Abstract
By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.
Highlights
With the rapid development of China’s urbanization and social economy, China’s motor vehicle fleet is growing rapidly and has become the world’s largest production and marketing of motor vehicles for eleven consecutive years
By installing on-board diagnostics (OBD) on tested vehicles [1], the after-treatment exhaust emissions can be monitored in real time to provide data support for the construction of dynamic emission inventories of mobile source emission
Due to security of data privacy and equipment installation costs, it is not possible to install monitoring equipment on all road-running vehicles for emission detection, while a series of problems such as human data tampering and equipment failure often leads to missing monitoring values, which greatly limits the application of OBD monitoring data in mobile source emission management. erefore, it is significant to improve the application efficiency of OBD monitoring data through reliable analysis of features affecting emission detection and accurate prediction of missing monitoring data for mobile source emission precise regulation
Summary
With the rapid development of China’s urbanization and social economy, China’s motor vehicle fleet is growing rapidly and has become the world’s largest production and marketing of motor vehicles for eleven consecutive years. E traditional driving cycle-based emission model uses artificially designed parameters such as vehicle speed and acceleration to characterize the relationship between vehicle driving cycle and pollution emissions, but it ignores the vehicle engine operating state information and inadequate representation of vehicle driving cycle characteristics, which makes it difficult to effectively estimate the exhaust emissions of monitoring missing vehicles under different driving conditions. Inspired by the insight of transfer learning, a novel NOx emission inversion prediction method for diesel vehicles is proposed in this paper It is a deep transfer learning (DTL)-based model which firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple influence factors (e.g., throttle state and engine-related states).
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