Enhancing energy management through driving style recognition in vehicular communication systems

Abstract

Driving style, a critical indicator that affects vehicle fuel economy, its recognition has traditionally relied on onboard sensors and limited vehicle-to-vehicle communication. However, 6G technology facilitates the collection of vast amounts of data, including vital signs like vehicle speed, power sources performance, and real-time traffic conditions, which can be used for real-time driving style recognition. It enables vehicles to make informed decisions about power allocation and fuel consumption, thereby advancing the future of green and efficient transportation. For driving style recognition problem, the principal component analysis (PCA) method is adopted to select the speed and the absolute values of acceleration as driving style identification parameters and the fuzzy-logic controller optimized by genetic algorithm (GA) is designed to identify driving style. Afterwards, the driving style optimal control strategy is realized by matching the recognized driving style with the optimal equivalent factor in each driving condition and the matched equivalent factor is combined with the objective function of ECMS. The effectiveness of proposed driving style based on ECMS is validated by real vehicle test, which indicates that, compared with the strategy without considering driving styles, the proposed driving style recognition based ECMS reduces the hydrogen consumption of FCHEV by 3.7% in the combination of HWFET and UDDS.

DOI: 10.61416/ceai.v26i2.9013