Dongsheng Ren

Electrical and Electronic Engineering · 2015–2021

A researcher in the Science corpus: 18 papers; trusts 9 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom Dongsheng Ren trusts overall

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

  1. Xuning Feng
  2. Minggao Ouyang
  3. Languang Lu
  4. Xuebing Han
  5. Qingsong Wang
  6. R. Spotnitz
  7. Xiangming He
  8. Jianqiu Li
  9. Xiang Liu
  10. Ping Ping

Who trusts Dongsheng Ren overall

  1. Xuning Feng
  2. Upender Rao Koleti
  3. Languang Lu
  4. Xiangming He

Papers from Dongsheng Ren's perspective

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

  1. Thermal behaviour analysis of lithium-ion battery at elevated temperature using deconvolution method
  2. Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode
  3. Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (IEEE transactions on industrial electronics (1982. Print))
  4. A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (Journal of Power Sources)
  5. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
  6. New Concepts in Electrolytes. (Chemical Reviews)
  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