New fuel consumption model considering vehicular speed, acceleration, and jerk

Abstract

A novel computational model for the volatile state was developed to improve eco-driving in intelligent transportation systems (ITS). First, the volatile state was divided into eight types using vehicle acceleration and jerk as delineating criteria. Data analysis showed that each jerk type had a different proportion and contribution level to fuel consumption. Next, the model was created by considering eight instantaneous driving decisions as represented by vehicle speed, acceleration, and jerk. The model input included vehicle speed multiplied by acceleration, with jerk as a classifier. The model was calibrated using quadratic polynomial fitting, and validated using another portion of the data. Finally, predictions were compared with the widely used Vehicle Specific Power (VSP) model and the Virginia Tech Microscopic (VT-Micro) model to evaluate model performance. The new model thoroughly captured the measured fuel consumption and provided more accurate predictions in new routes than the above-mentioned models. The mean absolute percentage error value of the new model was ∼4.9% and 3.2% lower than those of the VSP and VT-Micro models, respectively. The determinant coefficient value was up to 95.8%, which was ∼4.6% and 8.5% higher than those of the VSP and VT-Micro models, respectively.

Keywords:

• Eco-driving
• driving behaviors
• fuel consumption model
• vehicular acceleration
• vehicular jerk

Authors' contributions

The authors confirm contribution to the paper as follows: study conception and design: AJK, LCZ, XMZ; data collection: LCZ; analysis and interpretation of results: LCZ, AJK, XMZ; draft manuscript preparation: LCZ, KP. All authors reviewed the results and approved the final version of the manuscript.

Acknowledgments

The authors also appreciate anonymous reviewers and the editor for their thorough and most helpful comments and valuable suggestions, and for the journal’s rigorous review process that helped improve our manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research is supported by the National Natural Science Foundation of China (no. 51278058), the 111 project (no. B14043), the Joint Laboratory of Internet of Vehicles sponsored by Ministry of Education and China Mobile (no. 2012-364-812-105), the Natural Science Basic Research Program of Shaanxi Province, China (no. 2018JQ6091), and the Fundamental Research Funds for the Central Universities, CHD (no. 300102240503). The authors are grateful to Ms Kinzee Clark and Meredith King for English language editing.