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Xin Lai
Researcher at University of Shanghai for Science and Technology
Publications - 65
Citations - 2211
Xin Lai is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Battery (electricity) & State of charge. The author has an hindex of 15, co-authored 40 publications receiving 813 citations. Previous affiliations of Xin Lai include Tsinghua University.
Papers
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Journal ArticleDOI
A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries
Xin Lai,Yuejiu Zheng,Tao Sun +2 more
TL;DR: The results indicate that the model accuracy does not always improve by increasing the order of the RC network, and the higher-order RC model has better robustness considering the variation in model parameters and sensor errors.
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Micro-Short-Circuit Diagnosis for Series-Connected Lithium-Ion Battery Packs Using Mean-Difference Model
TL;DR: An MSC diagnostic method is developed by employing recursive least squares filter and it is demonstrated to examine the short-circuit resistance accurately, and shows that the proposed method requires low computational load for the SOC difference and short- circuit resistance diagnosis.
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A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries
TL;DR: PNGV and the exact algorithms are an ideal combination in the low SOC area and in the high SOC area, and PSO is an ideal identification algorithm for second-order RC models.
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Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives
TL;DR: In this article, a comprehensive review on the mechanism and evolutionary process of internal short circuit (ISC) is provided, including modeling and simulation experiments and the methods of detection and diagnosis.
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Parameter sensitivity analysis and simplification of equivalent circuit model for the state of charge of lithium-ion batteries
TL;DR: A simplified model, in which the crucial parameters with high sensitivities are updated with SOC and SOH, while the other parameters retain their initial values, is proposed to ensure model accuracy while reducing computational complexity greatly.