An Eco-Driving Evaluation Method for Battery Electric Bus Drivers Using Low-Frequency Big Data

Eco-driving can reduce vehicle energy consumption and carbon dioxide emissions. However, the effect of eco-driving training can diminish over time. It is necessary to provide the drivers with continuous feedback. Meanwhile, bus operators also need a fair incentive system to encourage drivers to drive ecologically to reduce energy costs. Considering the influence of traffic conditions, ambient temperature, and passenger load on the energy consumption of battery electric buses, a quantitative evaluation method for eco-driving with energy consumption as a single evaluation index is proposed. Specifically, the traffic conditions recognition method based on low-frequency data is constructed and then the division of ambient temperature range is discussed. According to the traffic conditions and ambient temperature, the actual operation data of 19 battery electric buses in one year are divided into 12 control groups and the reference energy consumption of each control group is obtained. The reference energy consumption describes the range of variation in energy consumption changes for different traffic conditions and ambient temperatures. In addition, to describe the impact of passenger load on bus energy consumption, a passenger load conversion factor is proposed. Finally, the eco-driving evaluation method is constructed using the reference energy consumption and the passenger load conversion factor. Since factors of the traffic conditions, ambient temperature, and passenger load are integrated into the eco-driving evaluation method design, the score depends only on the driver's eco-driving level and the results show that efficient drivers will not get lower scores due to driving in poor driving conditions.