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A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

https://doi.org/10.1002/cta.3339
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What this work says

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

inferredcites →https://doi.org/10.1002/cta.3115openalex-snapshot
A novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method for accurate state‐of‐charge and state‐of‐health co‐estimation of lithium‐ion batteries
inferredcites →https://doi.org/10.1002/er.5166openalex-snapshot
Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model
inferredcites →https://doi.org/10.1002/er.5374openalex-snapshot
Lithium‐ion battery SOC / SOH adaptive estimation via simplified single particle model
inferredcites →https://doi.org/10.1002/er.6615openalex-snapshot
A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries
inferredcites →https://doi.org/10.1002/er.6700openalex-snapshot
A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries
inferredcites →https://doi.org/10.1002/er.6719openalex-snapshot
A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation
inferredcites →https://doi.org/10.1002/er.7175openalex-snapshot
Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis
inferredcites →https://doi.org/10.1007/s11581-020-03716-0openalex-snapshot
The multi-innovation extended Kalman filter algorithm for battery SOC estimation
inferredcites →https://doi.org/10.1007/s11581-021-04165-zopenalex-snapshot
State estimation of lithium polymer battery based on Kalman filter
inferredcites →https://doi.org/10.1007/s12239-021-0116-1openalex-snapshot
Effects of Battery Model on the Accuracy of Battery SOC Estimation Using Extended Kalman Filter under Practical Vehicle Conditions Including Parasitic Current Leakage and Diffusion Of Voltage
inferredcites →https://doi.org/10.1007/s13369-018-3200-8openalex-snapshot
A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems
inferredcites →https://doi.org/10.1007/s43236-020-00146-zopenalex-snapshot
Novel reduced-order modeling method combined with three-particle nonlinear transform unscented Kalman filtering for the battery state-of-charge estimation
inferredcites →https://doi.org/10.1016/j.apenergy.2019.114324openalex-snapshot
A framework for state-of-charge and remaining discharge time prediction using unscented particle filter
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114569openalex-snapshot
Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering
inferredcites →https://doi.org/10.1016/j.conengprac.2018.09.010openalex-snapshot
Lithium-ion battery state of charge estimation with model parameters adaptation using H ∞ extended Kalman filter
inferredcites →https://doi.org/10.1016/j.energy.2017.10.043openalex-snapshot
Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique
inferredcites →https://doi.org/10.1016/j.energy.2018.10.133openalex-snapshot
Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation
inferredcites →https://doi.org/10.1016/j.energy.2020.117664openalex-snapshot
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
inferredcites →https://doi.org/10.1016/j.etran.2020.100064openalex-snapshot
A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems
inferredcites →https://doi.org/10.1016/j.jmsy.2021.11.006openalex-snapshot
Digital twin and cloud-side-end collaboration for intelligent battery management system
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/10.1049/pel2.12129openalex-snapshot
State‐of‐charge estimation technique for lithium‐ion batteries by means of second‐order extended Kalman filter and equivalent circuit model: Great temperature robustness state‐of‐charge estimation
inferredcites →https://doi.org/10.1049/pel2.12175openalex-snapshot
SOC estimation for lithium‐ion batteries based on a novel model
inferredcites →https://doi.org/10.1080/15435075.2019.1671423openalex-snapshot
A hybrid observer for SOC estimation of lithium-ion battery based on a coupled electrochemical-thermal model
inferredcites →https://doi.org/10.1109/access.2020.3031327openalex-snapshot
Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment
inferredcites →https://doi.org/10.1109/jestpe.2020.3034362openalex-snapshot
Optimal SoC Estimation Considering Hysteresis Effect for Effective Battery Management in Shipboard Batteries
inferredcites →https://doi.org/10.1109/tie.2017.2750629openalex-snapshot
SOC Estimation-Based Quasi-Sliding Mode Control for Cell Balancing in Lithium-Ion Battery Packs
inferredcites →https://doi.org/10.1109/tie.2019.2949534openalex-snapshot
Joint SoC and SoH Estimation for Zinc–Nickel Single-Flow Batteries
inferredcites →https://doi.org/10.1109/tii.2020.2974907openalex-snapshot
Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tpel.2017.2670081openalex-snapshot
A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation
inferredcites →https://doi.org/10.1109/tpel.2020.3041876openalex-snapshot
SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network
inferredcites →https://doi.org/10.1109/tpel.2021.3086555openalex-snapshot
A Fault-Tolerant SoC Estimation Method for Series–Parallel Connected Li-Ion Battery Pack
inferredcites →https://doi.org/10.1109/tsg.2018.2867017openalex-snapshot
Estimation of Cell SOC Evolution and System Performance in Module-Based Battery Charge Equalization Systems
inferredcites →https://doi.org/10.1109/tvt.2021.3085006openalex-snapshot
Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments
inferredcites →https://doi.org/10.1155/2020/9502605openalex-snapshot
Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm
inferredcites →https://doi.org/10.20964/2020.05.41openalex-snapshot
State of Charge Estimation for Lithium-Ion Battery Models Based on a Thermoelectric Coupling Model
inferredcites →https://doi.org/10.20964/2020.09.84openalex-snapshot
State of Charge (SOC) Estimation of Lithium-ion Battery Based on Adaptive Square Root Unscented Kalman Filter
inferredcites →https://doi.org/10.3906/elk-1912-42openalex-snapshot
A novel method for SOC estimation of Li-ion batteries using a hybrid machine learning technique

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.4753914openalex-extracted
Lithium Battery SOC Estimation Based on Improved Vffrls-Aekf
inferred← citeshttps://doi.org/10.1002/asjc.2968openalex-snapshot
An improved adaptive FastSLAM algorithm with time‐varying noise estimator
inferred← citeshttps://doi.org/10.1007/s10008-023-05514-wopenalex-snapshot
An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries
inferred← citeshttps://doi.org/10.1007/s11581-024-05749-1openalex-snapshot
An optimized quantum particle swarm optimization–extended Kalman filter algorithm for the online state of charge estimation of high-capacity lithium-ion batteries under varying temperature conditions
inferred← citeshttps://doi.org/10.1007/s11581-026-07049-2openalex-snapshot
Improved dung beetle-temporal convolutional network-bidirectional gated recurrent unit for high-precision state of charge estimation of lithium-ion batteries
inferred← citeshttps://doi.org/10.1007/s13369-024-08766-4openalex-snapshot
Remaining Useful Life Prediction of Super-Capacitors in Electric Vehicles Using Neural Networks
inferred← citeshttps://doi.org/10.1016/b978-0-443-16160-5.00011-1openalex-snapshot
Collaborative energy and peak power status estimation
inferred← citeshttps://doi.org/10.1016/b978-0-443-16160-5.00017-2openalex-snapshot
Aging characteristics of lithium-ion batteries
inferred← citeshttps://doi.org/10.1016/j.apenergy.2026.128551openalex-snapshot
State of energy estimation of lithium-ion batteries based on Bayesian-optimized bidirectional temporal convolutional neural network with ECM-based feature extraction and bidirectional filtering
inferred← citeshttps://doi.org/10.1016/j.energy.2026.142118openalex-snapshot
High precision state-of-charge estimation framework based on extended long short-term memory network under dynamic vehicle-to-grid conditions
inferred← citeshttps://doi.org/10.1016/j.est.2024.111552openalex-snapshot
An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles
inferred← citeshttps://doi.org/10.1016/j.est.2024.112907openalex-snapshot
An analysis and evaluation of state estimation methods for lithium-ion batteries regarding requirements of 48 V HEV applications
inferred← citeshttps://doi.org/10.1016/j.est.2024.113392openalex-snapshot
Lithium battery state of charge estimation based on improved variable forgetting factor recursive least squares method and adaptive Kalman filter joint algorithm
inferred← citeshttps://doi.org/10.1016/j.est.2024.114444openalex-snapshot
An improved progressive window-strong tracking multiple fading algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries under variable temperatures
inferred← citeshttps://doi.org/10.1016/j.est.2025.115346openalex-snapshot
A review of battery SOC estimation based on equivalent circuit models
inferred← citeshttps://doi.org/10.1016/j.est.2026.121600openalex-snapshot
Hybrid models for predicting the state of health of lithium-ion batteries: A comprehensive review
inferred← citeshttps://doi.org/10.1088/2631-8695/ae62deopenalex-snapshot
State of charge estimation for lithium-ion batteries based on grey wolf optimizer and extended Kalman filter
inferred← citeshttps://doi.org/10.1109/neessc59976.2023.10349263openalex-snapshot
An improved robust function correction-adaptive extended kalman filtering algorithm for SOC estimation of lithium-ion batteries
inferred← citeshttps://doi.org/10.1149/1945-7111/acaa5bopenalex-snapshot
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/10.1155/2024/4725806openalex-snapshot
State of Charge and State of Health Coestimation for Lithium‐Ion Capacitor Based on Multi‐innovation Filters
inferred← citeshttps://doi.org/10.3390/batteries10110388openalex-snapshot
State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm
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