Minggao Ouyang

Electrical and Electronic Engineering · 2011–2021

A researcher in the Science corpus: 50 papers; trusts 45 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

Whom Minggao Ouyang trusts overall

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

  1. Xuning Feng
  2. Matthieu Dubarry
  3. Languang Lu
  4. Xuebing Han
  5. R. Spotnitz
  6. Xiaosong Hu
  7. Jianqiu Li
  8. Bor Yann Liaw
  9. Qingsong Wang
  10. Shriram Santhanagopalan

Who trusts Minggao Ouyang overall

  1. Xuning Feng
  2. Xiaosong Hu
  3. Dongsheng Ren
  4. Rui Xiong
  5. Languang Lu
  6. Kailong Liu
  7. Prashant Shrivastava
  8. Xianke Lin
  9. Xuebing Han
  10. Xiangming He

Papers from Minggao Ouyang's perspective

Ordered by how strongly the researchers Minggao Ouyang 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. A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (Journal of Power Sources)
  3. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
  4. Effects of overdischarge on performance and thermal stability of a Li-ion cell
  5. A General Discussion of Li Ion Battery Safety
  6. Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode
  7. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (IEEE Transactions on Vehicular Technology)
  8. Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (IEEE Transactions on Vehicular Technology)
  9. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles (IEEE Transactions on Vehicular Technology)
  10. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (IEEE Transactions on Vehicular Technology)

Their papers