Lei Zhang

Automotive Engineering · Journal of Power Sources · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 5 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Automotive Engineering — 95 citations into it; #1,436 of 3,081 by trust here, #840 by citations
• Electrical and Electronic Engineering — 82 citations into it; #9,052 of 16,539 by trust here, #5,398 by citations

Whom Lei Zhang trusts overall

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

1. Xiaosong Hu
2. Shengbo Eben Li
3. Huei Peng
4. Alain Oustaloup
5. Changfu Zou
6. S. Alavi

Who trusts Lei Zhang overall

1. Xiaosong Hu
2. Zhenpo Wang
3. Greg Van Houdt

Papers from Lei Zhang's perspective

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

1. Fractional system identification for lead acid battery state of charge estimation (Signal Processing)
2. A comparative study of equivalent circuit models for Li-ion batteries
3. The distribution of relaxation times as basis for generalized time-domain models for Li-ion batteries
4. Solving an inverse heat conduction problem using a non-integer identified model
5. Frequency-band complex noninteger differentiator: characterization and synthesis
6. A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation
7. A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model
8. On state-of-charge determination for lithium-ion batteries
9. Fractional Order Systems: Modeling and Control Applications
10. Fractional order control - A tutorial (American Control Conference)

Their papers

• A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors (2018) — 452 citations
• Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus (2018) — 448 citations
• State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis (2019) — 329 citations
• Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks (2019) — 388 citations
• Thermal runaway behavior during overcharge for large-format Lithium-ion batteries with different packaging patterns (2019) — 140 citations
• Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods (2017) — 325 citations
• Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model (2016) — 268 citations