L
Lei Zhang
Researcher at Beijing Institute of Technology
Publications - 90
Citations - 5876
Lei Zhang is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Battery (electricity). The author has an hindex of 27, co-authored 69 publications receiving 2948 citations. Previous affiliations of Lei Zhang include University of Technology, Sydney.
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A review of supercapacitor modeling, estimation, and applications: A control/management perspective
TL;DR: In this article, a review of the state-of-the-art models for electrical, self-discharge, and thermal behaviors of supercapacitors is presented, where electrochemical, equivalent circuit, intelligent, and fractional-order models are highlighted.
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State estimation for advanced battery management: Key challenges and future trends
TL;DR: This paper presents a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain, for the first time, in SOC/SOE/SOH/SOP/SOT/SOS estimation.
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Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus
TL;DR: Results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance, and the resilience of the co-estimation scheme against battery aging is verified through experimentation.
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A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors
TL;DR: A critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors is provided, and these models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity.
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Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
TL;DR: The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy.