Related references
Note: Only part of the references are listed.Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter
Xin Lai et al.
ENERGY (2021)
A method to estimate battery SOH indicators based on vehicle operating data only
L. Vichard et al.
ENERGY (2021)
A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
Kailong Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)
A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge
Jianfang Jia et al.
JOURNAL OF ENERGY STORAGE (2021)
An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries
Bin Gou et al.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2021)
State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression
Xiaoyu Li et al.
ENERGY (2020)
An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms
Minqi Jiang et al.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2020)
A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization
Xing Shu et al.
ENERGY (2020)
State-of-charge estimation of lithium-ion batteries using LSTM and UKF
Fangfang Yang et al.
ENERGY (2020)
State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression
Xiaoyu Li et al.
ENERGY (2020)
An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system
Jinhao Meng et al.
ENERGY (2020)
An Incremental Voltage Difference Based Technique for Online State of Health Estimation of Li-ion Batteries
Arunava Naha et al.
SCIENTIFIC REPORTS (2020)
Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation With Short-Term Feature
Lei Cai et al.
IEEE TRANSACTIONS ON POWER ELECTRONICS (2020)
Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries
Jinpeng Tian et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries
Yuanwang Deng et al.
ENERGY (2019)
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
Fangfang Yang et al.
ENERGY (2019)
State of health prediction for lithium-ion batteries using multiple-view feature fusion and support vector regression ensemble
Chao Ma et al.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2019)
State estimation for advanced battery management: Key challenges and future trends
Xiaosong Hu et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)
Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation
Hongbin Ren et al.
ENERGY (2019)
State of health estimation of lithium-ion batteries based on the constant voltage charging curve
Zengkai Wang et al.
ENERGY (2019)
State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis
Xiaoyu Li et al.
JOURNAL OF POWER SOURCES (2019)
State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model
Xueying Zheng et al.
IEEE ACCESS (2019)
A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation
J. Li et al.
APPLIED ENERGY (2018)
Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis
Jufeng Yang et al.
APPLIED ENERGY (2018)
State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network
Bizhong Xia et al.
ENERGY (2018)
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
Jingwen Wei et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm
Xu Zhang et al.
JOURNAL OF POWER SOURCES (2018)
A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter
Yi Li et al.
JOURNAL OF POWER SOURCES (2018)
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
Duo Yang et al.
JOURNAL OF POWER SOURCES (2018)
An Online SOC and SOH Estimation Model for Lithium-Ion Batteries
Shyh-Chin Huang et al.
ENERGIES (2017)
A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering, framework
Chao Lyu et al.
ENERGY (2017)
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
Hicham Chaoui et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2017)
Gaussian process regression for forecasting battery state of health
Robert R. Richardson et al.
JOURNAL OF POWER SOURCES (2017)
A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model
Zhongbao Wei et al.
APPLIED ENERGY (2017)
State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
Caihao Weng et al.
APPLIED ENERGY (2016)
Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach
Gae-won You et al.
APPLIED ENERGY (2016)
On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis
Limei Wang et al.
APPLIED ENERGY (2016)
An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks
Ji Wu et al.
APPLIED ENERGY (2016)
Critical review of state of health estimation methods of Li-ion batteries for real applications
M. Berecibar et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2016)
Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach
Yujie Cheng et al.
ENERGY (2015)
Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model
Taichun Qin et al.
MICROELECTRONICS RELIABILITY (2015)
Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications
Zheng Chen et al.
JOURNAL OF POWER SOURCES (2013)
Share Paper
