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Xiaopeng Tang
Researcher at Hong Kong University of Science and Technology
Publications - 34
Citations - 1365
Xiaopeng Tang is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Battery (electricity) & State of charge. The author has an hindex of 14, co-authored 34 publications receiving 693 citations. Previous affiliations of Xiaopeng Tang include University of Science and Technology of China.
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Journal ArticleDOI
A fast estimation algorithm for lithium-ion battery state of health
TL;DR: A model-based SoH estimator is designed and shown to be capable of closely matching battery's aging data from NASA, with the error less than 2.5%.
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A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging
TL;DR: In this article, a migration-based framework is proposed for battery modeling, in which the effects of temperature and aging are treated as uncertainties, and an accurate model for a fresh cell is established first and then migrated to the degraded batteries through a Bayes Monte Carlo method.
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Model Migration Neural Network for Predicting Battery Aging Trajectories
TL;DR: A feed-forward migration neural network (NN) is proposed to predict the batteries’ aging trajectories and is experimentally verified with four different commercial batteries to demonstrate the model’s nonlinear transfer capability.
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A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer
TL;DR: In this article, a dual-circuit state observer was proposed to estimate the state-of-charge (SOC) of LiFePO 4 batteries, which is a combination of an open loop based current integrator and a proportionalintegral (PI) based state observer.
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Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method
TL;DR: Based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories of lithium-ion batteries at a root-mean-square error of less than 2.5% for all considered scenarios.