Jinhao Meng

Automotive Engineering · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Automotive Engineering — 87 citations into it
• Electrical and Electronic Engineering — 79 citations into it

Whom Jinhao Meng trusts overall

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

1. Johan A. K. Suykens
2. M. Petzl
3. Habiballah Rahimi-Eichi
4. Rui Xiong
5. Mohammad Charkhgard
6. B. Scrosati
7. Jun Xu
8. Jonghoon Kim
9. Xiaosong Hu
10. Issa Batarseh

Who trusts Jinhao Meng overall

1. Prashant Shrivastava
2. Ephrem Chemali
3. Guangzhao Luo
4. Mattia Ricco
5. Tey Kok Soon
6. P. Kollmeyer
7. Zhongbao Wei
8. Maciej Swierczynski

Papers from Jinhao Meng's perspective

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

1. A comparative study of equivalent circuit models for Li-ion batteries
2. A review on the key issues for lithium-ion battery management in electric vehicles
3. The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer (IEEE Transactions on Vehicular Technology)
4. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
5. Stable Configuration of a Li-Ion Series Battery Pack Based on a Screening Process for Improved Voltage/SOC Balancing (IEEE transactions on power electronics)
6. Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach (IEEE Transactions on Vehicular Technology)
7. State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF (IEEE transactions on industrial electronics (1982. Print))
8. Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles (IEEE transactions on power electronics)
9. Least Squares Support Vector Machine Classifiers
10. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks

Their papers

• Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016) — 297 citations
• An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery (2018) — 335 citations
• Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles (2018) — 330 citations
• A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery (2021) — 355 citations