Electric Vehicle Range Estimation Using Regression Techniques

World Electric Vehicle Journal

Zhiyu Mao¹  Moin Ahmed²  Yun Zheng²  Zhongwei Chen²  Tao Chen³ 

[1] College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua 321004, China;Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada;Department of Economics, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada;

关键词: electric vehicles;    linear regression;    lithium-ion batteries;   

DOI  :  10.3390/wevj13060105

来源: DOAJ

【 摘 要 】

Electric vehicles (EVs) are an attractive alternative to conventional vehicles powered by internal combustion engines due to their low carbon footprint, low running cost, and higher energy efficiency. However, currently, they suffer from a lower range than conventional vehicles, which induces range anxiety for consumers. This work explores the EV parameters that strongly impact range using data-driven techniques. A detailed dataset of the technical specifications of commercial EV models manufactured from 2008 to 2021 was collected through web mining. Strong correlations were observed between range and battery capacity, top speed, curb weight, and acceleration (with Pearson coefficients of 0.90, 0.79, 0.70, and −0.84, respectively). Furthermore, regression algorithms were trained and tested on this dataset, with the lowest root-mean-squared error (RMSE) of 31.4 km obtained from support vector machine regression. With a mean EV range in the test set of 364.5 km, an RMSE of 31.4 km equates to around 8.6% accuracy. Additionally, simple linear relationships between EV range and EV model, battery, and performance parameters were determined that may be useful to EV consumers in calculating range.

【 授权许可】

Unknown