Z
Zhongbao Wei
Researcher at Beijing Institute of Technology
Publications - 122
Citations - 5832
Zhongbao Wei is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Battery (electricity) & Computer science. The author has an hindex of 28, co-authored 75 publications receiving 2957 citations. Previous affiliations of Zhongbao Wei include Nanyang Technological University & Beihang University.
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Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review
TL;DR: In this paper, Li-ion battery thermal management systems (BTMSs) including the air, liquid, boiling, heat pipe and solid-liquid phase change based strategies are discussed.
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Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies
TL;DR: In this paper, the thermal performance of battery module under different cell arrangement structures, which includes: 1.5 × 5 arrays rectangular arrangement, 19 cells hexagonal arrangement and 28 cells circular arrangement, was explored.
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A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model
TL;DR: In this article, a multi-timescale method for dual estimation of state of charge (SOC) and capacity with an online identified battery model is presented, where the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them.
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Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer
TL;DR: A novel technique which integrates a recursive total least squares (RTLS) with an SOC observer is proposed to enhance the online model identification and SOC estimate and provides a more reliable estimation of SOC.
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Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
TL;DR: Li et al. as mentioned in this paper developed a machine-learning-enabled data-driven models for effective capacity predictions for lithium-ion (Li-ion) batteries under different cyclic conditions, which is able to achieve satisfactory results for both one-step and multistep predictions.