Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

Abstract

Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.

A Quantitative Study on Driving Behavior Economy Based on Big Data from the Pure Electric Bus

“…With reference to the eco-driving concept [15,16], in ref. [17] seven indicators were selected and processed with a prediction model explainer to allocate eco-drive scores, resulting in an average vehicle economy improvement of 12.1%; Andrieu et al [18] constructed an aggregated indicator using the probability of being an eco-driver to obtain an eco-index and can be useful for driver monitoring; furthermore, an Elman neural network was applied to establish a driving behavior economy evaluation model based on four economy indicators in [19]; in ref. [20], eight indicators associated with the vehicle's speed, acceleration, driving time and so on were chosen to objectively assess the driver's driving style and could improve the driver's behavior behind the wheel.…”