Zhipeng Jiao
Hebei University of Technology
7 Papers
4 Citations
Zhipeng Jiao is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 2, co-authored 2 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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A LightGBM Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions
TL;DR: In this article , a light gradient boosting machine (LightGBM) based framework with electro-chemical theory was proposed to predict the remaining useful life (RUL) degradation under driving conditions, where features from incremental capacity-differential voltage (DV) curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL.
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Analysis of performance degradation and residual life prediction of batteries for electric vehicles under driving conditions
Li Wenhua,Zhipeng Jiao,Lulu Zhou +2 more
Abstract: The analysis of residual life prediction of batteries for electric vehicles under driving conditions was performed based on the degradation data. This article, characteristics of lithium iron phosphate battery performance degradation as the object, mainly considers two factors about the charge–discharge rate and vibration stress, first, the lithium battery performance degradation test simulating a car in a real driving environment is designed, which provides the lithium battery performance test reference. Then, the model parameters were obtained based on the test data. Finally, the performance degradation model and residual life prediction model of the lithium battery based on the Wiener process are established. This method not only has a high prediction accuracy, but also avoids the construction of a complex battery mechanism degradation model. What is more, this article provides a good perspective for residual life prediction in simplified experiments saving cost. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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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.