汽车工程 ›› 2021, Vol. 43 ›› Issue (1): 1-9.doi: 10.19562/j.chinasae.qcgc.2021.01.001
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收稿日期:
2020-03-29
修回日期:
2020-06-28
出版日期:
2021-01-25
发布日期:
2021-02-03
通讯作者:
胡杰
E-mail:auto_hj@163.com
基金资助:
Received:
2020-03-29
Revised:
2020-06-28
Online:
2021-01-25
Published:
2021-02-03
Contact:
Jie Hu
E-mail:auto_hj@163.com
摘要:
为准确预测电动汽车动力电池的能耗,缓解驾驶者的里程焦虑,本文中提出一种基于数据驱动的电动汽车动力电池SOC预测模型。首先分析电动汽车能耗构成并提取能耗影响因素,接着基于某款电动出租车CAN总线采集的汽车运行数据,采用机器学习算法,提出基于温度分层的能耗模型,通过宏观数据与微观数据的融合减小误差,最后使用该模型对车载BMS提供的SOC数据进行对比验证。结果表明,该模型预测效果较好,为帮助优化电动汽车能量控制策略、缓解里程焦虑提供科学的决策支持。
胡杰,高志文. 基于数据驱动的电动汽车动力电池SOC预测[J]. 汽车工程, 2021, 43(1): 1-9.
Jie Hu,Zhiwen Gao. A Data-driven SOC Prediction Scheme for Traction Battery in Electric Vehicles[J]. Automotive Engineering, 2021, 43(1): 1-9.
表3
能耗模型特征表"
类型 | 特征字符 | 数据类型 | 说明 |
---|---|---|---|
车辆状态 信息 | begin_soc | int | 起始SOC值 |
average_mileage | float | 片段平均累计里程 | |
slice_time | int | 片段运行时间 | |
slice_mileage | int | 片段运行里程 | |
环境信息 | slice_temp | float | 平均温度 |
行驶工况 信息 | jiasu_average_a | float | 加速段平均加速度 |
jiansu_average_a | float | 减速段平均减速度 | |
max_a | float | 最大加速度 | |
max_v | float | 最大速度 | |
std_v | float | 速度标准差 | |
slice_average_speed | float | 平均速度 | |
jingzhi_ratio | float | 静止段比例 | |
jiasu_ratio | float | 加速段比例 | |
jiansu_ratio | float | 减速段比例 | |
yunsu_ratio | float | 匀速段比例 | |
low_v_ratio | float | 低速比例 | |
mid_low_v_ratio | float | 中低速比例 | |
mid_v_ratio | float | 中速比例 | |
mid_high_v_ratio | float | 中高速比例 | |
high_v_ratio | float | 高速比例 |
表4
微观特征表"
类型 | 特征字符 | 说明 |
---|---|---|
车辆行驶 信息 | speed | 速度 |
mileage | 里程 | |
time_diff | 相邻数据时间差 | |
speed_diff | 相邻数据速度差 | |
mileage_diff | 相邻数据里程差 | |
滑动窗口 信息 | mean_speed | 滑动窗口平均速度 |
max_speed | 滑动窗口最大速度 | |
min_speed | 滑动窗口最小速度 | |
Sliding_window_mean_a | 滑动窗口平均加速度 | |
Sliding_window _max_a | 滑动窗口最大加速度 | |
Sliding_window _std_a | 滑动窗口加速度标准差 | |
Sliding_window _min_a | 滑动窗口最小加速度 | |
Sliding_window _std_speed | 滑动窗口速度标准差 |
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