On the public citation graph

Remaining Useful Life Estimation for LFP Cells in Second-Life Applications

https://doi.org/10.1109/tim.2021.3055791
0 signed · 103 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.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4614-0134-6_2openalex-snapshot
Electric Vehicle Battery Technologies
inferredcites →https://doi.org/10.1016/j.est.2015.05.003openalex-snapshot
Nonlinear aging characteristics of lithium-ion cells under different operational conditions
inferredcites →https://doi.org/10.1016/j.jclepro.2018.09.149openalex-snapshot
State of health estimation of second-life LiFePO4 batteries for energy storage applications
inferredcites →https://doi.org/10.1016/j.jenvman.2019.05.095openalex-snapshot
Towards sustainable business models for electric vehicle battery second use: A critical review
inferredcites →https://doi.org/10.1016/j.jpowsour.2009.10.010openalex-snapshot
Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization
inferredcites →https://doi.org/10.1016/j.jpowsour.2015.08.091openalex-snapshot
Inline state of health estimation of lithium-ion batteries using state of charge calculation
inferredcites →https://doi.org/10.1016/j.renene.2014.11.058openalex-snapshot
Potential of lithium-ion batteries in renewable energy
inferredcites →https://doi.org/10.1016/j.rser.2018.04.035openalex-snapshot
Battery second life: Hype, hope or reality? A critical review of the state of the art
inferredcites →https://doi.org/10.1016/s0004-3702(97)00043-xopenalex-snapshot
Wrappers for feature subset selection
inferredcites →https://doi.org/10.1016/s0378-7753(01)00722-4openalex-snapshot
Aging mechanism in Li ion cells and calendar life predictions
inferredcites →https://doi.org/10.1016/s0378-7753(01)00783-2openalex-snapshot
An accelerated calendar and cycle life study of Li-ion cells
inferredcites →https://doi.org/10.1016/s0378-7753(03)00190-3openalex-snapshot
Power fade and capacity fade resulting from cycle-life testing of Advanced Technology Development Program lithium-ion batteries
inferredcites →https://doi.org/10.1016/s1088-467x(97)00008-5openalex-snapshot
Feature Selection for Classification
inferredcites →https://doi.org/10.1016/s1570-8659(04)13006-8openalex-snapshot
Modelling and Discretization of Circuit Problems
inferredcites →https://doi.org/10.1038/nclimate2564openalex-snapshot
Rapidly falling costs of battery packs for electric vehicles
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.2018.2858856openalex-snapshot
Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
inferredcites →https://doi.org/10.1109/access.2019.2905740openalex-snapshot
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Health Indicator and Gaussian Process Regression Model
inferredcites →https://doi.org/10.1109/access.2019.2917859openalex-snapshot
A Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations, Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies
inferredcites →https://doi.org/10.1109/ecce.2016.7855090openalex-snapshot
Evaluation of lithium-ion battery second life performance and degradation
inferredcites →https://doi.org/10.1109/jrproc.1955.277920openalex-snapshot
The Charging and Discharging of Nonlinear Capacitors
inferredcites →https://doi.org/10.1109/phm-chongqing.2018.00193openalex-snapshot
Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm
inferredcites →https://doi.org/10.1109/phm.2012.6228850openalex-snapshot
Prognostics of lithium-ion batteries using model-based and data-driven methods
inferredcites →https://doi.org/10.1109/smartmile.2013.6708211openalex-snapshot
Evaluation of LiFePO4 batteries for Electric Vehicle applications
inferredcites →https://doi.org/10.1109/tia.2015.2511096openalex-snapshot
Energy Storage Technologies for High-Power Applications
inferredcites →https://doi.org/10.1109/tia.2018.2801262openalex-snapshot
Technical Viability of Battery Second Life: A Study From the Ageing Perspective
inferredcites →https://doi.org/10.1109/tia.2019.2891213openalex-snapshot
Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis
inferredcites →https://doi.org/10.1109/tie.2016.2623260openalex-snapshot
Direct Remaining Useful Life Estimation Based on Support Vector Regression
inferredcites →https://doi.org/10.1109/tim.2014.2348613openalex-snapshot
An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation
inferredcites →https://doi.org/10.1109/tvt.2017.2715333openalex-snapshot
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
inferredcites →https://doi.org/10.1126/science.1212741openalex-snapshot
Electrical Energy Storage for the Grid: A Battery of Choices
inferredcites →https://doi.org/10.23919/iconac.2018.8748973openalex-snapshot
Indirect remaining useful life prognostics for lithium-ion batteries
inferredcites →https://doi.org/10.3390/en6083654openalex-snapshot
Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction
inferredcites →https://doi.org/10.3390/su11092527openalex-snapshot
Life Cycle Assessment of a Lithium Iron Phosphate (LFP) Electric Vehicle Battery in Second Life Application Scenarios

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.4049712openalex-extracted
A Novel Seeker Optimization - Particle Filtering Method for the Remaining Useful Life Prediction of Lithium-Ion Batteries Considering Capacity Recovery Effect
inferred← citeshttps://doi.org/10.1002/ente.70569openalex-snapshot
A Comprehensive Review on Hybrid Energy Storage for Effective Energy Management on Existing Topologies in Electric Vehicular Applications
inferred← citeshttps://doi.org/10.1002/er.8712openalex-snapshot
A novel cuckoo search particle filtering strategy for the remaining useful life prediction of the lithium‐ion batteries in hybrid electric vehicle
inferred← citeshttps://doi.org/10.1002/slct.202501481openalex-snapshot
Effect of Separator Type, Formation Cycle, and Fluoroethylene Carbonate on the long‐Term Cycling Performance of Lithium Metal Half‐Cells
inferred← citeshttps://doi.org/10.1007/s00202-024-02797-8openalex-snapshot
Multimonth-ahead data-driven remaining useful life prognostics of frequency regulation-battery energy storage systems in cell voltage inconsistency
inferred← citeshttps://doi.org/10.1007/s10751-026-02572-0openalex-snapshot
Integrated NSGA-II and MLP-PF model for enhanced lithium-ion battery prognostics
inferred← citeshttps://doi.org/10.1007/s43979-023-00049-5openalex-snapshot
A survey of second-life batteries based on techno-economic perspective and applications-based analysis
inferred← citeshttps://doi.org/10.1016/j.adapen.2021.100064openalex-snapshot
Electric/thermal hybrid energy storage planning for park-level integrated energy systems with second-life battery utilization
inferred← citeshttps://doi.org/10.1016/j.apenergy.2024.123542openalex-snapshot
State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges
inferred← citeshttps://doi.org/10.1016/j.apenergy.2025.126492openalex-snapshot
Modeling battery aging in linear energy system optimizations by applying convex hulls - accuracy benefits and computational costs
inferred← citeshttps://doi.org/10.1016/j.applthermaleng.2026.132916openalex-snapshot
A vision for second-life battery applications in the power sector: challenges and opportunities
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112638openalex-snapshot
Ensemble learning prediction model for lithium-ion battery remaining useful life based on embedded feature selection
inferred← citeshttps://doi.org/10.1016/j.energy.2024.132782openalex-snapshot
A capacity fade reliability model for lithium-ion battery packs based on real-vehicle data
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.105120openalex-snapshot
Image- and health indicator-based transfer learning hybridization for battery RUL prediction
inferred← citeshttps://doi.org/10.1016/j.est.2022.104376openalex-snapshot
Early prediction of cycle life for lithium-ion batteries based on evolutionary computation and machine learning
inferred← citeshttps://doi.org/10.1016/j.est.2022.106050openalex-snapshot
Particle swarm optimized data-driven model for remaining useful life prediction of lithium-ion batteries by systematic sampling
inferred← citeshttps://doi.org/10.1016/j.est.2023.109352openalex-snapshot
Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation
inferred← citeshttps://doi.org/10.1016/j.est.2024.111626openalex-snapshot
Remaining useful life prediction for lithium-ion batteries incorporating spatio-temporal information
inferred← citeshttps://doi.org/10.1016/j.est.2025.117152openalex-snapshot
Privacy-preserving federated semi-supervised learning for battery life prediction amid data scarcity
inferred← citeshttps://doi.org/10.1016/j.est.2025.117994openalex-snapshot
Data-driven classification of lithium-ion batteries for second-life applications
inferred← citeshttps://doi.org/10.1016/j.est.2025.118006openalex-snapshot
Electrochemical-acoustic coupling modeling and ultrasonic characterization of lithium iron phosphate batteries under cyclic aging
inferred← citeshttps://doi.org/10.1016/j.jclepro.2025.147120openalex-snapshot
A sustainable echelon re-utilization and recycling for dynamic end-of-life electric vehicle battery packs
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2023.233474openalex-snapshot
Battery prognostics and health management from a machine learning perspective
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2025.236620openalex-snapshot
Ultra-early prediction of lithium-ion battery cycle life based on assembled capacity curve extracted from a single cycle
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2025.238302openalex-snapshot
A flexible multi-feature transformer model for enhanced prediction of lithium-ion battery’s remaining useful life
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2026.239389openalex-snapshot
Comprehensive feature extraction for battery health prognostics: Identifying predictive indicators of state of health
inferred← citeshttps://doi.org/10.1016/j.ress.2025.111019openalex-snapshot
Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems
inferred← citeshttps://doi.org/10.1016/j.ress.2025.112144openalex-snapshot
Hybrid-feature-fusion-based stochastic estimation strategy for multi-sensor system
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2024.177748openalex-snapshot
A review on direct regeneration of spent lithium iron phosphate: From waste to wealth
inferred← citeshttps://doi.org/10.1016/j.xcrp.2024.101941openalex-snapshot
Taking second-life batteries from exhausted to empowered using experiments, data analysis, and health estimation
inferred← citeshttps://doi.org/10.1063/5.0298115openalex-snapshot
Using continuous learning to analyze and predict battery condition in real time
inferred← citeshttps://doi.org/10.1088/1742-6596/2936/1/012041openalex-snapshot
Research on LSTM-based winding state prediction method
inferred← citeshttps://doi.org/10.1088/2515-7655/acaa57openalex-snapshot
Roadmap for a sustainable circular economy in lithium-ion and future battery technologies
inferred← citeshttps://doi.org/10.1109/access.2024.3450790openalex-snapshot
Battery Passport for Second-Life Batteries: Potential Applications and Challenges
inferred← citeshttps://doi.org/10.1109/access.2024.3474089openalex-snapshot
Remaining Useful Life Estimation of Used Li-Ion Cells With Deep Learning Algorithms Without First Life Information
inferred← citeshttps://doi.org/10.1109/access.2025.3554682openalex-snapshot
A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations
inferred← citeshttps://doi.org/10.1109/cencon58932.2023.10368848openalex-snapshot
State of Charge Estimation of Second Life Batteries Using First Order Thevenin Model
inferred← citeshttps://doi.org/10.1109/icpsasia58343.2023.10294974openalex-snapshot
Optimal Control Strategy of Echelon Battery Energy Storage System Based on Battery Health State
inferred← citeshttps://doi.org/10.1109/iecon51785.2023.10312096openalex-snapshot
Two-Stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries
inferred← citeshttps://doi.org/10.1109/jestie.2026.3690670openalex-snapshot
Dual-Loop Control With Adaptive Weighting for SOC and Thermal Balancing in Second-Life Batteries
inferred← citeshttps://doi.org/10.1109/jiot.2025.3593027openalex-snapshot
An SVM-BaLSTM-Based Remaining Useful Life Prediction Method for SPD in IIoT
inferred← citeshttps://doi.org/10.1109/jsen.2025.3642392openalex-snapshot
Detection and Analysis of Acoustic Emission Signal in Lithium-Ion Batteries During Charging and Discharging
inferred← citeshttps://doi.org/10.1109/melecon56669.2024.10608745openalex-snapshot
Battery Reuse and Lithium Exploration: a Business Model Analysis for the Portuguese Scenario
inferred← citeshttps://doi.org/10.1109/naps61145.2024.10741767openalex-snapshot
Kolmogorov-Arnold Networks for Supervised Learning Tasks in Smart Grids
inferred← citeshttps://doi.org/10.1109/srse56746.2022.10067301openalex-snapshot
Early Prediction Method for Remaining Useful Life of Retired Batteries in Second-life Applications
inferred← citeshttps://doi.org/10.1109/srse56746.2022.10067595openalex-snapshot
Capacity and RUL Prediction of Retired Batteries Using Machine Learning Features
inferred← citeshttps://doi.org/10.1109/tase.2025.3550725openalex-snapshot
Adaptive Nonlinear Recursive Model Updating Method for Quality-Related Process Monitoring
inferred← citeshttps://doi.org/10.1109/tim.2021.3111009openalex-snapshot
Remaining Useful Life Estimation for Prognostics of Lithium-Ion Batteries Based on Recurrent Neural Network
inferred← citeshttps://doi.org/10.1109/tim.2022.3216594openalex-snapshot
iThing: Designing Next-Generation Things With Battery Health Self-Monitoring Capabilities for Sustainable IIoT
inferred← citeshttps://doi.org/10.1109/tim.2023.3287247openalex-snapshot
An IoT and Semi-Supervised Learning-Based Sensorless Technique for Panel Level Solar Photovoltaic Array Fault Diagnosis
inferred← citeshttps://doi.org/10.1109/tim.2023.3322478openalex-snapshot
A Novel Whale Optimization Algorithm Based on Music Theory Knowledge for RUL Prediction of Motor Bearing
inferred← citeshttps://doi.org/10.1109/tim.2024.3476559openalex-snapshot
A Characterization of the Uncertainties Associated With an Automated System for the Study of Lithium-Ion Cells: A Case-Study of a Domestic Grid 24-h Scenario
inferred← citeshttps://doi.org/10.1109/tim.2024.3480208openalex-snapshot
Theoretical Performance Bound and Its Experimental Validation of Battery Capacity Estimates in Rechargeable Batteries
inferred← citeshttps://doi.org/10.1109/tim.2024.3481588openalex-snapshot
Aeroengine Remaining Life Prediction Based on Advanced Health Index Construction and Gray Similarity Multiscale Matching
inferred← citeshttps://doi.org/10.1109/tim.2026.3659656openalex-snapshot
State-Switching Stochastic Estimation Strategy for Multistage Degradation With Sensor Fusion
inferred← citeshttps://doi.org/10.1109/tte.2022.3173918openalex-snapshot
Novel Image-Based Rapid RUL Prediction for Li-Ion Batteries Using a Capsule Network and Transfer Learning
inferred← citeshttps://doi.org/10.1109/tte.2024.3354107openalex-snapshot
Rapid Test and Assessment of Lithium-Ion Battery Cycle Life Based on Transfer Learning
inferred← citeshttps://doi.org/10.1142/s0218194024500396openalex-snapshot
Toward Proactive Maintenance: A Multi-Tiered Architecture for Industrial Equipment Health Monitoring and Remaining Useful Life Prediction
inferred← citeshttps://doi.org/10.1371/journal.pone.0335066openalex-snapshot
HybridoNet-Adapt: A domain-adapted framework for accurate lithium-ion battery RUL prediction
inferred← citeshttps://doi.org/10.1541/ieejjia.22004793openalex-snapshot
Machine Learning-based Remaining Useful Life Prediction Techniques for Lithium-ion Battery Management Systems: A Comprehensive Review
inferred← citeshttps://doi.org/10.2139/ssrn.4960301openalex-snapshot
A Hybrid Knowledge Graph and Fuzzy Evaluation Reasoning Approach for Dynamic Echelon Recycling of Retired Electric Vehicle Batteries
inferred← citeshttps://doi.org/10.3390/app13148498openalex-snapshot
A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples
inferred← citeshttps://doi.org/10.3390/batteries8090122openalex-snapshot
Procedure for Assessing the Suitability of Battery Second Life Applications after EV First Life
inferred← citeshttps://doi.org/10.3390/electronics10182294openalex-snapshot
Unsupervised Neural Networks for Identification of Aging Conditions in Li-Ion Batteries
inferred← citeshttps://doi.org/10.3390/en16020953openalex-snapshot
Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives
inferred← citeshttps://doi.org/10.3390/en17122979openalex-snapshot
A High-Speed Multichannel Electrochemical Impedance Spectroscopy System Using Broadband Multi-Sine Binary Perturbation for Retired Li-Ion Batteries of Electric Vehicles
inferred← citeshttps://doi.org/10.3390/en18030746openalex-snapshot
State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review
inferred← citeshttps://doi.org/10.3390/pr12112536openalex-snapshot
Q-Learning-Incorporated Robust Relevance Vector Machine for Remaining Useful Life Prediction
inferred← citeshttps://doi.org/10.47760/ijcsmc.2023.v12i05.001openalex-snapshot
Battery Management System for Analysing Accurate Real Time Battery Condition using Machine Learning
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.