C
Chao Qin
Researcher at University of Shanghai for Science and Technology
Publications - 4
Citations - 215
Chao Qin is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Extended Kalman filter & State of charge. The author has an hindex of 4, co-authored 4 publications receiving 99 citations.
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Journal ArticleDOI
Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter
Xin Lai,Xin Lai,Yi Wei,Yifan Cui,Chao Qin,Xuebing Han,Tao Sun,Long Zhou,Yuejiu Zheng,Yuejiu Zheng +9 more
TL;DR: A novel capacity estimation method realized by combining model-based and data-driven methods based on a sequential extended Kalman filter (SEKF), to improve the accuracy, and reliability of capacity estimation.
Journal ArticleDOI
A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model
TL;DR: The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model.
Journal ArticleDOI
A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles
TL;DR: A method based on charging curve sections which can be easily achieved for electric vehicles by using the complete charging curves and the corresponding capacities in experiments as the training data for a certain battery type.
Journal ArticleDOI
A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries
TL;DR: In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC) estimation, where the solid phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks.