数据驱动的新能源公交车能耗预测

胡杰; 杨光宇; 何陈; 朱雪玲

doi:10.13433/j.cnki.1003-8728.20220303

数据驱动的新能源公交车能耗预测

doi: 10.13433/j.cnki.1003-8728.20220303

胡杰^{1, 2, 3,},
杨光宇^{1, 2, 3},
何陈^{1, 2, 3},
朱雪玲^{1, 2, 3}

1.
武汉理工大学现代汽车零部件技术湖北省重点实验室，武汉　430070
2.
武汉理工大学汽车零部件技术湖北省协同创新中心，武汉　430070
3.
新能源与智能网联车湖北工程技术研究中心，武汉　430070

基金项目: 湖北省科技重大专项（2020AAA001）

详细信息

作者简介:
胡杰，副教授，博士生导师，博士，auto_hj@163.com

中图分类号: U491
计量
- 文章访问数: 74
- HTML全文浏览量: 15
- PDF下载量: 11
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-25
- 网络出版日期: 2024-03-08
- 刊出日期: 2024-02-01

Data-driven Energy Consumption Prediction of New Energy Buses

HU Jie^{1, 2, 3
,},
YANG Guangyu^{1, 2, 3},
HE Chen^{1, 2, 3},
ZHU Xueling^{1, 2, 3}

1.
Hubei Key Laboratory of Modern Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, China
2.
Hubei Collaborative Innovation Center of Auto Parts Technology, Wuhan University of Technology, Wuhan 430070, China
3.
Hubei Technology Research Center of New Energy and Intelligent Connected Vehicle Engineering, Wuhan 430070, China

摘要

摘要: 鉴于现有电动车能耗预测多基于实验室条件，存在结果过于理想化或预测准确度不足的问题。本文基于北京市51路公交车的实车运行数据，分析能耗影响因素，通过时钟循环编码优化时间信息、使用箱线图设置阈值以构造行驶工况、建立基于熵权法的驾驶行为评价体系对驾驶行为与工况状态进行辅助分析，最后，对聚类后的4类典型工况片段分别建立引入注意力机制的LSTM能耗预测模型，并将其与传统LSTM及LGBM等多种预测模型进行对比分析，验证结果表明引入注意力机制的LSTM预测模型性能显著高于其他模型。
- 城市交通 /
- 能耗预测 /
- 数据驱动 /
- 驾驶行为 /
- 注意力机制
Abstract: In view of the most of the existing energy consumption prediction of electric vehicles based on the laboratory conditions, the results are too ideal and the actual deviation is large or the accuracy is insufficient. According to the actual running data of Beijing No. 51 bus, the influencing factors of energy consumption is analyzed, the time information through clock cycle coding is optimized, and a driving behavior evaluation system is established based on the entropy weight method for auxiliary analysis of driving behavior and operating conditions by using the boxplots to set thresholds to construct driving conditions. Finally, the LSTM energy consumption prediction model by introducing the attention mechanism is established for the four types of typical working condition segments after clustering, and it is compared and analyzed with the various prediction models such as traditional LSTM and LGBM, the validation results show that the performance of the LSTM prediction model by incorporating the attention mechanism is significantly higher than the other models.
- urban traffic /
- energy consumption forecast /
- data driven /
- driving behavior /
- attention mechanism

HTML全文

图 1 基于注意力机制的LSTM能耗预测

Figure 1. Energy consumption prediction based on attention mechanism LSTM

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图 2 速度和加速度随着经纬度变化图

Figure 2. Variation of velocity and acceleration with latitude and longitude

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图 3 新能源公交车能耗变化趋势

Figure 3. The change trend of new energy bus energy consumption

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图 4 第$ i $个小时的式中循环编码表示方法

Figure 4. Clock cycle-coded representation of the i hour

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图 5 加速度90百分位箱线图

Figure 5. 90th percentile box of acceleration

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图 6 轮廓系数图

Figure 6. Silhouette Coefficient

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图 7 注意力机制结构简图

Figure 7. Structure of attention mechanisms

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图 8 模型总体结构图

Figure 8. Structure of the model

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图 9 测试集平均绝对百分比误差图

Figure 9. MAPE of test sets

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图 10 测试集均方误差图

Figure 10. RMSE of test sets

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表 1 数据采集表

Table 1. Data collection form

数据表示内容	数据格式
车辆状态	1.0启动，2.0熄火
充电状态	1.0停车充电，3.0未充电
运行模式	1.0纯电
车速	0 ~ 220 km/h
累计里程	0 ~ 99999.9 km，最小单位0.1 km
总电压	0 ~ 1000 V，最小单位0.1 V
总电流	−1000 ~ 1000 A，最小单位0.1 A
SOC	0 ~ 100%，最小单位1%
档位	13 倒挡，14 D档，15 P档
驱动电机控制器温度	0 ~ 250 ℃
驱动电机转速	−20000 ~ 45331 r/min
驱动电机转矩	−2000 ~ 4533.1 N·m
驱动电机温度	−40 ~ 210 ℃
电机控制器输入电压	0 ~ 6000 V，最小单位0.1 V
电机控制器直流母流电流	−1000 ~ 1000 A，最小单位0.1 A
加速踏板行程值	0 ~ 100%
制动踏板状态	0 ~ 100%

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表 2 随机森林填补结果

Table 2. The results of filling with random forests

参数	总电压	电机控制器输入电压	电机控制器直流母流电流	驱动电机转速	驱动电机转矩
平均相对误差	3.0%	3.8%	2.5%	2.0%	3.7%

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表 3 驾驶行为指标权值

Table 3. Index weights of driving behavior

评价指标	权值
速度变化率方差	0.2487
急加速比	0.1067
急减速比	0.1173
踏板保持率	0.0630
急踩踏板比	0.0843
怠速比	0.0503
匀速比	0.2136
高速比	0.1161

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表 4 特征库

Table 4. Feature list

特征类别	说明	Index
电池状态信息	电压一致性得分电池容量电流最大值、方差电压方差	1~5
时间信息	季度、天数、星期、刻钟	6~9
环境信息	气温、风速均值	10~11
电机运行信息	电机转速均值电机转矩均值电机电压方差	12~14
行驶工况	最大速度值最大加速度值速度变化率及标准差加速度变化率及标准差加、减、怠速比，	15~23
驾驶行为信息	加、减速踏板比踏板保持比例减速踏板平均状态驾驶行为得分驾驶行为得分方差及L₂范数	24~30

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表 5 LSTM模型效果对比

Table 5. Comparison of LSTM model effects

分组	MAPE	RMSE
LSTM	2.8%	0.026
聚类 + 多LSTM	2.35%	0.021
聚类 + 注意力机制 + 多LSTM	2.1%	0.015
LGBM	3.6 %	1.01
XGBoost	4.3%	1.37
CatBoost	4.6%	1.37
SVR	4.9%	1.83

下载: 导出CSV

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