Data-driven Energy Consumption Prediction of New Energy Buses
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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。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.
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Key words:
- urban traffic /
- energy consumption forecast /
- data driven /
- driving behavior /
- attention mechanism
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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% 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% 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 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 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 -
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