Research graph
References from A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries. Local targets link to admitted publications; unresolved targets remain external evidence.
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Capacity estimation for lithium-ion battery using experimental feature interval approach
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10.1016/j.egyai.2020.100006 · 2020 · Admitted local publication
Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review
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A review on prognostics and health management (PHM) methods of lithium-ion batteries
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Li-ion battery ageing model parameter: SEI layer analysis using magnetic field probing. Engineering science and technology
2018 · External reference
Safety modelling and testing of lithium-ion batteries in electrified vehicles
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Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries
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Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy
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Prognostics for state of health of lithium-ion batteries based on Gaussian process regression
2018 · External reference
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State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble
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Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles
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Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review
2019 · External reference
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Remaining useful life estimation of lithium-ion cells based on k-nearest neighbor regression with differential evolution optimization
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A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction
10.1016/j.jpowsour.2018.11.072 · 2019 · External reference
State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression
2020 · External reference
A comparison of three data-driven techniques for prognostics
2008 · External reference
Battery health prediction under generalized conditions using a Gaussian process transition model
10.1016/j.est.2019.03.022 · 2019 · External reference
Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression
2012 · External reference
Toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques
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Gaussian process regression for forecasting battery state of health
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Support vector based battery state of charge estimator
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A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery
10.1109/access.2019.2925468 · 2019 · External reference
A novel machine learning method based approach forLi-ion battery prognostic and health management
10.1109/access.2019.2947843 · 2019 · External reference
