On the public citation graph
High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering
0 signed · 40 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cta.3386openalex-snapshot
A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries
inferredcites →https://doi.org/10.1002/er.5758openalex-snapshot
A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor
inferredcites →https://doi.org/10.1002/est2.408openalex-snapshot
State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques
inferredcites →https://doi.org/10.1007/s11431-013-5431-yopenalex-snapshot
Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications
inferredcites →https://doi.org/10.1007/s42154-021-00162-0openalex-snapshot
An Innovative State-of-charge Estimation Method of Lithium-ion Battery Based on 5th-order Cubature Kalman Filter
inferredcites →https://doi.org/10.1016/j.apenergy.2016.09.010openalex-snapshot
Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.apm.2016.01.047openalex-snapshot
State of charge estimation of LiFePO4 batteries based on online parameter identification
inferredcites →https://doi.org/10.1016/j.energy.2018.06.113openalex-snapshot
Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter
inferredcites →https://doi.org/10.1016/j.energy.2019.116538openalex-snapshot
Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.energy.2022.124612openalex-snapshot
SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture
inferredcites →https://doi.org/10.1016/j.ensm.2022.06.007openalex-snapshot
Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning
inferredcites →https://doi.org/10.1016/j.est.2021.102559openalex-snapshot
Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter
inferredcites →https://doi.org/10.1016/j.est.2022.105474openalex-snapshot
State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm
inferredcites →https://doi.org/10.1016/j.est.2022.105830openalex-snapshot
State of charge estimation for lithium-ion batteries based on improved barnacle mating optimizer and support vector machine
inferredcites →https://doi.org/10.1016/j.jpowsour.2021.229913openalex-snapshot
Effects of open-circuit voltage tests and models on state-of-charge estimation for batteries in highly variable temperature environments: Study case nano-satellites
inferredcites →https://doi.org/10.1049/joe.2018.9385openalex-snapshot
Research on SOC fuzzy weighted algorithm based on GA‐BP neural network and ampere integral method
inferredcites →https://doi.org/10.1049/joe.2019.1214openalex-snapshot
SOC estimation algorithm of power lithium battery based on AFSA‐BP neural network
inferredcites →https://doi.org/10.1080/09540091.2021.1978930openalex-snapshot
State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models
inferredcites →https://doi.org/10.1080/09540091.2022.2118675openalex-snapshot
Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction
inferredcites →https://doi.org/10.1109/access.2022.3187193openalex-snapshot
Adaptive Integral Correction-Based State of Charge Estimation Strategy for Lithium-Ion Cells
inferredcites →https://doi.org/10.1109/tte.2020.3041604openalex-snapshot
Cross-Domain State-of-Charge Estimation of Li-Ion Batteries Based on Deep Transfer Neural Network With Multiscale Distribution Adaptation
inferredcites →https://doi.org/10.1177/0020294019827338openalex-snapshot
An equivalent circuit model analysis for the lithium-ion battery pack in pure electric vehicles
inferredcites →https://doi.org/10.17775/cseejpes.2019.03060openalex-snapshot
Recent progress and future trends on state of charge estimation methods to improve battery-storage efficiency: A review
inferredcites →https://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
inferredcites →https://doi.org/10.20964/2022.02.03openalex-snapshot
State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries
inferredcites →https://doi.org/10.20964/2022.02.18openalex-snapshot
A Variable-length scale Parameter Dependent State of Charge Estimation of Lithium Ion Batteries by Kalman Filters
inferredcites →https://doi.org/10.3389/fenrg.2022.933240openalex-snapshot
A Multi-Scale Time Method for the State of Charge and Parameter Estimation of Lithium-Ion Batteries Using MIUKF-EKF
inferredcites →https://doi.org/10.3390/en11113123openalex-snapshot
Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery
inferredcites →https://doi.org/10.3390/en12040757openalex-snapshot
Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods
inferredcites →https://doi.org/10.3390/en13071811openalex-snapshot
Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage
inferredcites →https://doi.org/10.3390/en13164197openalex-snapshot
State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter
inferredcites →https://doi.org/10.3390/en16010132openalex-snapshot
Joint SOH-SOC Estimation Model for Lithium-Ion Batteries Based on GWO-BP Neural Network
inferredcites →https://doi.org/10.3390/en16052155openalex-snapshot
State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm
inferredcites →https://doi.org/10.3390/machines10100912openalex-snapshot
Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
inferredcites →https://doi.org/10.3390/pr9081412openalex-snapshot
Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter
inferredcites →https://doi.org/10.6113/jpe.2018.18.3.910openalex-snapshot
Enhanced Coulomb Counting Method for State-of-Charge Estimation of Lithium-ion Batteries based on Peukert’s Law and Coulombic Efficiency
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.4672156openalex-extracted
An Improved K-Means Clustering-Genetic Backpropagation Algorithm for State of Charge Estimation of Lithium-Ion Batteries Adaptive to Extreme Low Temperatures
inferred← citeshttps://doi.org/10.1002/cta.4138openalex-snapshot
Battery lumped fractional‐order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time‐varying core temperature conditions
inferred← citeshttps://doi.org/10.1109/ies67184.2025.11161357openalex-snapshot
Impact of Kalman Filter on Backpropagation Neural Network for State of Charge Estimation in Buoy Batteries
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.