Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China

Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision, especially in developing countries. Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. However, current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior, and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption. One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is “graph spectrums”, which allows for an effective and illustrative representation of complex driving behavior characteristics. This study presented an assessment method of ecological driving for electric vehicles based on the graph. Firstly, a multi-source refined data set was constructed through naturalistic driving experiments (NDE). Four typical traffic state (CCCF: congested close car-following; CSSF: constrained slow free-flow; CSCF: constrained slow car-following; UFFF: unconstrained fast free-flow) were classified through longitudinal acceleration data, and driving behavior graph was constructed to realize the visual representation of driving behavior. Then, the energy consumption graph was constructed using the energy loss of 100 ​km (EL) index. After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph, proposing the quantitative analysis of fifteen drivers' ecology driving behavior. The results show that: 1) The graphical method can describe the individual features of a driver’s ecological driving behavior; 2) Rapid acceleration of driving behavior leads to high energy consumption; 3) In the comparison among the six eco-drivers and energy-intensive drivers, founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state; 4) The driving behavior was more complex and unecological in CCCF traffic state; 5) Fifteen drivers had lower ecological scores in start-up driving. This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors, but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.