respecting causality is all you need for training physics-informed neural networks
- A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis
- Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries
- Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
- Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
- 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
- Taming Rectified Flow for Inversion and Editing
- Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
- Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
- InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
- Flow-GRPO: Training Flow Matching Models via Online RL
- Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching (ClusCa)
- Denoising Diffusion Probabilistic Models (DDPM) | 中文总结
- High-Resolution Image Synthesis with Latent Diffusion Models (LDM/SD) | 中文总结
- Denoising Diffusion Implicit Models (DDIM)
- Elucidating the Design Space of Diffusion-Based Generative Models (EDM)
- Transformer Explainer: Interactive Learning of Text-Generative Models
- 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
- Vision Transformers: State of the Art and Research Challenges
- A predictive approach to enhance time-series forecasting
- Goal-oriented Feature Extraction: a novel approach for enhancing data-driven surrogate model