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.
Prediction of vehicle energy consumption on a planned route based on speed features forecasting
IET Intelligent Transport Systems ; 14 , 6 ; 511-522
2020-03-06
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
urban road , backpropagation , energy consumption , long-term vehicle speed feature prediction model , vehicle energy consumption , vehicle speed characteristics , energy consumption prediction model , planned route , energy management systems , long-term vehicle speed prediction , intelligent energy management system , road condition recognition , speed features , complex working conditions , support vector machines , predicting accuracy , speed sequence prediction , actual road–traffic conditions , vehicle speed sequence , road traffic , planned path , traffic engineering computing
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