Tey Kok Soon

Electrical and Electronic Engineering · 2015–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 68 citations into it; #12,524 of 16,539 by trust here, #12,348 by citations
• Automotive Engineering — 55 citations into it
• Artificial Intelligence — 7 citations into it

Whom Tey Kok Soon trusts overall

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

1. Rui Xiong
2. S. Buller
3. Xuebing Han
4. Languang Lu
5. Minggao Ouyang
6. Rudolph van der Merwe
7. Hongwen He
8. Jinhao Meng
9. Zheng Chen
10. Habiballah Rahimi-Eichi

Who trusts Tey Kok Soon overall

1. Jubaer Ahmed
2. Zainal Salam

Papers from Tey Kok Soon's perspective

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

1. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
2. A review on the key issues for lithium-ion battery management in electric vehicles
3. Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination
4. Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach (IEEE Transactions on Vehicular Technology)
5. On state-of-charge determination for lithium-ion batteries
6. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge
7. 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)
8. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles
9. Methods for state-of-charge determination and their applications
10. State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model (IEEE Transactions on Vehicular Technology)

Their papers

• Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method (2015) — 301 citations
• Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019) — 604 citations