Chen et al., 2024 - Google Patents
A novel time series forecasting model for capacity degradation path prediction of lithium-ion battery packChen et al., 2024
- Document ID
- 2479985950060278033
- Author
- Chen X
- Yang Y
- Sun J
- Deng Y
- Yuan Y
- Publication year
- Publication venue
- The Journal of Supercomputing
External Links
Snippet
Monitoring battery health is critical for electric vehicle maintenance and safety. However, existing research has limited focus on predicting capacity degradation paths for entire battery packs, representing a gap between literature and application. This paper proposes a …
- 238000006731 degradation reaction 0 title abstract description 50
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yao et al. | Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data | |
Gao et al. | Machine learning toward advanced energy storage devices and systems | |
Tian et al. | Flexible battery state of health and state of charge estimation using partial charging data and deep learning | |
Khan et al. | Batteries state of health estimation via efficient neural networks with multiple channel charging profiles | |
Yao et al. | Remaining useful life prediction of lithium-ion batteries using a hybrid model | |
Li et al. | Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles | |
Zhao et al. | Battery prognostics and health management from a machine learning perspective | |
Cui et al. | A dynamic spatial-temporal attention-based GRU model with healthy features for state-of-health estimation of lithium-ion batteries | |
Yao et al. | A novel graph-based framework for state of health prediction of lithium-ion battery | |
Yu et al. | Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter | |
Naaz et al. | A generative adversarial network‐based synthetic data augmentation technique for battery condition evaluation | |
Fei et al. | A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data | |
Deng et al. | Rapid health estimation of in-service battery packs based on limited labels and domain adaptation | |
Jia et al. | State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer | |
Ma et al. | Deep learning-based battery state of charge estimation: Enhancing estimation performance with unlabelled training samples | |
Li et al. | A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression | |
Nair et al. | AI‐Driven Digital Twin Model for Reliable Lithium‐Ion Battery Discharge Capacity Predictions | |
Shen et al. | Transfer learning-based state of charge and state of health estimation for Li-ion batteries: A review | |
Yang et al. | A temporal convolution and gated recurrent unit network with attention for state of charge estimation of lithium-ion batteries | |
Qu et al. | Insights and reviews on battery lifetime prediction from research to practice | |
Beltran et al. | Machine learning techniques suitability to estimate the retained capacity in lithium-ion batteries from partial charge/discharge curves | |
Zhang et al. | Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries | |
Huang et al. | A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon | |
Zhang et al. | Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction | |
Li et al. | Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD |