Machine Learning Predicts Emissions of Brake Wear PM<sub>2.5</sub>: Model Construction and Interpretation
Ning Wei, Zhenyu Jia, Zhengyu Men, Chunzhe Ren, Yanjie Zhang, Jianfei Peng, Lin Wu, Ting Wang, Qijun Zhang, Hongjun Mao
Abstract
Brake emissions are generated every time a brake is applied to a vehicle. However, revealing the pattern of brake emissions under different operating conditions is conventionally considered highly challenging. Here, we compiled a brake wear PM2.5 data set collected from brake dynamometer simulation experiments and obtained the mapping relationship between brake emissions and influencing factors through a machine learning (ML) method. The random forest model was devised and displayed good prediction performance with an R2 of 0.89 on the test set. Model-related (similarity network analysis) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) interpretation methods were used to break the black box of ML to obtain the marginal contribution of the model input feature parameters (brake energy dissipation, average temperature during braking, brake pad metal content, and brake pad surface area) to the model output results. This study suggests that avoiding rapid braking behavior and using brake pads with a lower metal content are feasible ways to reduce brake wear PM2.5 emissions. The development of a ML-based brake emission model provides novel insights into the accurate assessment and control of brake emissions.