Xuebing Han

Electrical and Electronic Engineering · 2013–2021

A researcher in the Science corpus: 20 papers; trusts 15 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

Whom Xuebing Han trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Matthieu Dubarry
  2. Languang Lu
  3. Minggao Ouyang
  4. Xiaosong Hu
  5. Xuning Feng
  6. R. Spotnitz
  7. D. Aurbach
  8. Kandler A. Smith
  9. Venkat R. Subramanian
  10. Jianqiu Li

Who trusts Xuebing Han overall

  1. Xuning Feng
  2. Rui Xiong
  3. Kailong Liu
  4. Prashant Shrivastava
  5. Minggao Ouyang
  6. Dongsheng Ren
  7. Ferran Brosa Planella
  8. Xiaosong Hu
  9. Languang Lu
  10. Scott Jason Moura

Papers from Xuebing Han's perspective

Ordered by how strongly the researchers Xuebing Han trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (IEEE transactions on industrial electronics (1982. Print))
  2. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
  3. A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (Journal of Power Sources)
  4. Effects of overdischarge on performance and thermal stability of a Li-ion cell
  5. A General Discussion of Li Ion Battery Safety
  6. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (IEEE Transactions on Vehicular Technology)
  7. Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode
  8. Li-ion battery materials: present and future
  9. Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (IEEE Transactions on Vehicular Technology)
  10. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles (IEEE Transactions on Vehicular Technology)

Their papers