基于可解释极端随机树模型的DCT液压响应预测  

Interpretable Extremely Randomized Trees Model for Predicting DCT Hydraulic Response

作　　者：李想 王鑫 蔡辰 赵宗琴 冉若愚 杨德 皮家甜 LI Xiang;WANG Xin;CAI Chen;ZHAO Zongqin;RAN Ruoyu;YANG De;PI Jiatian(Chongqing Normal University,Chongqing 401331,China;Chongqing Chang'an Automobile Co.,Ltd.,Chongqing 401120,China)

机构地区：[1]重庆师范大学,重庆401331 [2]重庆长安汽车股份有限公司,重庆401120

出　　处：《汽车工程学报》2023年第6期889-898,共10页Chinese Journal of Automotive Engineering

基　　金：重庆市教委重点项目(KJZD-K202114802)。

摘　　要：为解决传统湿式双离合器变速器(Dual Clutch Transmission,DCT)控制策略在硬件误差以及复杂工况下液压响应预测精度不完全可控的问题,提出了一种基于SHAP图可解释极端随机树预测模型,使用机器学习方法结合某汽车公司DCT实验室采集的真实离合器数据对DCT液压响应进行预测。模型利用SHAP算法对于重要特征选择的可解释性,筛选并保留对液压响应影响较大的特征,将时间切片和升降压判定作为特征加入训练数据,训练预测模型。结果表明,该模型训练结果的均方误差MSE为0.6703,可决系数R2为1.0000,并且在测试集上预测值与实际值之间的平均误差为12.99 kPa,远低于设计误差25 kPa,具有较高的预测精度,特征选择较准确,可以很好地解决传统物理模型无法计算不同工况下液压响应的问题,为下阶段基于数据和物理双驱动的DCT控制策略优化提供较准确的预测结果。Traditional wet dual-clutch transmissions(DCT)control strategies face challenges in accurately predicting hydraulic responses,especially under hardware errors and complex working conditions.Therefore this paper proposes an Interpretable Extremely Randomized Trees prediction model based on the SHAP graph.By employing machine learning techniques and utilizing the actual clutch data collected from the DCT laboratory of a certain automobile company,it predicts the hydraulic response of DCT.This model uses the interpretability of the SHAP algorithm to select essential features that greatly impact hydraulic response,and adds time slices and buck-boost determinations as features into the training data to train the prediction model.The results show that the mean square error(MSE)of the model's training results is 0.6703,with a coefficient of determination(R2)of 1.0000.Furthermore,the average error between the predicted and actual values on the test set is 12.99 kPa,which is much lower than the design error of 25 kPa.This proves high prediction accuracy and precise feature selection of the model,which can effectively address the limitations of traditional physical models in calculating hydraulic responses under different working conditions.The proposed model provides more accurate prediction results for the next stage of DCT control strategy optimization based on a dual data and physical drive approach.

关 键 词：湿式双离合器变速器 液压响应预测 特征选择 可解释性 极端随机树 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] U463[自动化与计算机技术—控制科学与工程]