SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring | Paper Citation Record

Observation 2015fa9f-f86f-47b4-8a40-69ef6977b37a · outbound

This paper cites Progress in battery thermal management systems technologies for electric vehicles.Renewable and Sustainable Energy Reviews , 202:114654, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Progress in battery thermal management systems technologies for electric vehicles.Renewable and Sustainable Energy Reviews , 202:114654, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.960850Z

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Observation 58ae77f8-31d6-4260-9f8a-f4fe91315e27 · outbound

This paper cites Anonlinestateofchargeestimationforlithium-ionand supercapacitor in hybrid electric drive vehicle.Journal of Energy Storage, 26:100946, 2019.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Anonlinestateofchargeestimationforlithium-ionand supercapacitor in hybrid electric drive vehicle.Journal of Energy Storage, 26:100946, 2019

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.951857Z

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Observation 29565263-e8b1-42c7-a60d-8a32a1323e72 · outbound

This paper cites Optimizing electric vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Optimizing electric vehicles efficiency with hybrid energy storage: Comparative analysis of rule-based and neural network power management systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.942422Z

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Observation e467c497-183a-4990-88dc-b61e69243455 · outbound

This paper cites Lithiuminventorytrackingasanon-destructive battery evaluation and monitoring method.Nature Energy, 9(5):612–621, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Lithiuminventorytrackingasanon-destructive battery evaluation and monitoring method.Nature Energy, 9(5):612–621, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.933442Z

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Observation 9a6d8e99-eaac-4d17-a51e-41cbc97c38c7 · outbound

This paper cites Comparisonstudybetweenhybridnelder-mead particleswarmoptimizationandopencircuitvoltage—recursiveleastsquareforthebatteryparametersestimation.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Comparisonstudybetweenhybridnelder-mead particleswarmoptimizationandopencircuitvoltage—recursiveleastsquareforthebatteryparametersestimation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.924156Z

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Observation 1d1e6126-b22b-4162-a10c-5da83795aaf5 · outbound

This paper cites Gandoman, Joris Jaguemont, Shovon Goutam, Rahul Gopalakrishnan, Yousef Firouz, Theodoros Kalogiannis, Noshin Omar, and Joeri Van Mierlo.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Gandoman, Joris Jaguemont, Shovon Goutam, Rahul Gopalakrishnan, Yousef Firouz, Theodoros Kalogiannis, Noshin Omar, and Joeri Van Mierlo

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.914807Z

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Observation 2770a090-921d-4b5f-a5b2-79766ee6897f · outbound

This paper cites Areviewofthestateofhealthforlithium-ionbatteries:Researchstatusandsuggestions.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Areviewofthestateofhealthforlithium-ionbatteries:Researchstatusandsuggestions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.905640Z

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Observation 8df90e3a-183d-4ee4-89ef-fa89911e69c6 · outbound

This paper cites Towards machine-learning driven prognostics and health management of li-ion batteries.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Towards machine-learning driven prognostics and health management of li-ion batteries

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.896583Z

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Observation 93098fec-49ee-4d20-87ee-f7a5ee0e158b · outbound

This paper cites Seol et al.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Seol et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.887293Z

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Observation bb862841-69a7-4bb0-a506-61c2ccdff847 · outbound

This paper cites Batterysohestimationmethodbased on gradual decreasing current, double correlation analysis and gru.Green Energy and Intelligent Transportation, 2(5):100108, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Batterysohestimationmethodbased on gradual decreasing current, double correlation analysis and gru.Green Energy and Intelligent Transportation, 2(5):100108, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.878434Z

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Observation 366dd515-538b-421c-8fd5-80678396220b · outbound

This paper cites Zheng and X.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Zheng and X

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.869326Z

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Observation 02ecab8c-ee2a-40b6-a46c-9c2fc3b57522 · outbound

This paper cites A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented kalman filter.Energy Reports, 8:426–436, 2022.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented kalman filter.Energy Reports, 8:426–436, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.860495Z

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Observation df6aa9ed-a982-4743-a7f2-cdbadb270955 · outbound

This paper cites Nuroldayeva et al.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Nuroldayeva et al

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.851469Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c9585f09-4075-402e-80e4-7075b61f9ef0 · outbound

This paper cites A novel graph-based framework for state of health prediction of lithium-ion battery.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A novel graph-based framework for state of health prediction of lithium-ion battery

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.842779Z

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Observation 2e201d5d-d89b-4c25-9c86-59d82fb84a9f · outbound

This paper cites Liang et al.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Liang et al

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.833851Z

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Observation f3bf13c2-0379-4b8f-97c6-dc1af52ed9f4 · outbound

This paper cites A data-driven approach with uncertainty quantification forpredictingfuturecapacitiesandremainingusefullifeoflithium-ionbattery.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A data-driven approach with uncertainty quantification forpredictingfuturecapacitiesandremainingusefullifeoflithium-ionbattery

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.825088Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3feb29b7-93e6-4ad2-83dd-2e5e57bb0502 · outbound

This paper cites A review of battery state of health estimation methods: Hybrid electric vehicle challenges.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A review of battery state of health estimation methods: Hybrid electric vehicle challenges

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.816160Z

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Observation 190dbbe1-5463-42cd-b8f5-098fc26f2a28 · outbound

This paper cites State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.807467Z

Source-reported events for the cited work

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Observation 64fc7033-b059-40d0-aa3d-aa731b309f3d · outbound

This paper cites State-of-health estimation for lithium-ion batteries based on kullback–leibler divergence and a retentive network.Applied Energy, 376:124266, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State-of-health estimation for lithium-ion batteries based on kullback–leibler divergence and a retentive network.Applied Energy, 376:124266, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.798539Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c0bc81c1-a687-4de4-a99f-4e9ef33a8710 · outbound

This paper cites A novel method for state of health estimation of lithium-ion batteries based on improved lstm and health indicators extraction.Energy, 251:123973, 2022.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A novel method for state of health estimation of lithium-ion batteries based on improved lstm and health indicators extraction.Energy, 251:123973, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.789606Z

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Observation a091e3a8-7163-40a4-9be2-8c34bdbbc7b5 · outbound

This paper cites Data-drivenpredictionofbatterycyclelifebeforecapacitydegradation.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Data-drivenpredictionofbatterycyclelifebeforecapacitydegradation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.780695Z

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Observation 23c12667-ba97-4fc8-a677-d41b61fc6f92 · outbound

This paper cites Analyzingelectricvehiclebatteryhealthperformanceusingsupervisedmachinelearning.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Analyzingelectricvehiclebatteryhealthperformanceusingsupervisedmachinelearning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.771758Z

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Observation 2b3cd5ed-328a-4475-acb7-21e70757c850 · outbound

This paper cites Lithium-ionbatterydigitalization:Combiningphysics-basedmodels and machine learning.Renewable and Sustainable Energy Reviews, 200:114577, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Lithium-ionbatterydigitalization:Combiningphysics-basedmodels and machine learning.Renewable and Sustainable Energy Reviews, 200:114577, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.763497Z

Source-reported events for the cited work

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Observation d3598c3a-9db6-4636-9bc1-107bf8aac5b9 · outbound

This paper cites Anoveldeeplearningframeworkforstateofhealthestimationoflithium-ion battery.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Anoveldeeplearningframeworkforstateofhealthestimationoflithium-ion battery

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.754542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1981cc2-d064-42c4-abc4-e6794a0862e1 · outbound

This paper cites Prognostics and health management of lithium-ion battery using deep learning methods: A review.Renewable and sustainable energy reviews, 161:112282, 2022.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Prognostics and health management of lithium-ion battery using deep learning methods: A review.Renewable and sustainable energy reviews, 161:112282, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.745678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58d530e5-488b-4f51-b972-567a48f57307 · outbound

This paper cites Early prediction of remaining useful life for lithium-ion batteries based on ceemdan-transformer-dnn hybrid model.Heliyon, 9(7), 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Early prediction of remaining useful life for lithium-ion batteries based on ceemdan-transformer-dnn hybrid model.Heliyon, 9(7), 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.736608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 43ffc201-7de4-43c1-a0b7-c1bce8b7cdeb · outbound

This paper cites Deeplearningtoestimatelithium-ionbatterystateofhealthwithout additional degradation experiments.Nature Communications, 14(1):2760, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Deeplearningtoestimatelithium-ionbatterystateofhealthwithout additional degradation experiments.Nature Communications, 14(1):2760, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.727341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b8e965be-5c1b-476c-a6f9-d17554db5c24 · outbound

This paper cites Hybrid deep neural network with dimension attention for state-of-health estimation of lithium-ion batteries.Energy, 278:127734, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Hybrid deep neural network with dimension attention for state-of-health estimation of lithium-ion batteries.Energy, 278:127734, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.717693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5cc27660-5b62-4a66-929c-2fed63e444e8 · outbound

This paper cites Stateofhealthpredictionforli-ion batteries with end-to-end deep learning.Journal of Energy Storage, 65:107218, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Stateofhealthpredictionforli-ion batteries with end-to-end deep learning.Journal of Energy Storage, 65:107218, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.708696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b295cc6-1474-4110-97ea-bec059d06c24 · outbound

This paper cites State of health estimation of lithium-ion batteries using support vector regression and long short-term memory.Open Journal of Applied Sciences, 12(8):1366–1382, 2022.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State of health estimation of lithium-ion batteries using support vector regression and long short-term memory.Open Journal of Applied Sciences, 12(8):1366–1382, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.699510Z

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Observation 05328751-6e5f-4552-9363-41f66e880175 · outbound

This paper cites Guerrero.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Guerrero

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.690589Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4844b53e-ad6d-4328-beed-64ec31ad05c1 · outbound

This paper cites Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis.Nature Communications, 15(1):4332, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis.Nature Communications, 15(1):4332, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.682531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1d48158-ba3d-4d21-a054-6022e713b469 · outbound

This paper cites Boostingbatterystateofhealthestimationbasedonself-supervisedlearning.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Boostingbatterystateofhealthestimationbasedonself-supervisedlearning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.673736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b3e8e3dd-05a9-4678-8c46-37524e4cffeb · outbound

This paper cites Expert Systems with Applications, 238:122041, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Expert Systems with Applications, 238:122041, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.665452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a0a149ab-9135-4d65-991f-fc2e07376666 · outbound

This paper cites Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.656908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7551796a-a9c2-407b-aa8f-bcecb86e88ed · outbound

This paper cites Stateofhealthestimationforlithium-ionbatteries based on hybrid attention and deep learning.Reliability Engineering & System Safety, 232:109066, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Stateofhealthestimationforlithium-ionbatteries based on hybrid attention and deep learning.Reliability Engineering & System Safety, 232:109066, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.648378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e67c147d-bd8d-450b-b89e-efcf417c158e · outbound

This paper cites State-of-health estimation of lithium-ion batteries: A comprehensive literature review from cell to pack levels.Energy Conversion and Economics, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State-of-health estimation of lithium-ion batteries: A comprehensive literature review from cell to pack levels.Energy Conversion and Economics, 2024

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.639601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d562acc3-ac4f-452e-8eec-68cbc1a7e010 · outbound

This paper cites An overview of artificial intelligence driven li-ion battery state estimation.IoT Enabled-DC Microgrids: Architecture, Algorithms, Applications, and Technologies, page 121, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring An overview of artificial intelligence driven li-ion battery state estimation.IoT Enabled-DC Microgrids: Architecture, Algorithms, Applications, and Technologies, page 121, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.630469Z

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Observation f33da542-7428-4a77-ae28-341db5461e9f · outbound

This paper cites Reviewonmodelingandsoc/sohestimationofbatteriesforautomotiveapplications.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Reviewonmodelingandsoc/sohestimationofbatteriesforautomotiveapplications

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.621915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 003cf067-8102-4ef1-b6b4-8d147762421a · outbound

This paper cites Technologies for energy storage power stations safety operation: Battery state evaluation survey and a critical analysis.IEEE Access, 12:31334–31356, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Technologies for energy storage power stations safety operation: Battery state evaluation survey and a critical analysis.IEEE Access, 12:31334–31356, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.613750Z

Source-reported events for the cited work

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Observation 47365557-6d91-456f-8897-4c3160c67f62 · outbound

This paper cites Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T13:07:23.165316Z

Source-reported events for the cited work

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Observation a7d13514-2ae3-43ff-9f6b-212ec6faaa89 · outbound

This paper cites Advanced state-of-health estimation for lithium-ion batteries using multi-feature fusion and kan-lstm hybrid model.Batteries, 10(12):433, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Advanced state-of-health estimation for lithium-ion batteries using multi-feature fusion and kan-lstm hybrid model.Batteries, 10(12):433, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.605119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc580b73-2967-4a75-b8d9-898f9b94fa8c · outbound

This paper cites Stateofhealthestimationofli-ionbattery via incremental capacity analysis and internal resistance identification based on kolmogorov–arnold networks.Batteries, 10(9), 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Stateofhealthestimationofli-ionbattery via incremental capacity analysis and internal resistance identification based on kolmogorov–arnold networks.Batteries, 10(9), 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.596762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7155b4f0-a448-415c-970e-b02e7aff3cc4 · outbound

This paper cites A parallel weighted adtc-transformer framework with funet fusion and kan for improved lithium-ion battery soh prediction.Control Engineering Practice, 159:106302, 2025.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A parallel weighted adtc-transformer framework with funet fusion and kan for improved lithium-ion battery soh prediction.Control Engineering Practice, 159:106302, 2025

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.587658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6ceaceed-bcf8-4eef-a5ee-8c4f5f2f58e5 · outbound

This paper cites Lithium-ion battery soh estimation method based on multi-feature and cnn-bilstm-mha.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Lithium-ion battery soh estimation method based on multi-feature and cnn-bilstm-mha

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.578953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5587190d-ed17-432b-a4f2-4244daca7140 · outbound

This paper cites Online fusion estimation method for state of charge and state of health in lithium battery storage systems.AIP Advances, 13(4), 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Online fusion estimation method for state of charge and state of health in lithium battery storage systems.AIP Advances, 13(4), 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.570436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 820cc12b-3b56-4d5c-aa39-5f8166615489 · outbound

This paper cites an unresolved cited work.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:23.561979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1d5310c4-8fc1-4d23-b640-eb6eef96b5d9 · outbound

This paper cites A novel hybrid neural network-based soh and rul estimation method for lithium-ion batteries.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A novel hybrid neural network-based soh and rul estimation method for lithium-ion batteries

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.554049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d6bc0aaf-6618-4e89-8a86-b1d2c4b780fd · outbound

This paper cites an unresolved cited work.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:23.545825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1740ff79-2ed0-4d72-9637-17cf8fa847f2 · outbound

This paper cites Animprovedcnn-lstmmodel-basedstate-of-healthestimation approach for lithium-ion batteries.Energy, 276:127585, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Animprovedcnn-lstmmodel-basedstate-of-healthestimation approach for lithium-ion batteries.Energy, 276:127585, 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.537218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 81b8f283-51d8-47a6-8df1-321c20f8ce32 · outbound

This paper cites A data-driven battery soh estimation method with cnn-lstm model and ssa optimizing.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A data-driven battery soh estimation method with cnn-lstm model and ssa optimizing

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.528780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7cbe1e6-89d8-4fc2-aba9-b5b7f439d75e · outbound

This paper cites an unresolved cited work.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:23.519975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9b458c9c-d4e6-4761-a6e7-02308e3c0d35 · outbound

This paper cites Energy Reports, 9:2993–3021, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Energy Reports, 9:2993–3021, 2023

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.511714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60825bbe-0a38-41af-afd8-f34cbb94b82c · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring KAN: Kolmogorov-Arnold Networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T13:07:23.201561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb67dd8-581b-4e0a-b7a1-6941bc9fcb61 · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Sigmoid-weighted linear units for neural network function approximation in reinforcement learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.502950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9ea1b37b-88d8-47c0-94c8-e14803cadb78 · outbound

This paper cites Cvkan: Complex-valued kolmogorov-arnold networks.arXiv preprint arXiv:2502.02417, 2025.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Cvkan: Complex-valued kolmogorov-arnold networks.arXiv preprint arXiv:2502.02417, 2025

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T13:07:23.207414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 072ba241-4a1d-4bf6-bd76-d757c01fed35 · outbound

This paper cites Kolmogorov–arnold recurrent network for short term load forecasting across diverse consumers.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Kolmogorov–arnold recurrent network for short term load forecasting across diverse consumers

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.494241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 06b46031-ff3b-4af0-88bd-7d31cb2095fa · outbound

This paper cites Physics-informed kolmogorov-arnold networks for power system dynamics.IEEE Open Access Journal of Power and Energy, 2025.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Physics-informed kolmogorov-arnold networks for power system dynamics.IEEE Open Access Journal of Power and Energy, 2025

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.485677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6d233e83-e66d-44c3-a895-27e825a701be · outbound

This paper cites State-of-health prediction for lithium-ion batteries with multiple gaussian process regression model.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State-of-health prediction for lithium-ion batteries with multiple gaussian process regression model

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.476917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0df91222-3ef7-4519-9257-630b5176826b · outbound

This paper cites An optimal clustering algorithm for second use of retired ev batteries using dbscan and pca schemes considering performance deviation.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring An optimal clustering algorithm for second use of retired ev batteries using dbscan and pca schemes considering performance deviation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.467413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60b44541-3bb7-4016-ba26-51dd1b71fb22 · outbound

This paper cites A contribution-aware federated framework for electric vehicle batteries health estimation.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A contribution-aware federated framework for electric vehicle batteries health estimation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.457742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf4aad01-322c-4561-96ec-3bb70b15657d · outbound

This paper cites State of health estimation approach for li-ion batteries based on mechanism feature empowerment.Journal of Energy Storage, 84:110965, 2024.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring State of health estimation approach for li-ion batteries based on mechanism feature empowerment.Journal of Energy Storage, 84:110965, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.448527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4f0249e-2089-4e2f-ae77-3393302fc140 · outbound

This paper cites Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse bayesian learning.Energy, 282:128445, 2023.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse bayesian learning.Energy, 282:128445, 2023

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.439511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05e4b2c2-acfb-41c2-8286-bce3eefec1eb · outbound

This paper cites A cmmog-based lithium-battery soh estimation method using multi-task learning framework.Journal of Energy Storage, 107:114884, 2025.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring A cmmog-based lithium-battery soh estimation method using multi-task learning framework.Journal of Energy Storage, 107:114884, 2025

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.430252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 78ef0db3-7f85-41a7-9829-7f17978c678c · outbound

This paper cites Dgl-stfa:Predictinglithium-ionbatteryhealthwithdynamicgraphlearningandspatial–temporalfusionattention.

SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring Dgl-stfa:Predictinglithium-ionbatteryhealthwithdynamicgraphlearningandspatial–temporalfusionattention

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:23.420260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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