Research graph
References from Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
Development of an experimental testbed for research in lithium-ion battery management systems
10.3390/en6105231 · 2013 · External reference
Efficient macro-micro scale coupled modeling of batteries
10.1149/1.2032427 · 2005 · External reference
A comparative study of equivalent circuit models for Li-ion batteries
10.1016/j.jpowsour.2011.10.013 · 2012 · External reference
A single particle model for lithium-ion batteries with electrolyte and stress-enhanced diffusion physics
10.1149/2.1541704jes · 2017 · External reference
Investigation of the performance of direct forecasting strategy using machine learning in state-of-charge prediction of Li-ion batteries exposed to dynamic loads
10.1016/j.est.2021.102351 · 2021 · External reference
Life prediction of battery based on random forest optimized by genetic algorithm
2020 · External reference
Internal short circuit detection for lithium-ion battery pack with parallel-series hybrid connections
10.1016/j.jclepro.2020.120277 · 2020 · External reference
A compact resonant switched-capacitor heater for lithium-ion battery self-heating at low temperatures
10.1109/tpel.2019.2954703 · 2019 · External reference
Unresolved reference
2019 · External reference
toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques
10.1038/s41598-020-61464-7 · 2020 · External reference
10.1016/j.egyai.2021.100081 · 2021 · Admitted local publication
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Unresolved reference
1997 · External reference
Xgboost: A scalable tree boosting system
2016 · External reference
The lithium-ion battery state-of-charge estimation using random forest regression
2014 · External reference
Data science approaches for electrochemical engineers: an introduction through surrogate model development for lithium-ion batteries
10.1149/2.1391714jes · 2018 · External reference
Random forest regression for online capacity estimation of lithium-ion batteries
10.1016/j.apenergy.2018.09.182 · 2018 · External reference
High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm
2012 · External reference
A neural network application for a lithium-ion battery pack state-of-charge estimator with enhanced accuracy
2020 · External reference
State-of-charge balancing of lithium-ion batteries with state-of-health awareness capability
10.1109/tia.2020.3029755 · 2020 · External reference
ANN-based adaptive control of robotic manipulators with friction and joint elasticity
10.1109/tie.2009.2024657 · 2009 · External reference
A hierarchical state of charge estimation method for lithium-ion batteries via XGBoost and kalman filter
2020 · External reference
Mathematical model reformulation for lithium-ion battery simulations: galvanostatic boundary conditions
10.1149/1.3065083 · 2009 · External reference
Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell
10.1149/1.2221597 · 1993 · External reference
Review of simplified pseudo-two-dimensional models of lithium-ion batteries
10.1016/j.jpowsour.2016.07.036 · 2016 · External reference
Reduced-order electrochemical model-based SOC observer with output model uncertainty estimation
10.1109/tcst.2016.2598764 · 2017 · External reference
