On the public citation graph

Useful energy prediction model of a Lithium-ion cell operating on various duty cycles

https://doi.org/10.17531/ein.2022.2.13

0 signed · 83 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-540-28650-9_4openalex-snapshot

Gaussian Processes in Machine Learning

inferredcites →https://doi.org/10.1016/j.apenergy.2014.08.081openalex-snapshot

A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries

inferredcites →https://doi.org/10.1016/j.apenergy.2015.03.110openalex-snapshot

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

inferredcites →https://doi.org/10.1016/j.egyai.2021.100081openalex-snapshot

Lithium-ion battery data and where to find it

inferredcites →https://doi.org/10.1016/j.egypro.2016.06.125openalex-snapshot

Model-based State-of-energy Estimation of Lithium-ion Batteries in Electric Vehicles

inferredcites →https://doi.org/10.1016/j.electacta.2017.04.004openalex-snapshot

Power capability prediction for lithium-ion batteries based on multiple constraints analysis

inferredcites →https://doi.org/10.1016/j.energy.2015.07.120openalex-snapshot

A method for state of energy estimation of lithium-ion batteries based on neural network model

inferredcites →https://doi.org/10.1016/j.est.2019.03.022openalex-snapshot

Battery health prediction under generalized conditions using a Gaussian process transition model

inferredcites →https://doi.org/10.1016/j.est.2021.102765openalex-snapshot

Modelling the cycling degradation of Li-ion batteries: Chemistry influenced stress factors

inferredcites →https://doi.org/10.1016/j.jpowsour.2005.01.006openalex-snapshot

Ageing mechanisms in lithium-ion batteries

inferredcites →https://doi.org/10.1016/j.jpowsour.2005.03.172openalex-snapshot

Main aging mechanisms in Li ion batteries

inferredcites →https://doi.org/10.1016/j.jpowsour.2010.11.134openalex-snapshot

Cycle-life model for graphite-LiFePO4 cells

inferredcites →https://doi.org/10.1016/j.jpowsour.2011.08.067openalex-snapshot

Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes

inferredcites →https://doi.org/10.1016/j.jpowsour.2012.05.012openalex-snapshot

Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

inferredcites →https://doi.org/10.1016/j.jpowsour.2013.02.012openalex-snapshot

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

inferredcites →https://doi.org/10.1016/j.jpowsour.2014.02.012openalex-snapshot

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

inferredcites →https://doi.org/10.1016/j.jpowsour.2014.06.111openalex-snapshot

A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation

inferredcites →https://doi.org/10.1016/j.jpowsour.2014.07.030openalex-snapshot

Degradation of lithium ion batteries employing graphite negatives and nickel–cobalt–manganese oxide + spinel manganese oxide positives: Part 1, aging mechanisms and life estimation

inferredcites →https://doi.org/10.1016/j.jpowsour.2015.03.165openalex-snapshot

Implementation and robustness of an analytically based battery state of power

inferredcites →https://doi.org/10.1016/j.jpowsour.2015.11.087openalex-snapshot

An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles

inferredcites →https://doi.org/10.1016/j.jpowsour.2017.01.054openalex-snapshot

A novel approach of remaining discharge energy prediction for large format lithium-ion battery pack

inferredcites →https://doi.org/10.1016/j.jpowsour.2017.04.084openalex-snapshot

Lithium-ion battery aging mechanisms and life model under different charging stresses

inferredcites →https://doi.org/10.1016/j.jpowsour.2017.09.048openalex-snapshot

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO 4 battery

inferredcites →https://doi.org/10.1016/j.jpowsour.2019.226830openalex-snapshot

Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells

inferredcites →https://doi.org/10.1016/j.jpowsour.2019.227281openalex-snapshot

Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis

inferredcites →https://doi.org/10.1016/j.jpowsour.2019.227666openalex-snapshot

Analysis and modeling of cycle aging of a commercial LiFePO4/graphite cell

inferredcites →https://doi.org/10.1016/j.knosys.2021.106900openalex-snapshot

A novel method for the modeling of the state of health of lithium-ion cells using machine learning for practical applications

inferredcites →https://doi.org/10.1016/j.mseb.2016.04.017openalex-snapshot

Fatigue damage approach applied to Li-ion batteries ageing characterization

inferredcites →https://doi.org/10.1016/j.ress.2015.05.018openalex-snapshot

Variable importance analysis: A comprehensive review

inferredcites →https://doi.org/10.1016/j.rser.2017.05.127openalex-snapshot

A review of stochastic battery models and health management

inferredcites →https://doi.org/10.1016/j.rser.2019.109254openalex-snapshot

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

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/10.1021/acsenergylett.1c00173openalex-snapshot

Half-Cell Cumulative Efficiency Forecasts Full-Cell Capacity Retention in Lithium-Ion Batteries

inferredcites →https://doi.org/10.1038/ncomms2518openalex-snapshot

New class of nonaqueous electrolytes for long-life and safe lithium-ion batteries

inferredcites →https://doi.org/10.1038/s41467-017-02251-3openalex-snapshot

Nanoparticle anchoring targets immune agonists to tumors enabling anti-cancer immunity without systemic toxicity

inferredcites →https://doi.org/10.1038/s41524-018-0064-0openalex-snapshot

Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries

inferredcites →https://doi.org/10.1109/access.2020.2980961openalex-snapshot

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

inferredcites →https://doi.org/10.1109/access.2021.3089032openalex-snapshot

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

inferredcites →https://doi.org/10.1109/ias.2019.8912003openalex-snapshot

State-of-Charge Estimation of Li-ion Battery in Electric Vehicles: A Deep Neural Network Approach

inferredcites →https://doi.org/10.1109/ojies.2020.3015396openalex-snapshot

A Generic Cycle Life Model for Lithium-Ion Batteries Based on Fatigue Theory and Equivalent Cycle Counting

inferredcites →https://doi.org/10.1109/tec.2007.895457openalex-snapshot

A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation

inferredcites →https://doi.org/10.1109/tia.2020.3004294openalex-snapshot

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

inferredcites →https://doi.org/10.1109/tie.2015.2461523openalex-snapshot

Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling

inferredcites →https://doi.org/10.1109/tie.2016.2593650openalex-snapshot

IEEE Transactions on Industrial Electronics

inferredcites →https://doi.org/10.1109/tie.2017.2733475openalex-snapshot

A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries

inferredcites →https://doi.org/10.1109/tie.2017.2779411openalex-snapshot

A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects

inferredcites →https://doi.org/10.1109/tie.2020.2973876openalex-snapshot

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

inferredcites →https://doi.org/10.1109/tii.2018.2794997openalex-snapshot

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

inferredcites →https://doi.org/10.1109/tii.2019.2941747openalex-snapshot

Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

inferredcites →https://doi.org/10.1109/tim.2008.2005965openalex-snapshot

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

inferredcites →https://doi.org/10.1109/tpel.2018.2873247openalex-snapshot

Lithium-Ion Batteries Health Prognosis Considering Aging Conditions

inferredcites →https://doi.org/10.1109/tvt.2018.2864688openalex-snapshot

Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

inferredcites →https://doi.org/10.1111/rssb.12377openalex-snapshot

Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models

inferredcites →https://doi.org/10.1149/1.1759972openalex-snapshot

Effect of Porosity on the Capacity Fade of a Lithium-Ion Battery

inferredcites →https://doi.org/10.1149/1.1787631openalex-snapshot

Cycle Life Modeling of Lithium-Ion Batteries

inferredcites →https://doi.org/10.1149/1.3043429openalex-snapshot

Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries

inferredcites →https://doi.org/10.1149/1.3432560openalex-snapshot

Life Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach

inferredcites →https://doi.org/10.1149/2.0411609jesopenalex-snapshot

Calendar Aging of Lithium-Ion Batteries

inferredcites →https://doi.org/10.1177/0142331220966425openalex-snapshot

Fast battery capacity estimation using convolutional neural networks

inferredcites →https://doi.org/10.1177/1687814015622327openalex-snapshot

Lithium-ion battery remaining useful life prediction based on grey support vector machines

inferredcites →https://doi.org/10.15199/48.2018.12.34openalex-snapshot

Modelowanie pracy i trwałości akumulatorów litowo-jonowych w pojazdach elektrycznych

inferredcites →https://doi.org/10.17531/ein.2016.1.4openalex-snapshot

Cycle life prediction of lithium-ion cells under complex temperature profiles

inferredcites →https://doi.org/10.17531/ein.2017.2.10openalex-snapshot

Modelling and analysis of dynamic states of the lead-acid batteries in electric vehicles

inferredcites →https://doi.org/10.17531/ein.2018.1.3openalex-snapshot

Operation of electric hybrid drive systems in varied driving conditions

inferredcites →https://doi.org/10.17531/ein.2021.1.18openalex-snapshot

Prediction of remaining useful life for lithium-ion battery with multiple health indicators

inferredcites →https://doi.org/10.33063/diva2-1452023openalex-snapshot

Battery 2030+ Roadmap

inferredcites →https://doi.org/10.3390/batteries7020028openalex-snapshot

Calendar Aging of Li-Ion Cells—Experimental Investigation and Empirical Correlation

inferredcites →https://doi.org/10.3390/en12203899openalex-snapshot

Analysis and Modeling of the Wear-Out Process of a Lithium-Nickel-Manganese-Cobalt Cell during Cycling Operation under Constant Load Conditions

inferredcites →https://doi.org/10.3390/en14175327openalex-snapshot

Potential of Using Medium Electric Vehicle Fleet in a Commercial Enterprise Transport in Germany on the Basis of Real-World GPS Data

inferredcites →https://doi.org/10.33961/jecst.2023.00724openalex-snapshot

Aging Mechanisms of Lithium-ion Batteries

inferredcites →https://doi.org/10.48550/arxiv.1612.08468openalex-snapshot

Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models

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.4633591openalex-extracted

E-Damics - a Methodology for Improving the Production Process in Terms of Eliminating Energy Waste Based on the Example of the Manufacturing Process of Lead-Acid Batteries

inferred← citeshttps://doi.org/10.1007/s10098-024-02836-9openalex-snapshot

An environmental life cycle assessment of electric race car: a case study of eVarta

inferred← citeshttps://doi.org/10.1016/j.engfailanal.2023.107661openalex-snapshot

Comprehensive study on reliability, deterioration, and ageing of lithium-ion batteries in electric-powered aircraft assessed after a storage period using functional data analysis and non-parametric back up

inferred← citeshttps://doi.org/10.1038/s41598-025-91832-0openalex-snapshot

Predicting creep failure life in adhesive-bonded single-lap joints using machine learning

inferred← citeshttps://doi.org/10.1109/epe68416.2026.11537447openalex-snapshot

Energy storages in the context of energy security in the event of catastrophic failures

inferred← citeshttps://doi.org/10.17531/ein/220211openalex-snapshot

Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation

inferred← citeshttps://doi.org/10.24425/aee.2023.146042openalex-snapshot

Modelling of Li-Ion battery state-of-health with Gaussian processes

inferred← citeshttps://doi.org/10.24425/bpasts.2022.144049openalex-snapshot

Minimizing the emission of material waste in the production process of batteries

inferred← citeshttps://doi.org/10.3390/app14198884openalex-snapshot

Recent Applications of Explainable AI (XAI): A Systematic Literature Review

inferred← citeshttps://doi.org/10.3390/en16176360openalex-snapshot

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

inferred← citeshttps://doi.org/10.3390/en17164051openalex-snapshot

Vehicle Acceleration and Speed as Factors Determining Energy Consumption in Electric Vehicles

inferred← citeshttps://doi.org/10.3390/en17235942openalex-snapshot

A Diagnostic Approach to Improving the Energy Efficiency of Production Processes—2E-DAmIcS Methodology

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.