Driving Range Prediction of Fuel Cell Vehicles Based on Energy Consumption Weighting Strategy

被引：0

作者：

Jiang J. ^{[1
]}

Yang W. ^{[1
]}

Peng B. ^{[1
]}

Guo T. ^{[2
]}

Xu Y. ^{[1
]}

Wang G. ^{[2
]}

机构：

[1] School of Transportation and Automobile Engineering, Hefei University of Technology, Hefei

[2] China Automotive Technology and Research Center Co., Ltd., Tianjin

来源：

Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 12期

关键词：

driving range prediction; energy consumption prediction; energy consumption weighting; fuel cell vehicle; machine learning; working condition clustering;

D O I：

10.19562/j.chinasae.qcgc.2023.12.018

中图分类号：

学科分类号：

摘要：

Energy consumption and driving range of fuel cell vehicle are the key indexes to evaluate its performance. Taking electric hybrid fuel cell vehicle as an example, a data-driven method is used to design and build a multi-model collaborative energy consumption prediction algorithm for fuel cell vehicle, taking into account of real-time energy consumption predicted by integrated learning model and fragment energy consumption calculated by fuzzy C-means clustering conditions, so as to get the corrected energy consumption value. Based on this, a driving range prediction algorithm weighted by historical and real-time energy consumption is constructed to solve the problem of large deviation in driving range prediction caused by changes in extreme operating conditions within segments, to achieve effective driving range prediction for fuel cell vehicles. Finally, the indoor drum experiment and open road experiment of fuel cell vehicle are carried out, and the predicted results are in good agreement with the experimental results, which verifies the accuracy of the algorithm. © 2023 SAE-China. All rights reserved.

引用

页码：2357 / 2365

页数：8

共 11 条

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