基于PCA-SSA-XGBoost的车辆驾驶性评估

doi:10.13229/j.cnki.jdxbgxb.20230215

摘要/Abstract

摘要：

为提高车辆驾驶性评估的效率与质量，提出了一种基于主成分分析、极限梯度提升树和麻雀优化算法的驾驶性评估模型。以双离合变速箱（Dual clutch transmission，DCT）车辆动力升挡为典型工况，研究并定义了动力升挡工况下的18项客观评价指标，利用主成分分析法对客观评价指标进行约简，降低其冗余性与耦合性，优化了模型输入样本，训练极限梯度提升树模型对驾驶性主观评分进行预测，并采用麻雀算法优化极限梯度提升树的核心超参数，提高模型精度与稳定性。道路试验表明：经主成分分析约简客观评价指标后，模型评估准确率达97%，优于BPNN（90%）、SVM（91%）、ELM（92%）与SSA-XGBoost（95%）。证明了本文PCA-SSA-XGBoost模型的准确性与稳定性优于其他模型，能更有效地完成驾驶性评估。该评估模型可迁移应用于其他驾驶工况，对于解决驾驶性评估中的主客观映射问题具有应用价值。

关键词: 驾驶性, 主成分分析, 麻雀算法, 极限梯度提升树

Abstract:

To improve the efficiency and quality of vehicle driveability evaluation， a driving evaluation model based on principal component analysis， limit gradient lifting tree and Sparrow optimization algorithm was proposed.The dynamic upshift of Dual clutch transmission（DCT） vehicle is taken as a typical working condition， and 18 objective evaluation indexes are studied and defined. Principal component analysis was used to reduce the objective evaluation index， reduce its redundancy and coupling. The Extreme Gradient Boosting algorithm model was trained to predict the subjective driving score， and the Sparrow algorithm was used to improve the accuracy and stability of the model. The road test shows that the accuracy of the model is 97% after the objective evaluation index is reduced by principal component analysis. It is better than BPNN（90%）， SVM（91%）， ELM（92%） and SSA-XGBoost （95%）. It is proved that the accuracy and stability of the proposed PCA-SSA-XGBoost model are better than other models， and can complete the driving evaluation more effectively.The evaluation model can be applied to other driving conditions and has application value to solve the problem of subjective and objective mapping in driving evaluation.

Key words: drivability, principal component analysis, sparrow algorithm, extreme gradient boosting algorithm

中图分类号:

U461

吴飞,王鹏程,杨康. 基于PCA-SSA-XGBoost的车辆驾驶性评估[J]. 吉林大学学报(工学版), 2025, 55(1): 105-115.

Fei WU,Peng-cheng WANG,Kang YANG. Drivability evaluation model based on PCA-SSA-XGBoost[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 105-115.

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信号名称	单位	信号名称	单位
发动机转速（E）	r/min	发动机扭矩（ET）	N·m
输入轴转速（EI）	r/min	车速（v）	km/h
输出轴转速（EO）	r/min	加速度（a）	m/s²
离合器1扭矩（CT1）	N·m	油门开度（TO）	%
离合器2扭矩（CT2）	N·m	踏板角度（PO）	-
离合器1状态（CS1）	-	目标挡位（TG）	-
离合器2状态（CS2）	-	当前挡位（CG）	-

指标	单位	定义
D_d	s	升挡响应时间
T_d	s	升挡持续时间
R_d	s	传动比变化时间
GS_d	s	扭矩传递时间
a_mean	m/s²	纵向加速度算术平均值
a_peak	m/s²	纵向加速度峰值
Δa_hole	m/s²	纵向加速度最大扰动值
Δa_peak-peak	m/s²	纵向加速度峰峰值
a_grad	-	纵向加速度最大梯度
j_peak	m/s³	冲击度峰值
j_g_rad	-	冲击度最大梯度
VDV_shift	m/s^1.75	纵向冲击剂量值
ΔE_peak	r/min	发动机转速超调
E_grad	-	发动机转速梯度
V_δ	-	车速变化线性度
a_loss	m/s²	平均加速度损失
ΔCT_s	N·m	结合初始扭矩振荡
ΔCT_d	N·m	结合结束扭矩振荡

超参数	寻优范围
max_depth	［1，15］
min_child_weight	［1，10］
learning_rate	［0.01，0.2］

成分	初始特征值			主成分提取
成分	特征根	方差百分比/%	累积/%	特征根	方差百分比/%	累积/%
F1	6.489	36.051	36.051	6.489	36.051	36.051
F2	2.699	14.994	51.045	2.699	14.994	51.045
F3	1.804	10.020	61.066	1.804	10.02	61.066
F4	1.578	8.766	69.831	1.578	8.766	69.831
F5	1.055	5.859	75.690	1.055	5.859	75.69
F6	0.937	5.207	80.897	0.937	5.207	80.897
F7	0.762	4.233	85.130	0.762	4.233	85.13
F8	0.613	3.408	88.537	0.613	3.408	88.537
F9	0.565	3.138	91.675	0.565	3.138	91.675
F10	0.428	2.376	94.052	-	-	-
F11	0.379	2.103	96.155	-	-	-
F12	0.321	1.781	97.936	-	-	-
F13	0.194	1.077	99.013	-	-	-
F14	0.091	0.508	99.522	-	-	-
F15	0.063	0.352	99.874	-	-	-
F16	0.018	0.101	99.975	-	-	-
F17	0.003	0.015	99.990	-	-	-
F18	0.002	0.010	100.000	-	-	-

特征指标	载荷系数
特征指标	F1	F2	F3	F4	F5	F6	F7	F8	F9
D_d	-0.206	0.829	0.192	-0.142	0.013	0.340	0.073	0.160	-0.058
T_d	-0.398	0.813	0.275	0.224	0.051	0.102	0.035	0.132	-0.032
R_d	-0.443	0.324	0.218	0.629	0.072	-0.312	-0.025	0.008	0.003
GS_d	0.108	0.232	-0.377	-0.369	0.638	0.292	-0.186	-0.201	-0.177
a_mean	0.871	0.367	-0.077	0.048	0.094	-0.052	0.067	-0.139	0.071
a_peak	0.962	0.102	-0.058	0.055	0.031	-0.115	0.047	-0.102	0.064
Δa_hole	0.385	0.108	0.600	0.082	-0.248	0.362	0.197	-0.369	0.093
Δa_peak-peak	0.894	-0.032	0.280	0.015	-0.018	-0.117	-0.052	-0.003	-0.110
a_grad	0.223	-0.313	0.487	0.195	0.296	0.167	-0.557	0.151	0.339
j_peak	0.514	-0.350	0.603	-0.056	0.074	0.129	-0.021	-0.072	-0.307
j_grad	0.037	-0.340	0.374	-0.135	0.573	-0.13	0.547	0.250	0.095
VDV_shift	0.940	0.194	-0.098	0.059	0.044	-0.108	0.065	-0.108	0.101
ΔE_peak	0.802	0.326	-0.270	0.101	0.095	-0.069	0.049	-0.026	0.270
E_grad	0.620	-0.326	0.003	-0.467	-0.325	0.042	-0.014	0.240	0.026
V_δ	0.640	0.494	-0.002	-0.274	-0.135	0.005	-0.064	0.355	0.066
a_loss	-0.680	-0.163	-0.098	-0.188	-0.061	0.324	0.170	-0.108	0.416
ΔCT_s	0.348	-0.373	-0.387	0.584	0.066	0.236	0.108	0.065	-0.018
ΔCT_d	0.479	-0.197	-0.268	0.471	-0.068	0.478	0.099	0.248	-0.140