Abstract:

Improving driver behavior is an important way to reduce vehicle energy consumption. Currently, many scholars have conducted extensive research on eco-driving behavior and proposed various eco-driving suggestions. However, there is a lack of evaluation methods for the driving level of electric bus drivers. In order to reduce the operating costs and energy consumption of electric buses, this study collected natural driving data of them. Firstly, the original data was pre-processed with unified sampling frequency, data cleaning, and parameter supplementation. Secondly, by selecting driver operation characteristic parameters and vehicle operation parameters, the impact of bus driver driving behavior on energy consumption is analyzed. Based on the driving behavior characteristic parameters that affect energy consumption, seven types of driving events that affect energy consumption are proposed, including average starting acceleration time, number of times the accelerator pedal is forcefully pressed, duration of continuous high pedal opening, number of sudden accelerations, braking proportion during deceleration, duration of low-speed driving, and duration of economic speed driving, based on the distance between the starting and ending stations of the bus. Afterwards, a multiple regression model for eco-driving level evaluation was established by analyzing the Pearson correlation coefficients between various driving event parameters and energy consumption per 100 kilometers, and the driving level of the driver during the journey was scored. Finally, based on the evaluation model, an eco-driving assistance feedback platform is built to help fleet managers better understand the eco-driving level of drivers. The results show that the proposed eco-driving level evaluation model for electric buses based on driving events has an accuracy of 93.52% and an average error of 6.48% in evaluating the eco-driving behavior. The model has a good effect on calculating eco-driving scores. The eco-driving assistance feedback platform can help fleet managers understand the operation status of buses and the eco-driving level of drivers, and help drivers understand their own driving situation.

Key words: electric bus, eco-driving, driving behavior, assessment models, improvement recommendations