G
Guangzhong Dong
Researcher at Chalmers University of Technology
Publications - 47
Citations - 2253
Guangzhong Dong is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Battery (electricity) & State of charge. The author has an hindex of 19, co-authored 37 publications receiving 1270 citations. Previous affiliations of Guangzhong Dong include City University of Hong Kong & University of Science and Technology of China.
Papers
More filters
Journal ArticleDOI
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression
TL;DR: A novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism and the results show that the proposed SOH estimation method can provide an accurate and robustness result.
Journal ArticleDOI
Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering
TL;DR: The experimental results show the superiority of the proposed Brownian motion based degradation model in battery health prognosis and it can provide accurate and robust SOH and RUL forecasting.
Journal ArticleDOI
Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method
TL;DR: In this paper, an online estimation approach for battery SOC and parameters of a battery based on the IIM (invariant-imbedding-method) algorithm has been proposed, which can accurately capture the real-time characteristics of the battery, including the OCV hysteresis phenomena.
Journal ArticleDOI
A method for state of energy estimation of lithium-ion batteries based on neural network model
TL;DR: In this paper, a method based on wavelet-neural-network-based battery model and particle filter estimator is presented for the state-of-energy estimation for LiFePO4 batteries.
Journal ArticleDOI
Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization
TL;DR: A novel parameterization method combining instrumental variable (IV) estimation and bilinear principle is proposed to compensate for the noise-induced biases of model identification and SOC estimation and results reveal that the proposed method is superior to existing method in terms of the immunity to noise corruption.