基于能耗加权策略的燃料电池汽车续驶里程预测

doi:10.19562/j.chinasae.qcgc.2023.12.018

Abstract

Abstract:

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.

Key words: fuel cell vehicle, energy consumption prediction, driving range prediction, machine learning, working condition clustering, energy consumption weighting

Junzhao Jiang,Wenhao Yang,Bin Peng,Ting Guo,Yekai Xu,Guozhuo Wang. Driving Range Prediction of Fuel Cell Vehicles Based on Energy Consumption Weighting Strategy[J].Automotive Engineering, 2023, 45(12): 2357-2365.

Figures/Tables 23

符号	含义	单位
$v m$	平均速度	km/h
$v m a x$	最高速度	km/h
$v s d$	速度标准差	km/h
$v m r$	平均巡航速度	km/h
$a m$	平均加速度	m/s²
$A N A$	负加速度平均值	m/s²
$A P A$	正加速度平均值	m/s²
$a m a x$	最大加速度	m/s²
$P i$	驻车比例
$P a$	加速比例
$P d$	减速比例
$P c$	匀速比例

原始特征	主成分1	主成分2	主成分3	主成分4	主成分5
$v m$	0.958	0.075	0.086	0.182	-0.080
$v m a x$	0.948	-0.173	0.124	-0.128	-0.049
$v s d$	0.232	0.441	-0.023	0.747	-0.122
$v m r$	0.968	-0.150	0.0427	0.037	-0.110
$a m$	-0.056	0.914	0.068	0.201	-0.149
$A N A$	-0.050	0.044	0.635	-0.093	0.728
$A P A$	-0.320	0.868	-0.014	0.201	-0.058
$a m a x$	-0.063	-0.675	-0.211	-0.109	0.599
$P i$	0.221	0.099	0.957	0.063	0.022
$P a$	0.096	0.251	-0.005	0.086	-0.913
$P d$	-0.675	0.066	-0.422	0.467	0.287
$P c$	0.529	-0.323	-0.215	-0.630	0.309

参数	$v m$	$a m$	$A N A$	$P c$	$P i$
第1类	23.611	-0.003	-0.325	0.181	0.296
第2类	79.820	0.008	-0.286	0.409	0.024

参数	$v m$	$a m$	$A N A$	$P c$	$P i$
第1类	21.374	-0.001	-0.315	0.179	0.319
第2类	57.771	0.001	-0.380	0.326	0.050
第3类	102.625	0.005	-0.187	0.459	0.004

项目	MAE	RMSE	$ε r a n g e / %$
室内转鼓实验能耗	0.006 228	0.008 695
开放道路实验能耗	0.021 134	0.031 263
室内转鼓实验里程	0.597 576		0.29
开放道路实验里程	0.847 365		0.39

References 16

1	YUAN X， ZHANG C， HONG G， et al. Method for evaluating the real-world driving energy consumptions of electric vehicles［J］. Energy， 2017， 141： 1955-1968.
2	余颖弘，李以农，梁艺潇，等.基于数据挖掘的电动汽车瞬态能耗模型建模［C］. 2019中国汽车工程学会年会论文集（2），2019：162-168.
	YU Y H， LI Y N， LIANG Y X， et al. Modeling of transient energy consumption model of electric vehicle based on data mining［C］.Proceedings of 2019 China SAE Congress （2）， 2019：162-168.
3	ZENG T， ZHANG C， HU M， et al. Modelling and predicting energy consumption of a range extender fuel cell hybrid vehicle［J］. Energy， 2018， 165： 187-197.
4	ZHANG J， WANG Z， LIU P， et al. Energy consumption analysis and prediction of electric vehicles based on real-world driving data［J］. Applied Energy， 2020， 275： 115408.
5	NAN S， TU R， LI T， et al. From driving behavior to energy consumption： a novel method to predict the energy consumption of electric bus［J］. Energy，2022，261（PA）.
6	刘光明，欧阳明高，卢兰光，等. 基于电池能量状态估计和车辆能耗预测的电动汽车续驶里程估计方法研究［J］. 汽车工程， 2014， 36（11）： 1302-1309.
	LIU G M， OUYANG M G， LU L G， et al. Driving range estimation for electric vehicles based on battery energy state estimation and vehicle energy consumption prediction［J］. Automotive Engineering， 2014， 36（11）： 1302-1309.
7	尹安东，赵韩，周斌，等. 基于行驶工况识别的纯电动汽车续驶里程估算［J］. 汽车工程， 2014， 36（11）： 1310-1315.
	YIN A D， ZHAO H， ZHOU B， et al. Driving range estimation for battery electric vehicles based on driving cycle identification［J］. Automotive Engineering， 2014， 36（11）： 1310-1315.
8	郑宁安. 纯电动汽车能耗预测与续驶里程估算［D］.大连：大连理工大学，2016.
	ZHENG N A. Energy consumption prediction and driving range estimation for electric vehicles［D］. Dalian： Dalian University of Technology， 2016.
9	舒航，史强，袁凯，等.基于平均能耗并融合整车状态的续驶里程估算［J］.公路交通科技，2022，39（2）：174-182.
	SHU H， SHI Q， YUAN K， et al. Estimation on driving range based on average energy consumption and integrated vehicle status［J］. Journal of Highway and Transportation Research and Development， 2022，39（2）：174-182.
10	WEI H， HE C， LI J， et al. Online estimation of driving range for battery electric vehicles based on SOC-segmented actual driving cycle［J］. Journal of Energy Storage， 2022， 49： 104091.
11	石琴，郑与波，姜平. 基于运动学片段的城市道路行驶工况的研究［J］. 汽车工程， 2011，33（3）： 256-261.
	SHI Q， ZHENG Y B， JIANG P. A research on driving cycle of city roads based on microtrips［J］. Automotive Engineering， 2011，33（3）： 256-261.
12	张林平，李风军. 基于主成分分析和优化聚类算法的行驶工况研究［J］. 华南师范大学学报（自然科学版）， 2021， 53（2）： 121-128.
	ZHANG L P， LI F J. A study of driving conditions based on principal component analysis and optimization clustering algorithm［J］. Journal of South China Normal University（Natural Science Edition）， 2021， 53（2）： 121-128.
13	WU X， FREESE D， CABRERA A， et al. Electric vehicles’ energy consumption measurement and estimation［J］. Transportation Research Part D： Transport and Environment， 2015， 34： 52-67.
14	吕清. 基于数据的纯电动车剩余续驶里程建模与预测研究［D］. 广州：华南理工大学， 2021.
	LÜ Q. Research on modeling and predicting of remaining driving range of battery electric vehicles based on data［D］. Guangzhou： South China University of Technology， 2021.
15	SCHAPIRE R E， SINGER Y. Improved boosting algorithms using confidence-rated predictions［J］. Machine Learning， 1998：80-91.
16	全国汽车标准化技术委员会. 电动汽车能量消耗量和续驶里程试验方法.第1部分：轻型汽车： GB/T 18386.1—2021 ［S］. 北京：中国标准出版社， 2021.
	National Technical Committee of Auto Standardization. Test methods for energy consumption and range of electric vehicles—Part 1： Light-duty vehicles： GB/T 18386.1—2021［S］. Beijing： Standards Press of China， 2021.

[1]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[2]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[3]	JIANG Ting, DAI Bing. [J]. , 2016, 38(12): 1459 -1466 .
[4]	CHEN Wu-Wei, DENG Shu-Chao, HUANG He, XIE You-Hao. [J]. , 2016, 38(12): 1488 -1493 .
[5]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[6]	ZHAO Liang, ZHANG Qiang, GUO Kong-Hui. [J]. , 2017, 39(1): 86 -91 .
[7]	CAO Li-Bo, HU Yuan, YAN Ling-Bo, PENG Yu, SHI Xiang-南. [J]. , 2017, 39(2): 174 -180 .
[8]	ZHANG Yong, GU Zheng-Qi, LIU Shui-Chang, MI Cheng-Ji. [J]. , 2017, 39(3): 275 -280 .
[9]	YU Wei-Jing, ZHANG Ying-Chao, REN Lin-Lin, SU Chang. [J]. , 2017, 39(4): 407 -411 .
[10]	MA Cong-Cheng, LAN Feng-Chong, CHEN Ji-Qing. [J]. , 2017, 39(4): 432 -439 .

聚类种类	能耗值/（kW·h）
第1类	0.355
第2类	0.544
第3类	0.889

参数名称	类型与数值
车辆类型	M₁
整备质量/kg	1 975
最大设计总质量/kg	2 415
氢罐型号	Ⅳ型瓶
燃料电池电堆输出功率/kW	114
氢罐标称压力/MPa	70
储氢容器容积/L	60，62.4
储氢量/kg	4.6
辅助动力电池	镍氢电池
辅助动力电池标称能量/（kW·h）	1.24

[1]	Weifeng Kong,Chuan Fang,Jihong Liu,Jianqiu Li,Feiqiang Li,Shengtao Huang,Xingwang Zhao,Yan Shi,Dian Yuan,Liangfei Xu,Peng Sun,Enfei Zhou,Minggao Ouyang. Research on Fuel Cell Cold Start Strategy Based on Single Cell Impedance Consistency Purging [J]. Automotive Engineering, 2024, 46(2): 260-268.
[2]	Jiqing Chen,Zihan Li,Fengchong Lan,Xinping Jiang,Wei Pan,Jikai Chen. Real-Vehicle Battery Health State Estimation Based on Nonlinear Reduced-Dimensional IC Features [J]. Automotive Engineering, 2023, 45(2): 199-208.
[3]	Gege Cui,Lü Chao,Jinghang Li,Zheyu Zhang,Guangming Xiong,Jianwei Gong. Data-Driven Personalized Scenario Risk Map Construction for Intelligent Vehicles [J]. Automotive Engineering, 2023, 45(2): 231-242.
[4]	Zhongwen Zhu,Xin Wang,Weihai Jiang,Cheng Li. Research on Integrated Thermal Management System of Hydrogen Fuel Cell Vehicle [J]. Automotive Engineering, 2023, 45(11): 1991-2000.
[5]	Zhicheng He,Zejun Xie,Kan Liu,Enlin Zhou,Qian Tang,Yuanyi Huang. Collaborative Design Optimization of Pure Electric Vehicle Drivetrain and Motor Structure Parameters [J]. Automotive Engineering, 2023, 45(11): 2113-2122.
[6]	Yubo Lian,Heping Ling,Junbin Wang,Hua Pan,Zhao Xie. A Real-time Thermal Runaway Detection Method of Power Battery Based on Guassian Mixed Model and Hidden Markov Model [J]. Automotive Engineering, 2023, 45(1): 139-146.
[7]	Zhiming Zhang,Jiaqi Pan,Tong Zhang. Analysis of Key Influencing Factors on Rotor Critical Speed of Centrifugal Air Compressor for Fuel Cell Vehicles [J]. Automotive Engineering, 2022, 44(9): 1386-1393.
[8]	Xiaodong Wei,Bo Liu,Jianghao Leng,Xingyu Zhou,Chao Sun,Fengchun Sun. Research on Eco-driving of Fuel Cell Vehicles via Convex Optimization [J]. Automotive Engineering, 2022, 44(6): 851-858.
[9]	Jian Zhao,Yaxin Li,Jing Tong,Bing Zhu,Weixiang Wu,Bohua Sun,Jiayi Han. Cross-Country Road Classification Method Based on Vehicle Dynamic Response Characteristics [J]. Automotive Engineering, 2022, 44(6): 909-918.
[10]	Jing Huang,Yang Peng,Ye Huang,Xiaoyan Peng. Evaluation of Driver's Mental Load State Considering the Influence of Noisy Labels [J]. Automotive Engineering, 2022, 44(5): 771-777.
[11]	Chuan Fang,Dian Yuan,Yangbin Shao,Liangfei Xu,Feiqiang Li,Zunyan Hu,Jianqiu Li,Bao Zhou,Wei Dai. Technical Breakthrough of New Generation Fuel Cell System for Winter Olympics Application Environment [J]. Automotive Engineering, 2022, 44(4): 535-544.
[12]	Yaxiong Wang,Keke Wang,Shunbin Zhong,Hongwen He,Xuechao Wang. Research Progress on Durability Enhancement-oriented Electric Control Technology of Automotive Fuel Cell System [J]. Automotive Engineering, 2022, 44(4): 545-559.
[13]	Jiquan Han,Xiangcheng Kong,Jianmei Feng,Xueyuan Peng. Performance Analysis of Hydrogen Recirculation System of High Power Fuel Cell Vehicles [J]. Automotive Engineering, 2022, 44(1): 1-7.
[14]	Jie Hu,Xueling Zhu,Chen He,Guangyu Yang. Prediction on Battery State of Health of Electric Vehicles Based on Real Vehicle Data [J]. Automotive Engineering, 2021, 43(9): 1291-1299.
[15]	Wenjie Zheng,Jing Yang,Linpei Zhu,Fei Xiong. Simulation and Experimental Study on Thermal Management System of Vehicle Fuel Cell [J]. Automotive Engineering, 2021, 43(3): 381-386.

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

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 23

References 16

Related Articles 15

Metrics

Comments

Recommended 10