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Remaining Useful Life Prediction of Lithium Battery Based on Sequential CNN–LSTM Method

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

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An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

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Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network

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Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine

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Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression

inferredcites →https://doi.org/10.1016/j.est.2018.12.011openalex-snapshot

Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks

inferredcites →https://doi.org/10.1016/j.ijhydene.2019.03.101openalex-snapshot

An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

inferredcites →https://doi.org/10.1016/j.isatra.2018.11.024openalex-snapshot

Adaptive relevant vector machine based RUL prediction under uncertain conditions

inferredcites →https://doi.org/10.1016/j.jpowsour.2014.06.111openalex-snapshot

A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation

inferredcites →https://doi.org/10.1016/j.jpowsour.2014.09.127openalex-snapshot

Differential thermal voltammetry for tracking of degradation in lithium-ion batteries

inferredcites →https://doi.org/10.1016/j.jpowsour.2015.12.122openalex-snapshot

Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries

inferredcites →https://doi.org/10.1016/j.jpowsour.2017.09.046openalex-snapshot

A review on battery thermal management in electric vehicle application

inferredcites →https://doi.org/10.1016/j.jpowsour.2017.11.011openalex-snapshot

Tracking degradation in lithium iron phosphate batteries using differential thermal voltammetry

inferredcites →https://doi.org/10.1016/j.jpowsour.2019.227575openalex-snapshot

Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance

inferredcites →https://doi.org/10.1016/j.jpowsour.2020.228358openalex-snapshot

Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear 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

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Remaining useful life estimation in prognostics using deep convolution neural networks

inferredcites →https://doi.org/10.1016/j.rser.2019.109254openalex-snapshot

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

inferredcites →https://doi.org/10.1038/d41586-019-01138-1openalex-snapshot

Machine-learning techniques used to accurately predict battery life

inferredcites →https://doi.org/10.1038/s41560-019-0356-8openalex-snapshot

Data-driven prediction of battery cycle life before capacity degradation

inferredcites →https://doi.org/10.1109/access.2019.2919566openalex-snapshot

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

inferredcites →https://doi.org/10.1109/access.2019.2920932openalex-snapshot

Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles

inferredcites →https://doi.org/10.1109/access.2019.2928037openalex-snapshot

Convolutional Gated Recurrent Unit–Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries

inferredcites →https://doi.org/10.1109/icmla.2018.00137openalex-snapshot

Battery Degradation Temporal Modeling Using LSTM Networks

inferredcites →https://doi.org/10.1109/icpre.2017.8390489openalex-snapshot

Prediction of lithium-ion battery remaining useful life based on hybrid data-driven method with optimized parameter

inferredcites →https://doi.org/10.1109/tmech.2020.3040010openalex-snapshot

General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries

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/10.1115/1.4042987openalex-snapshot

A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles

inferredcites →https://doi.org/10.1142/s1469026818500086openalex-snapshot

Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks

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ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.12783/dteees/iceee2018/27855openalex-snapshot

State of Health Estimation for Lithium-Ion Battery Based on Long Short Term Memory Networks

What points at this work

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

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A Novel Seeker Optimization - Particle Filtering Method for the Remaining Useful Life Prediction of Lithium-Ion Batteries Considering Capacity Recovery Effect

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A Remaining Useful Life Prediction Method for Lithium Battery Based on Dynamic Regression Selection Algorithm

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An AI-Based Method for Defining the Similarity of Cyber-Physical Systems

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Lithium-ion battery remaining useful life prediction based on dynamic filter frequency mixing learner and dual-stream Mamba

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Lithium-ion battery remaining useful life prediction based on efficient compressed sensing and interactive enhanced Mamba model

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A hybrid residual correction method with auxiliary particle filter and conditional kernel density estimation for remaining useful life prediction

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A deep feature learning method for remaining useful life prediction of drilling pumps

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Onboard health estimation approach with segment warping and trajectory self-learning for swappable lithium battery

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Probabilistic lithium-ion battery state-of-health prediction using convolutional neural networks and Gaussian process regression

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Performance optimization of lithium-ion battery based on CFD numerical simulation and deep learning algorithm

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Early-stage degradation trajectory prediction for lithium-ion batteries: A generalized method across diverse operational conditions

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A comprehensive overview of remaining useful life prediction: From traditional literature review to scientometric analysis

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Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries

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An analytical examination of the performance assessment of CNN-LSTM architectures for state-of-health evaluation of lithium-ion batteries

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Assessment of Lithium-ion Battery Controllable Capacity Based on CNN-BiLSTM-Attention

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Energy Storage Battery Multi-State Joint Estimation Based on CNN-LSTM

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Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

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Fatigue life prediction of composite bolted joints based on finite element model and machine learning

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State of Charge and State of Health Estimation of Lithium-Ion Battery Packs With Inconsistent Internal Parameters Using Dual Extended Kalman Filter

inferred← citeshttps://doi.org/10.1145/3575882.3575903openalex-snapshot

A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data

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Research on Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Variational Mode Decomposition and Improved Particle Swarm Optimization LSTM

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Integration of ARIMA and LSTM Models for Remaining Useful Life Prediction of a Water Hydraulic High-Speed On/Off Valve

inferred← citeshttps://doi.org/10.3390/batteries11060223openalex-snapshot

The Application of BiGRU-MSTA Based on Multi-Scale Temporal Attention Mechanism in Predicting the Remaining Life of Lithium-Ion Batteries

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State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review

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Fusion Technology-Based CNN-LSTM-ASAN for RUL Estimation of Lithium-Ion Batteries

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Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

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A Hybrid CNN-BiLSTM and Wiener Process-based Prediction Approach of Remaining Useful life for Rolling Bearings

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