Jingwen Wei
Automotive Engineering · 2018
A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Automotive Engineering — 15 citations into it; #889 of 3,081 by trust here, #2,035 by citations
- Electrical and Electronic Engineering — 11 citations into it; #5,820 of 16,539 by trust here, #10,998 by citations
- Control and Systems Engineering — 6 citations into it
Whom Jingwen Wei trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Jingwen Wei overall
- Kailong Liu
- Yandan Tan
- Zhenpo Wang
- Guangcai Zhao
- Valentin Sulzer
- Changgui Yuan
- Yunlong Shang
- Yi Li
- Widanalage Dhammika Widanage
Papers from Jingwen Wei's perspective
Ordered by how strongly the researchers Jingwen Wei trusts endorse each paper. Same corpus, a different order for every researcher.
- An integrated approach to battery health monitoring using bayesian regression and state estimation (2007 IEEE Autotestcon)
- Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (IEEE transactions on industrial electronics (1982. Print))
- A review on the key issues for lithium-ion battery management in electric vehicles
- Cycle-life model for graphite-LiFePO 4 cells
- Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter
- State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF (IEEE transactions on industrial electronics (1982. Print))
- Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error
- Cycle life testing and modeling of graphite/LiCoO 2 cells under different state of charge ranges
- A comparison of lead-acid and lithium-based battery behavior and capacity fade in off-grid renewable charging applications
- An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks