Yunhong Che

Automotive Engineering · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Automotive Engineering — 46 citations into it
• Electrical and Electronic Engineering — 38 citations into it
• Safety, Risk, Reliability and Quality — 15 citations into it
• Control and Systems Engineering — 14 citations into it

Whom Yunhong Che trusts overall

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

1. Caihao Weng
2. Chih-Jen Lin
3. Wei He
4. Yi Li
5. Xiaosong Hu
6. Qiang Miao
7. Ron Kohavi
8. George Eastman House
9. Michael G. Pecht
10. Robert R. Richardson

Papers from Yunhong Che's perspective

Ordered by how strongly the researchers Yunhong Che 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. Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me
3. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
4. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework (IEEE Transactions on Instrumentation and Measurement)
5. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model
6. A review on the key issues for lithium-ion battery management in electric vehicles
7. Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries
8. An ensemble model for predicting the remaining useful performance of lithium-ion batteries (Microelectronics and reliability)
9. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application
10. Probabilistic machine learning and artificial intelligence

Their papers

• General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries (2021) — 168 citations
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020) — 311 citations
• Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression (2020) — 377 citations