Zhongbao Wei

Automotive Engineering · 2014–2022

A researcher in the Science corpus: 8 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Zhongbao Wei trusts overall

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

  1. Xiaosong Hu
  2. Maria Skyllas-Kazacos
  3. Zhonghao Rao
  4. Xuning Feng
  5. Rui Xiong
  6. K. Åström
  7. Jinhao Meng
  8. Mohammad Charkhgard
  9. Jinpeng Tian
  10. Kailong Liu

Who trusts Zhongbao Wei overall

  1. Jufeng Yang
  2. S. Landini
  3. Bing Xia

Papers from Zhongbao Wei's perspective

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

  1. An ensemble model for predicting the remaining useful performance of lithium-ion batteries (Microelectronics and reliability)
  2. State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF (IEEE transactions on industrial electronics (1982. Print))
  3. Support Vector Machines Used to Estimate the Battery State of Charge (IEEE transactions on power electronics)
  4. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
  5. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
  6. Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (IEEE Transactions on Vehicular Technology)
  7. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation
  8. Modeling and Applications of Electrochemical Impedance Spectroscopy (EIS) for Lithium-ion Batteries (Journal of Electrochemical Science and Technology)
  9. SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline (Journal of the Electrochemical Society)
  10. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (Journal of Power Sources)

Their papers