Xuebo Li
7 Papers
Xuebo Li is an academic researcher. The author has contributed to research in topics: Computer science & Kalman filter. The author has an hindex of 1, co-authored 5 publications.
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Papers
Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating
TL;DR: In this article , an adaptive robust unscented Kalman filter (ARUKF) based on multi-parameter update is proposed to achieve high accuracy in state-of-charge (SOC) estimation.
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Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL
TL;DR: In this article , a method combining adaptive robust unscented Kalman filter and smooth variable structure filter with time-varying smoothing boundary layer (SVSF-VBL) is proposed to accurately estimate the state-of-charge inconsistency of cells with different performance parameters and working states.
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Study on Braking Energy Recovery Control Strategy for Four-Axle Battery Electric Heavy-Duty Trucks
TL;DR: In this article , a segmented torque distribution strategy was proposed to maximize energy recovery while ensuring braking stability, and the composite braking control strategy including torque distribution and dynamic coordinated control for the four-axle BET equipped with the electromechanical braking system was studied.
Development of a Rapid Inspection Driving Cycle for Battery Electric Vehicles Based on Operational Safety
TL;DR: In this article , a novel method of driving cycle development for battery electric vehicles' operational safety is proposed, and three inspection items are proposed based on relevant testing standards, and the proposed inspection calculation method is developed based on the acceleration changing rate.
Research on characteristic parameter selection and attention-GRU-based model for braking intention identification
TL;DR: In this article , an identification model based on Gated Recurrent Unit (GRU) Network with Attention mechanism is proposed for accurate identification of braking intention, based on numerous vehicle braking test data, braking process analysis, characteristic parameters selection, identification model training and verification.
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