Research on eco-driving optimization of hybrid electric vehicle queue considering the driving style

With the vehicle to infrastructure and vehicle to vehicle communication information, it is beneficial to improve the fuel economy of hybrid electric vehicle by providing the driver with economy-oriented velocity. However, the drivers with different driving styles differ much from each other in tracking the advisory economy-oriented velocity. Hence, this paper investigates the eco-driving optimization of the hybrid electric vehicle queue in urban road conditions considering the driving characteristics in following the advisory speed. The multi-objective optimization problem for the velocities of the hybrid electric vehicle queue is constructed, in which the initial target velocities of different vehicles are obtained according to the signal phase and timing information of traffic lights. In order to improve the adaptability of the driver to the advisory velocity, the driving style coefficients are introduced in the multi-objective eco-driving optimization problem. The driving feature parameters of different drivers are collected through driving simulator test. By means of principle component analysis and K-means clustering method, the collected driving feature parameters are applied for driving style classification. Then, the coefficients of different driving styles are derived through car following tests. The simulated annealing algorithm is used to solve the nonlinear constrained problem and obtain the optimal advisory velocities. In the powertrain level, the model predictive control which can make full use of the predicted optimal velocity is further applied to optimize the energy allocations. Comparative simulation studies are conducted to test the performance of the proposed strategy. Simulation results demonstrate the superior performance of the proposed strategy in reducing the fuel consumption and exhaust emissions of the hybrid electric vehicle queue, as well as in improving the traffic smoothness. The driver adaptability in tracking the advisory eco-driving velocity has also been improved.