Caihao Weng

Automotive Engineering · 2013–2019

A researcher in the Science corpus: 4 papers; trusts 3 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Automotive Engineering — 64 citations into it; #426 of 3,081 by trust here, #691 by citations
• Electrical and Electronic Engineering — 60 citations into it; #3,121 of 16,539 by trust here, #4,002 by citations

Whom Caihao Weng trusts overall

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

1. Xiaosong Hu
2. Matthieu Dubarry
3. Minggao Ouyang
4. Xuebing Han
5. Xuning Feng
6. Languang Lu
7. Mohammadhosein Safari
8. Jianqiu Li
9. Yongzhi Zhang

Who trusts Caihao Weng overall

1. Xiaosong Hu
2. Robert R. Richardson
3. Jinpeng Tian
4. Rui Xiong
5. D. Roman
6. Yunhong Che
7. Caiping Zhang
8. Christoph R. Birkl
9. Yi Li
10. Jufeng Yang

Papers from Caihao Weng's perspective

Ordered by how strongly the researchers Caihao Weng 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. State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (IEEE Transactions on Vehicular Technology)
5. Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (IEEE Transactions on Vehicular Technology)
6. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles (IEEE Transactions on Vehicular Technology)
7. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (IEEE Transactions on Vehicular Technology)
8. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
9. The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles (IEEE Transactions on Vehicular Technology)
10. A review on the key issues for lithium-ion battery management in electric vehicles

Their papers

• State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking (2016) — 280 citations
• On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression (2013) — 499 citations
• A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring (2014) — 351 citations
• Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019) — 395 citations