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Xianke Lin
Researcher at University of Ontario Institute of Technology
Publications - 19
Citations - 2017
Xianke Lin is an academic researcher from University of Ontario Institute of Technology. The author has contributed to research in topics: Battery (electricity) & State of health. The author has an hindex of 12, co-authored 19 publications receiving 553 citations.
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Battery Lifetime Prognostics
TL;DR: A timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches is presented, analyzed, and compared.
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Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures
TL;DR: A comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults are provided.
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Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression
TL;DR: A data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution to SOC estimation of battery packs, and its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions.
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Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends
TL;DR: A multilayer design architecture for advanced battery management, which consists of three progressive layers, which aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management.
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General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries
TL;DR: In this article, a novel feature extraction method is proposed to extract health indicators (HIs) from general discharging conditions, and typical data-driven methods, including linear regression, support vector machine, relevance vector machine and Gaussian process regression (GPR), are constructed to predict battery SOH.