Prediction of vehicle energy consumption on a planned route based on speed features forecasting. Issue 6 (6th March 2020)

• Record Type: Journal Article
• Authors: Yufang, Li; Jun, Zhang; Chen, Ren; Xiaoding, Lu
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 6(2020)
• Page Start: 511
• Page End: 522
• Publication Date: 2020-03-06
• Subjects: energy consumption -- road traffic -- energy management systems -- support vector machines -- traffic engineering computing -- backpropagation; long‐term vehicle speed feature prediction model -- energy consumption prediction model -- long‐term vehicle speed prediction -- vehicle energy consumption -- planned route -- speed features -- intelligent energy management system -- actual road–traffic conditions -- planned path -- road condition recognition -- speed sequence prediction -- predicting accuracy -- urban road -- complex working conditions -- vehicle speed characteristics -- vehicle speed sequence; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2019.0538
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : The prediction of energy consumption is the primary goal of an intelligent energy management system (IEMS). Based on the actual road–traffic conditions, the vehicle energy consumption on the whole planned path can be predicted online by road condition recognition or speed sequence prediction. Because the speed sequence prediction required by the latter cannot accurately reflect the real dynamic characteristics of vehicle speed such as acceleration and deceleration changes due to the random factors of traffic or human beings, which will greatly affect the predicting accuracy, especially on the urban road with complex working conditions. Therefore, based on the analysis of the cumulative relationship between vehicle speed characteristics and energy consumption, this study proposes a prediction method of vehicle driving energy consumption based on the statistical characteristics of vehicle speed, regardless of the accuracy of the prediction of vehicle speed sequence, including the establishment of a long‐term vehicle speed feature prediction model and energy consumption prediction model by BP and SVM algorithms. Finally, its rationality is validated based on the authentic data with an accuracy of about 95%, significantly improved compared with that based on long‐term vehicle speed prediction.