BEIJING, Jan. 18 (Xinhua) -- A group of Chinese researchers has proposed a novel data-driven framework for real-time online estimation and analysis of the remaining driving range of electric vehicles (EVs).
Range anxiety is still one of the major issues embarrassing the EV drivers despite more eco-friendly limos speeding on the roads. Accurate estimation of the remaining driving range can effectively address this problem.
However, in real-world operations, the coupling effects of factors such as driving behavior, ambient temperature, and battery aging pose significant challenges to accurately estimating the remaining driving range.
To help EV drivers be more confident of the endurance mileage of their cars, the researchers from the Dalian Institute of Chemical Physics (DICP), the Chinese Academy of Sciences, figured out a method for calculating energy consumption and state of health based on real vehicle operation data, according to a research article published in the journal Applied Energy.
By integrating multiple factors, including driving behavior, ambient temperature, and battery health, they built a per-mile energy-consumption model. Based on this model, they pinpointed the remaining range.
This staged approach can explain which factors contribute and by how much to the driving range.
The proposed framework was validated on passenger vehicles and buses in different domestic cities over a three-year period. Based on real operational data with total mileage exceeding 300,000 km, the validation results showed that the driving range prediction accuracy reached a mean relative error of less than 5.5 percent.
By adjusting driving behavior, the driving range can be improved by over 30 percent for passenger vehicles and over 10 percent for buses, according to the researchers.
This framework is poised to support smart fleet dispatch, energy-optimal operations, and residual-value appraisal of EVs. These researchers will extend their study to harsher cold regions and more complex road conditions.
To tackle capacity fade and energy volatility at low temperatures, they will expand the framework's generalizability by feeding it additional environmental parameters such as road surface and humidity.
In addition, they will drive deep integration of their study with on-board battery management systems and cloud-based operations platforms to build a safer and more efficient new-energy transportation system, according to the DICP. ■
