"The SOC Estimation of Power Li-Ion Battery Based on ANFIS Model"
written by Tiezhou Wu, Mingyue Wang, Qing Xiao, Xieyang Wang,
published by Smart Grid and Renewable Energy, Vol.3 No.1, 2012
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Predicting the state of charge and health of batteries using data-driven machine learning
[2] Advanced Monitoring Algorithms for Battery Storage Systems in Electric Vehicles
[3] Karakter Kelistrikan Sistem Growbox Tabung Menggunakan Sel Surya
[4] Systematic mixed adaptive observer and EKF approach to estimate SOC and SOH of lithium–ion battery
[5] Comparison of Li-Ion Battery State of Charge Prediction by Artificial Neural Network and Adaptive Neuro Fuzzy Inference System
[6] A Systematic Mixed Adaptive Observer and EKF Approach to Estimate the SOC and SOH of a Lithium-Ion Battery
[7] Estimation of state of charge for lithium-ion batteries-A Review
[8] Auslegungsaspekte von Batteriepacks und Batteriemanagement-Systemen
Thesis, 2018
[9] State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles
Energy, 2018
[10] State of Charge Estimation of a Lithium-Ion Battery Using Robust Nonlinear Observer Approach
IET Electrical Systems in Transportation, 2018
[11] Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter
Mathematical Problems in Engineering, 2018
[12] State of charge estimation of a lithium-ion battery using robust non-linear observer approach
[13] State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network
[14] Lithium-ion battery models: a comparative study and a model-based powerline communication
[15] Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with …
International Journal of Electrical Power & Energy Systems, 2017
[16] Model-in-the-Loop Testing of SOC and SOH Estimation Algorithms in Battery Management Systems
[17] An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model
Energy, 2016
[18] Some Constraints on the Reuse of Li-ion Batteries in Data Centers
The Scientific Bulletin of Electrical Engineering Faculty, 2016
[19] Adaptive model-based state monitoring and prognostics for lithium-ion batteries
[20] Accuracy improvement of SOC estimation in lithium-ion batteries
Journal of Energy Storage, 2016
[21] Evolutionary Networks with Generalized Neurons Applied in Battery SoC Estimation
[22] A Self-Cognizant Dynamic System Approach for Prognostics and Health Management
Journal of Power Sources, 2015
[23] State-of-Charge Estimation of Lithium-ion Battery for a Satellite Power Management System
[24] Estimation of Li-ion battery SOH using Fletcher-Reeves based ANFIS
[25] Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition
[26] A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles
[27] Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition.
Advanced Materials Research, 2014
[28] A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery
Applied Energy, 2014
[29] Accuracy state of charge estimation used in energy storage system for electric transportation
Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo. IEEE, 2014
[30] On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 1. Requirements, critical review of methods and modeling
Journal of Power Sources, 2014
[31] Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
Journal of Power Sources, 2014
[32] Estimating the State-of-Charge of all-Vanadium Redox Flow Battery using a Divided, Open-circuit Potentiometric Cell
Elektronika ir Elektrotechnika, 2013
[33] A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter
Journal of Power Sources, 2013
[34] Predicting the Current and Future State of Batteries using Data-Driven Machine Learning