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A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve

https://doi.org/10.1177/01423312211040901
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apenergy.2008.11.021openalex-snapshot
Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.apenergy.2011.08.002openalex-snapshot
A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation
inferredcites →https://doi.org/10.1016/j.apenergy.2016.01.125openalex-snapshot
On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis
inferredcites →https://doi.org/10.1016/j.apenergy.2016.07.126openalex-snapshot
State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115895openalex-snapshot
Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter
inferredcites →https://doi.org/10.1016/j.egypro.2017.03.976openalex-snapshot
An Online Model-based Battery Parameter and State Estimation Method Using Multi-scale Dual Adaptive Particle Filters
inferredcites →https://doi.org/10.1016/j.energy.2016.12.004openalex-snapshot
A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework
inferredcites →https://doi.org/10.1016/j.jpowsour.2010.07.071openalex-snapshot
Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.03.158openalex-snapshot
Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.11.135openalex-snapshot
Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter
inferredcites →https://doi.org/10.1016/j.jpowsour.2015.01.005openalex-snapshot
A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter
inferredcites →https://doi.org/10.1016/j.jpowsour.2016.07.065openalex-snapshot
A novel state of health estimation method of Li-ion battery using group method of data handling
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.03.015openalex-snapshot
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.10.019openalex-snapshot
Towards a smarter battery management system: A critical review on battery state of health monitoring methods
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.12.001openalex-snapshot
An evolutionary framework for lithium-ion battery state of health estimation
inferredcites →https://doi.org/10.1016/j.jpowsour.2020.228450openalex-snapshot
A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm
inferredcites →https://doi.org/10.1016/j.jpowsour.2021.230063openalex-snapshot
State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network
inferredcites →https://doi.org/10.1016/j.microrel.2013.03.010openalex-snapshot
Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression
inferredcites →https://doi.org/10.1016/j.neucom.2017.05.063openalex-snapshot
Remaining useful life estimation of engineered systems using vanilla LSTM neural networks
inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot
Deep learning in neural networks: An overview
inferredcites →https://doi.org/10.1016/j.rser.2020.110015openalex-snapshot
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
inferredcites →https://doi.org/10.1109/mim.2008.4579269openalex-snapshot
Prognostics in Battery Health Management
inferredcites →https://doi.org/10.1109/tie.2014.2341576openalex-snapshot
Lyapunov-Based Adaptive State of Charge and State of Health Estimation for Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tie.2017.2782224openalex-snapshot
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
inferredcites →https://doi.org/10.1109/tie.2018.2880703openalex-snapshot
A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain
inferredcites →https://doi.org/10.1109/tpel.2021.3075558openalex-snapshot
Health Prognosis With Optimized Feature Selection for Lithium-Ion Battery in Electric Vehicle Applications
inferredcites →https://doi.org/10.1109/tvt.2018.2805189openalex-snapshot
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1177/0142331208092030openalex-snapshot
Comparison of prognostic algorithms for estimating remaining useful life of batteries
inferredcites →https://doi.org/10.1177/0142331220961576openalex-snapshot
Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter
inferredcites →https://doi.org/10.3390/en9020100openalex-snapshot
Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4132302openalex-extracted
inferred← citeshttps://doi.org/10.1007/s11581-025-06439-2openalex-snapshot
SOH estimation of lithium battery based on improved quantum particle swarm optimization hybrid neural network
inferred← citeshttps://doi.org/10.1016/j.egyr.2023.01.109openalex-snapshot
HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery
inferred← citeshttps://doi.org/10.1016/j.energy.2022.125278openalex-snapshot
A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena
inferred← citeshttps://doi.org/10.1016/j.jechem.2023.03.026openalex-snapshot
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
inferred← citeshttps://doi.org/10.1109/phm-hangzhou58797.2023.10482710openalex-snapshot
A Transformer-CNN Based Transferable Model for State-of-Health Prediction of Lithium-ion Batteries
inferred← citeshttps://doi.org/10.1177/01423312221114506openalex-snapshot
Remaining useful life prediction for lithium-ion batteries in later period based on a fusion model
inferred← citeshttps://doi.org/10.1177/01423312221119065openalex-snapshot
A review on fault-tolerant control strategies for lithium-ion battery systems
inferred← citeshttps://doi.org/10.21203/rs.3.rs-6202767/v1openalex-snapshot
SOH estimation of lithium battery based on improved quantum particle swarm optimization hybrid neural network
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