Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle

Driving condition is the core factor influencing vehicle energy management and power output strategies, hence realistic and accurate driving pattern recognition is crucial for realizing intelligent control of Electric Vehicles in complex scenarios. However, driving patterns mismatch with real scenarios caused by lack of road environment information and the reduced global recognition accuracy triggered by the fixed recognition horizon together constrain the development of intelligent control strategies. In this study, a framework for variable horizon multivariate driving pattern recognition strategy based on vehicle-road two-dimensional (2D) information is proposed. Specifically, multivariate driving pattern extraction is carried out based on real vehicle-road 2D data, and the extraction data serves as input for training offline driving pattern recognizer. To meet the challenge of large differences and frequent changes in driving conditions during the online application, a variable horizon recognition strategy is proposed based on the optimal horizon matching regularity. Finally, test results show that the proposed approach achieves an average recognition accuracy of 88.42% under different cycles, including 88.39% for complex driving cycles. Furthermore, the recognition accuracy is improved by 63.14% and 47.51% compared with the fixed long/short horizon. The framework can provide a basis for intelligent control strategies, thus broadening the Electric Vehicle application scenarios.