汽车工程 ›› 2022, Vol. 44 ›› Issue (10): 1527-1536.doi: 10.19562/j.chinasae.qcgc.2022.10.007
所属专题: 底盘&动力学&整车性能专题2022年
收稿日期:
2022-04-17
修回日期:
2022-05-13
出版日期:
2022-10-25
发布日期:
2022-10-21
通讯作者:
陈国迎
E-mail:cgy-011@163.com
基金资助:
Zhenhai Gao1,Wenhao Wen1,Minghong Tang1,Jian Zhang2,Guoying Chen1()
Received:
2022-04-17
Revised:
2022-05-13
Online:
2022-10-25
Published:
2022-10-21
Contact:
Guoying Chen
E-mail:cgy-011@163.com
摘要:
针对现有车辆运动状态估计算法严重依赖动力学模型精度且在大的质心侧偏角工况下准确性难以保障的问题,本文提出了一种基于混合神经网络的车辆运动状态估计算法。通过分析车辆本身的动力学基本特性,设计了适合于车辆运动状态估计的HNN混合神经网络架构,实现了车辆运动状态的深度学习估计。基于多个标准工况组成的数据集与典型实车测试工况进行了网络训练与测试验证。结果表明,相比于传统算法,本算法基于神经网络实现了精准的无动力学模型的汽车运动状态估计,提高了估计精度,且对路面附着系数变化具有鲁棒性。
高振海,温文昊,唐明弘,张建,陈国迎. 基于混合神经网络的汽车运动状态估计[J]. 汽车工程, 2022, 44(10): 1527-1536.
Zhenhai Gao,Wenhao Wen,Minghong Tang,Jian Zhang,Guoying Chen. Estimation of Vehicle Motion State Based on Hybrid Neural Network[J]. Automotive Engineering, 2022, 44(10): 1527-1536.
表9
不同车速下各算法估计均方误差RMSE"
估计量 | 车速/(km·h-1) | 对比算法 | |||
---|---|---|---|---|---|
EKF | DNN | GRU | HNN | ||
30 | 0.240 6 | 0.087 0 | 1.104 1 | 0.019 8 | |
70 | 0.230 3 | 0.341 4 | 0.749 7 | 0.021 2 | |
120 | 2.052 8 | 0.627 0 | 2.024 6 | 0.044 9 | |
30 | 0.196 9 | 0.352 9 | 0.741 2 | 0.110 0 | |
70 | 0.940 2 | 0.299 8 | 0.500 2 | 0.279 1 | |
120 | 3.192 5 | 1.226 6 | 2.709 2 | 1.179 6 | |
30 | 1.891 7 | 0.810 8 | 2.108 6 | 0.565 8 | |
70 | 0.723 5 | 0.520 2 | 2.044 3 | 0.381 5 | |
120 | 2.917 4 | 1.709 6 | 2.434 5 | 1.504 1 |
表10
不同路面下各算法估计均方误差RMSE"
估计量 | 路面 | 对比算法 | |||
---|---|---|---|---|---|
EKF | DNN | GRU | HNN | ||
0.3 | 1.060 7 | 0.504 8 | 1.437 8 | 0.030 6 | |
0.5 | 0.925 5 | 0.502 4 | 1.508 3 | 0.029 5 | |
0.85 | 1.320 7 | 0.505 5 | 1.562 1 | 0.037 4 | |
1.0 | 2.192 7 | 0.506 7 | 1.593 7 | 0.041 5 | |
0.3 | 1.518 6 | 1.127 0 | 1.755 1 | 1.064 0 | |
0.5 | 1.675 7 | 0.970 7 | 1.882 9 | 0.922 6 | |
0.85 | 2.673 2 | 0.734 0 | 2.050 8 | 0.720 4 | |
1.0 | 3.145 3 | 0.661 8 | 2.085 4 | 0.660 5 | |
0.3 | 2.208 5 | 1.407 3 | 2.328 9 | 1.133 4 | |
0.5 | 2.206 7 | 1.193 3 | 2.192 5 | 1.037 8 | |
0.85 | 2.056 1 | 1.232 0 | 2.227 2 | 1.066 7 | |
1.0 | 2.023 8 | 1.317 4 | 2.240 7 | 1.147 2 |
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