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电动汽车动力电池充电能量的预测方法

胡杰 蔡世杰 黄腾飞 王成 杜常清

胡杰,蔡世杰,黄腾飞, 等. 电动汽车动力电池充电能量的预测方法[J]. 机械科学与技术,2020,39(6):926-936 doi: 10.13433/j.cnki.1003-8728.20190226
引用本文: 胡杰,蔡世杰,黄腾飞, 等. 电动汽车动力电池充电能量的预测方法[J]. 机械科学与技术,2020,39(6):926-936 doi: 10.13433/j.cnki.1003-8728.20190226
Hu Jie, Cai Shijie, Huang Tengfei, Wang Cheng, Du Changqing. Prediction Method of Charging Energy for Power Battery of Electric Vehicle[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(6): 926-936. doi: 10.13433/j.cnki.1003-8728.20190226
Citation: Hu Jie, Cai Shijie, Huang Tengfei, Wang Cheng, Du Changqing. Prediction Method of Charging Energy for Power Battery of Electric Vehicle[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(6): 926-936. doi: 10.13433/j.cnki.1003-8728.20190226

电动汽车动力电池充电能量的预测方法

doi: 10.13433/j.cnki.1003-8728.20190226
基金项目: 国家自然科学基金项目(51775393)与柳州市科技计划项目(2018B0301b003)资助
详细信息
    作者简介:

    胡杰(1984−),副教授,博士生导师,博士,研究方向为汽车控制与诊断,车联网,auto_hj@163.com

    蔡世杰:

  • 中图分类号: U469.72;TP181

Prediction Method of Charging Energy for Power Battery of Electric Vehicle

  • 摘要: 提出了一种基于机器学习中Stacking模型的电池充电能量预测方法,该方法通过对充电数据进行数据探索、特征工程和模型筛选,选取RMSE作为预测结果的评价指标,最后采用Stacking模型对充电能量作出预测。为了验证Stacking模型的预测结果,将Stacking模型与采用单个算法模型的预测结果进行对比,以确保方案的可行性。其结果表明,采用该模型进行预测时,其预测结果的RMSE分值为0.104 1,实现了比单个算法模型更好的预测效果。
  • 图  1  充电能量预测的技术构架

    图  2  Charge_energe和Charge_soc之间的关系图

    图  3  动力电池充电结束时SOC分布

    图  4  修正后Charge_energe和Charge_soc之间的关系图

    图  5  修正后动力电池充电结束时SOC分布

    图  6  4种聚类模型的标记结果

    图  7  贝叶斯高斯混合算法对所有车辆离群值的标记结果

    图  8  动力电池的充电能量分布

    图  9  对数似然函数$f({{X}},{{Y}},{{\lambda }})$与参数${\rm{\lambda }}$之间的关系曲线

    图  10  BOX-COX变换后动力电池的充电能量分布

    图  11  所有车辆的电池容量分布

    图  12  所有车辆充电时间和充电能量的分布

    图  13  多种聚类算法对不同充电模式的标记结果

    图  14  各特征的频率排序

    图  15  各算法模型的RMSE得分

    图  16  采用堆叠模型的学习曲线

    图  17  预测结果的相对误差

    图  18  预测结果的绝对误差

    表  1  数据分类与说明

    类型特征字符数据类型说明
    时间信息类 Charge_start_time int 充电开始时间
    Charge_end_time int 充电结束时间
    车辆信息类 Vehicle_id string 车辆唯一标志码
    Mileage float 充电开始时刻车辆仪表里程/km
    电池信息类 Charge_start_soc float 充电开始时刻动力电池soc/%
    Charge_end_soc float 充电结束时刻动力电池soc/%
    Charge_start_U float 充电开始时刻动力电池总电压/V
    Charge_end_U float 充电结束时刻动力电池总电压/V
    Charge_start_I float 充电开始时刻动力电池总电流/A
    Charge_end_I float 充电结束时刻动力电池总电流/A
    Charge_max_temp float 充电过程中电池系统最大温度/℃
    Charge_min_temp float 充电过程中电池系统最小温度/℃
    Charge_energy float 此充电过程的充电能量/ (kW·h)
    下载: 导出CSV

    表  2  动力电池充电能量的基本特征

    类型特征字符数据类型说明
    时间信息类 Charge_time int 充电时间
    Day_of_week int 充电时间日标记
    Weekend int 充电时间周末标记
    Charge_start_hour int 充电开始时标记
    Hour_of_day int 充电时间日夜标记
    电池信息类 Soc_time float 充电过程中电池SOC的增长率
    Charge_diff_U float 充电起止电压差值/V
    Charge_diff_I float 充电起止电流差值/A
    Charge_diff_temp float 充电起止温度差/℃
    Charge_aver_U float 充电过程动力电池平均总电压/V
    Charge_aver_I float 充电过程动力电池平均总电流/A
    Charge_start_power float 充电开始功率/W
    Charge_end_power float 充电结束功率/W
    Charge_aver_power float 充充电过程中平均功率/W
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-04-20
  • 刊出日期:  2020-06-05

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