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State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Multifunctional Estimation and Analysis of Lithium-Ion Battery State Based on Data Model Fusion under Multiple Constraints
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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter
inferred← citeshttps://doi.org/10.1149/1945-7111/acd148openalex-snapshot
Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs
inferred← citeshttps://doi.org/10.1149/1945-7111/acd303openalex-snapshot
High-Precision State of Charge Estimation of Urban-Road-Condition Lithium-Ion Batteries Based on Optimized High-Order Term Compensation-Adaptive Extended Kalman Filtering
inferred← citeshttps://doi.org/10.1149/1945-7111/ace555openalex-snapshot
An Improved Comprehensive Learning - Particle Swarm Optimization - Extended Kalman Filtering Method for the Online High-Precision State of Charge and Model Parameter Co-Estimation of Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.1149/1945-7111/ace65bopenalex-snapshot
Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries
inferred← citeshttps://doi.org/10.1149/1945-7111/ae3e19openalex-snapshot
Fusing Temperature-Guided Attention with LFGWO-Optimized CNN-BiLSTM for Accurate Battery SOC Estimation
inferred← citeshttps://doi.org/10.1155/2022/3920317openalex-snapshot
SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron
inferred← citeshttps://doi.org/10.1371/journal.pone.0350331openalex-snapshot
Estimation of battery SOC using a combined approach of temporal convolutional networks and Unscented Kalman Filter
inferred← citeshttps://doi.org/10.20964/2021.07.68openalex-snapshot
A Novel Fading Memory Square Root UKF Algorithm for the High-precision State of Charge Estimation of High-power Lithium-ion Batteries
inferred← citeshttps://doi.org/10.20964/2021.08.26openalex-snapshot
A Novel Joint Support Vector Machine - Cubature Kalman Filtering Method for Adaptive State of Charge Prediction of Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.20964/2021.09.34openalex-snapshot
A Novel Fading Memory Recursive Least Square Method (FMLS) for Accurate State of Charge Estimation of Lithium-ion Batteries Combined with Improved Second Order PNGV Modeling
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3954387/v1openalex-snapshot
Accurate Estimation for State of Charge (SoC) using different topologies of LSTM
inferred← citeshttps://doi.org/10.2139/ssrn.6579710openalex-snapshot
Aging-Aware Adaptive Battery State Estimation for LiFePO₄ Modules: Full-Lifespan Validation and Kalman Filter Comparative Analysis
inferred← citeshttps://doi.org/10.2139/ssrn.6843709openalex-snapshot
Aging-Aware Adaptive Battery State Estimation for LiFePO₄ Modules: Full-Lifespan Validation and Kalman Filter Comparative Analysis
inferred← citeshttps://doi.org/10.2139/ssrn.7199404openalex-snapshot
Temperature-Gated Causal Multi-Scale Physics-Informed Network for Lithium-Ion Battery State-of-Charge Estimation
inferred← citeshttps://doi.org/10.2139/ssrn.7386834openalex-snapshot
State-of-Charge Estimation of Lithium-Ion Batteries Using a Transformer-Kalman Filter with UCB-Based Adaptive Noise Tuning
inferred← citeshttps://doi.org/10.23919/chain.2024.000002openalex-snapshot
Multi-Scenario Deep Learning-Based Framework to Estimate the Remaining Charge Time of Lithium-Ion Power Batteries
inferred← citeshttps://doi.org/10.3389/fenrg.2022.984107openalex-snapshot
State-of-charge estimation method for lithium-ion batteries based on competitive SIR model
inferred← citeshttps://doi.org/10.3390/app131910910openalex-snapshot
Influence of Time-Series Length and Hyperparameters on Temporal Convolutional Neural Network Training in Low-Power Battery SOC Estimation
inferred← citeshttps://doi.org/10.3390/app14146240openalex-snapshot
Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
inferred← citeshttps://doi.org/10.3390/app14209245openalex-snapshot
State of Charge Estimation for Lithium-Ion Batteries Using Optimized Model Based on Optimal HPPC Conditions Created Using Taguchi Method and Multi-Objective Optimization
inferred← citeshttps://doi.org/10.3390/batteries12080294openalex-snapshot
SOH Estimation of Lithium-Ion Batteries Using a Residual Multilayer Perceptron-Based, Physics-Informed Neural Network for the Battery Management System
inferred← citeshttps://doi.org/10.3390/batteries12100420openalex-snapshot
A Physics-Informed TCN-LSTM Approach for Interpretable Internal Temperature Estimation of NMC811 Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.3390/batteries8100145openalex-snapshot
State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles under Extreme Operating Temperatures Based on an Adaptive Temporal Convolutional Network
inferred← citeshttps://doi.org/10.3390/electronics13101964openalex-snapshot
A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation
inferred← citeshttps://doi.org/10.3390/electronics15091968openalex-snapshot
Equivalent Circuit Models for Lithium-Ion Batteries: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/en15103835openalex-snapshot
Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network
inferred← citeshttps://doi.org/10.3390/en17112686openalex-snapshot
Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves
inferred← citeshttps://doi.org/10.3390/forecast5030032openalex-snapshot
Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
inferred← citeshttps://doi.org/10.3390/machines10100912openalex-snapshot
Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
inferred← citeshttps://doi.org/10.3390/pr12102166openalex-snapshot
A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.3390/s23052777openalex-snapshot
Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication
inferred← citeshttps://doi.org/10.3390/s23125457openalex-snapshot
A Novel Fusion Method for State-of-Charge Estimation of Lithium-Ion Batteries Based on Improved Genetic Algorithm BP and Adaptive Extended Kalman Filter
inferred← citeshttps://doi.org/10.3390/s26010264openalex-snapshot
State-of-Charge Estimation of Lithium-Ion Batteries Based on the CNN-Bi-LSTM-AM Model Under Low-Temperature Environments
inferred← citeshttps://doi.org/10.3390/su18189337openalex-snapshot
A Study on SOC Estimation for Lithium-Ion Batteries Based on the FFRLS-PSO-WMIUKF Algorithm
inferred← citeshttps://doi.org/10.3390/wevj16100572openalex-snapshot
State of Charge (SoC) Accurate Estimation Using Different Models of LSTM
inferred← citeshttps://doi.org/10.36306/konjes.1554945openalex-snapshot
AN ADAPTIVE AND HYBRID STATE OF CHARGE ESTIMATION METHOD INTEGRATING SEQUENCE-TO-POINT LEARNING AND COULOMB COUNTING FOR LI-ION BASED ENERGY STORAGE SYSTEMS
inferred← citeshttps://doi.org/10.46855/energy-proceedings-9229openalex-snapshot
TCRDN: Allied Temporal Convolution-Recurrent Diagnosis Network for the Thermal Health Management of Lithium-ion Energy System
inferred← citeshttps://doi.org/10.54097/fcis.v4i3.11146openalex-snapshot
SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm
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